arXiv subjects

Yang Cao

Publications and source records attributed to Yang Cao.

At least 307 records · Page 17Linked to original sources

arXiv2021

Local large temperature difference and ultra-wideband photothermoelectric response of the silver nanostructure film/carbon nanotube film heterostructure

Photothermoelectric materials have important applications in many fields. Here, we joined a silver nanostructure film (AgNSF) and a carbon nanotube film (CNTF) by van der Waals force to form a AgNSF/CNTF heterojunction, which shows excellent photothermal and photoelectric conversion properties. The local temperature difference and the output photovoltage increase rapidly when the heterojunction is irradiated by lasers with wavelengths ranging from ultraviolet to terahertz. The maximum of the local temperature difference reaches 205.9 K, which is significantly higher than that of other photothermoelectric materials reported in literatures. The photothermal and photoelectric responsivity depend on the wavelength of lasers, which are 175-601 K/W and 9.35-40.4 mV/W, respectively. We demonstrate that light absorption of the carbon nanotube is enhanced by local surface plasmons, and the output photovoltage is dominated by Seebeck effect. The AgNSF/CNTF heterostructure can be used as high-efficiency sensitive photothermal materials or as ultra-wideband fast-response photoelectric material.

physics.app-ph↗

arXiv2021

Learning Visual Affordance Grounding from Demonstration Videos

Visual affordance grounding aims to segment all possible interaction regions between people and objects from an image/video, which is beneficial for many applications, such as robot grasping and action recognition. However, existing methods mainly rely on the appearance feature of the objects to segment each region of the image, which face the following two problems: (i) there are multiple possible regions in an object that people interact with; and (ii) there are multiple possible human interactions in the same object region. To address these problems, we propose a Hand-aided Affordance Grounding Network (HAGNet) that leverages the aided clues provided by the position and action of the hand in demonstration videos to eliminate the multiple possibilities and better locate the interaction regions in the object. Specifically, HAG-Net has a dual-branch structure to process the demonstration video and object image. For the video branch, we introduce hand-aided attention to enhance the region around the hand in each video frame and then use the LSTM network to aggregate the action features. For the object branch, we introduce a semantic enhancement module (SEM) to make the network focus on different parts of the object according to the action classes and utilize a distillation loss to align the output features of the object branch with that of the video branch and transfer the knowledge in the video branch to the object branch. Quantitative and qualitative evaluations on two challenging datasets show that our method has achieved stateof-the-art results for affordance grounding. The source code will be made available to the public.

cs.CV↗

arXiv2021

One-Shot Object Affordance Detection in the Wild

Affordance detection refers to identifying the potential action possibilities of objects in an image, which is a crucial ability for robot perception and manipulation. To empower robots with this ability in unseen scenarios, we first study the challenging one-shot affordance detection problem in this paper, i.e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected. To this end, we devise a One-Shot Affordance Detection Network (OSAD-Net) that firstly estimates the human action purpose and then transfers it to help detect the common affordance from all candidate images. Through collaboration learning, OSAD-Net can capture the common characteristics between objects having the same underlying affordance and learn a good adaptation capability for perceiving unseen affordances. Besides, we build a large-scale Purpose-driven Affordance Dataset v2 (PADv2) by collecting and labeling 30k images from 39 affordance and 103 object categories. With complex scenes and rich annotations, our PADv2 dataset can be used as a test bed to benchmark affordance detection methods and may also facilitate downstream vision tasks, such as scene understanding, action recognition, and robot manipulation. Specifically, we conducted comprehensive experiments on PADv2 dataset by including 11 advanced models from several related research fields. Experimental results demonstrate the superiority of our model over previous representative ones in terms of both objective metrics and visual quality. The benchmark suite is available at https://github.com/lhc1224/OSAD Net.

cs.CV↗

arXiv2021

Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning

Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes. It is a challenging task since representation optimization and prototype reorganization can only be achieved under little supervision. To address this problem, we propose a novel incremental prototype learning scheme. Our scheme consists of a random episode selection strategy that adapts the feature representation to various generated incremental episodes to enhance the corresponding extensibility, and a self-promoted prototype refinement mechanism which strengthens the expression ability of the new classes by explicitly considering the dependencies among different classes. Particularly, a dynamic relation projection module is proposed to calculate the relation matrix in a shared embedding space and leverage it as the factor for bootstrapping the update of prototypes. Extensive experiments on three benchmark datasets demonstrate the above-par incremental performance, outperforming state-of-the-art methods by a margin of 13%, 17% and 11%, respectively.

cs.CV↗

arXiv2021

Add drop multiplexers for terahertz communications using two-wire waveguide based plasmonic circuits

Terahertz (THz) band is considered as the next frontier in wireless communications. The emerging THz multiplexing techniques are expected to dramatically increase the information capacity of THz communications far beyond a single channel limit. In this work, we explore the THz frequency-division multiplexing modality enabled by novel add-drop multiplexer (ADM) design. Based on modular two-wire plasmonic waveguides fabricated using additive manufacturing and metallization techniques, we demonstrate four-port THz ADMs containing grating-loaded side couplers for operation at ~140 GHz carrier frequency. Particular attention is payed to the design of plasmonic waveguide Bragg gratings and directional couplers capable of splitting broadband THz light in spectral and spatial domains, respectively. Finally, we demonstrate multiplexing and demultiplexing of THz signals with bit rates up to 6 Gbps using the developed ADMs. We believe that proposed plasmonic circuits hold strong potential to provide robust integrated solutions for analogue signal processing in the upcoming THz communications.

physics.app-ph↗

arXiv2021

One-Shot Affordance Detection

Affordance detection refers to identifying the potential action possibilities of objects in an image, which is an important ability for robot perception and manipulation. To empower robots with this ability in unseen scenarios, we consider the challenging one-shot affordance detection problem in this paper, i.e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected. To this end, we devise a One-Shot Affordance Detection (OS-AD) network that firstly estimates the purpose and then transfers it to help detect the common affordance from all candidate images. Through collaboration learning, OS-AD can capture the common characteristics between objects having the same underlying affordance and learn a good adaptation capability for perceiving unseen affordances. Besides, we build a Purpose-driven Affordance Dataset (PAD) by collecting and labeling 4k images from 31 affordance and 72 object categories. Experimental results demonstrate the superiority of our model over previous representative ones in terms of both objective metrics and visual quality. The benchmark suite is at ProjectPage.

cs.CV↗

arXiv2021

Understanding the Interplay between Privacy and Robustness in Federated Learning

Federated Learning (FL) is emerging as a promising paradigm of privacy-preserving machine learning, which trains an algorithm across multiple clients without exchanging their data samples. Recent works highlighted several privacy and robustness weaknesses in FL and addressed these concerns using local differential privacy (LDP) and some well-studied methods used in conventional ML, separately. However, it is still not clear how LDP affects adversarial robustness in FL. To fill this gap, this work attempts to develop a comprehensive understanding of the effects of LDP on adversarial robustness in FL. Clarifying the interplay is significant since this is the first step towards a principled design of private and robust FL systems. We certify that local differential privacy has both positive and negative effects on adversarial robustness using theoretical analysis and empirical verification.

cs.CR↗

arXiv2021

Preventing Manipulation Attack in Local Differential Privacy using Verifiable Randomization Mechanism

Several randomization mechanisms for local differential privacy (LDP) (e.g., randomized response) are well-studied to improve the utility. However, recent studies show that LDP is generally vulnerable to malicious data providers in nature. Because a data collector has to estimate background data distribution only from already randomized data, malicious data providers can manipulate their output before sending, i.e., randomization would provide them plausible deniability. Attackers can skew the estimations effectively since they are calculated by normalizing with randomization probability defined in the LDP protocol, and can even control the estimations. In this paper, we show how we prevent malicious attackers from compromising LDP protocol. Our approach is to utilize a verifiable randomization mechanism. The data collector can verify the completeness of executing an agreed randomization mechanism for every data provider. Our proposed method completely protects the LDP protocol from output-manipulations, and significantly mitigates the expected damage from attacks. We do not assume any specific attacks, and it works effectively against general output-manipulation, and thus is more powerful than previously proposed countermeasures. We describe the secure version of three state-of-the-art LDP protocols and empirically show they cause acceptable overheads according to several parameters.

cs.CR↗

arXiv2021

Microstructured fiber links for THz communications and their fabrication using infinity printing

In this work, a novel infinity 3D printing technique is explored to fabricate continuous multi-meter-long low-loss near-zero dispersion suspended-core polypropylene fibers for application in terahertz (THz) communications. Particular attention is paid to process parameter optimization for 3D printing with low-loss polypropylene plastic. Three microstructured THz fibers were 3D printed using the standard and infinity 3D printers, and an in-depth theoretical and experimental comparison between the fibers were carried out. Transmission losses (by power) of 4.79 dB/m, 17.34 dB/m and 11.13 dB/m are experimentally demonstrated for the three fibers operating at 128 GHz. Signal transmission with BER far below the forward error correction limit (10-3) for the corresponding three fiber types of lengths of 2 m, 0.75 m and 1.6 m are observed, and an error-free transmission is realized at the bit rates up to 5.2 Gbps. THz imaging of the fiber near-field is used to visualize modal distributions and study optimal fiber excitation conditions. The ability of shielding the fundamental mode from the environment, mechanical robustness and ease of handling of thus developed effectively single-mode high optical performance fibers make them excellent candidates for upcoming fiber-assisted THz communications. Additionally, novel fused deposition modeling (FDM)-based infinity printing technique allows continuous fabrication of unlimited in length fibers of complex transverse geometries using advanced thermoplastic composites, which, in our opinion, is poised to become a key fabrication technique for advanced terahertz fiber manufacturing.

physics.optics↗

arXiv2021

Transparent Contribution Evaluation for Secure Federated Learning on Blockchain

Federated Learning is a promising machine learning paradigm when multiple parties collaborate to build a high-quality machine learning model. Nonetheless, these parties are only willing to participate when given enough incentives, such as a fair reward based on their contributions. Many studies explored Shapley value based methods to evaluate each party's contribution to the learned model. However, they commonly assume a semi-trusted server to train the model and evaluate the data owners' model contributions, which lacks transparency and may hinder the success of federated learning in practice. In this work, we propose a blockchain-based federated learning framework and a protocol to transparently evaluate each participant's contribution. Our framework protects all parties' privacy in the model building phase and transparently evaluates contributions based on the model updates. The experiment with the handwritten digits dataset demonstrates that the proposed method can effectively evaluate the contributions.

cs.LG↗

arXiv2021

Universal characteristics of one-dimensional non-Hermitian superconductors

We establish a non-Bloch band theory for one-dimensional(1D) non-Hermitian topological superconductors. The universal physical properties of non-Hermitian topological superconductors are revealed based on the theory. According to the particle-hole symmetry, there exist reciprocal particle and hole loops of generalized Brillouin zone (GBZ). The critical point of quantum phase transition, where the energy gap closes, appears when the particle and hole loops intersect and their values of GBZ satisfy |β| = 1. If the non-Hermitian system has skin modes, these modes should be Z2 style, i.e., the corresponding eigenstates of particle and hole localize at opposite ends of an open chain, respectively. The non-Bloch band theory is applied to two examples, non-Hermitian p- and s-wave topological superconductors. Topological phase transitions occur at β_{c}= \pm 1 in the two systems. In terms of Majorana Pfaffian, a Z2 non-Bloch topological invariant is defined to establish the non-Hermitian bulk-boundary correspondence in non-Hermitian superconductors.

cond-mat.supr-con↗

arXiv2021

FLAME: Differentially Private Federated Learning in the Shuffle Model

Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train a model without collecting users' raw data. To ensure users' privacy, differentially private federated learning has been intensively studied. The existing works are mainly based on the \textit{curator model} or \textit{local model} of differential privacy. However, both of them have pros and cons. The curator model allows greater accuracy but requires a trusted analyzer. In the local model where users randomize local data before sending them to the analyzer, a trusted analyzer is not required but the accuracy is limited. In this work, by leveraging the \textit{privacy amplification} effect in the recently proposed shuffle model of differential privacy, we achieve the best of two worlds, i.e., accuracy in the curator model and strong privacy without relying on any trusted party. We first propose an FL framework in the shuffle model and a simple protocol (SS-Simple) extended from existing work. We find that SS-Simple only provides an insufficient privacy amplification effect in FL since the dimension of the model parameter is quite large. To solve this challenge, we propose an enhanced protocol (SS-Double) to increase the privacy amplification effect by subsampling. Furthermore, for boosting the utility when the model size is greater than the user population, we propose an advanced protocol (SS-Topk) with gradient sparsification techniques. We also provide theoretical analysis and numerical evaluations of the privacy amplification of the proposed protocols. Experiments on real-world dataset validate that SS-Topk improves the testing accuracy by 60.7\% than the local model based FL.

cs.LG↗

arXiv2021

Topological mild mixing of all orders along polynomials

A minimal system $(X,T)$ is topologically mildly mixing if all non-empty open subsets $U,V$, $\{n\in \Z: U\cap T^{-n}V\neq \emptyset\}$ is an IP$^*$-set. In this paper we show that if a minimal system is topologically mildly mixing, then it is mild mixing of all orders along polynomials. That is, suppose that $(X,T)$ is a topologically mildly mixing minimal system, $d\in \N$, $p_1(n),\ldots, p_d(n)$ are integral polynomials with no $p_i$ and no $p_i-p_j$ constant, $1\le i\neq j\le d$, then for all non-empty open subsets $U , V_1, \ldots, V_d $, $\{n\in \Z: U\cap T^{-p_1(n) }V_1\cap T^{-p_2(n)}V_2\cap \ldots \cap T^{-p_d(n) }V_d \neq \emptyset \}$ is an IP$^*$-set. We also give the theorem for systems under abelian group actions.

math.DS↗

arXiv2021

Strong approximation for a family of norm varieties

We study strong approximation of the equation N_{L/k}(x) = \prod_{i=1}^n p_i(t) where L/k is a finite extension of number fields and p_i(t)'s are distinct irreducible polynomials over k. We prove this equation satisfies strong approximation with Brauer-Manin obstruction when L can be imbedded in k[t]/(p_i(t)) over k for all 1\leq i\leq n. Under Schinzel's hypothesis, we prove that the same result is true without assuming that L can be imbedded in k[t]/(p_i(t)) for all 1\leq i\leq n when L/k is cyclic.

math.NT↗

arXiv2021

FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

Graph neural network (GNN) is widely used for recommendation to model high-order interactions between users and items. Existing GNN-based recommendation methods rely on centralized storage of user-item graphs and centralized model learning. However, user data is privacy-sensitive, and the centralized storage of user-item graphs may arouse privacy concerns and risk. In this paper, we propose a federated framework for privacy-preserving GNN-based recommendation, which can collectively train GNN models from decentralized user data and meanwhile exploit high-order user-item interaction information with privacy well protected. In our method, we locally train GNN model in each user client based on the user-item graph inferred from the local user-item interaction data. Each client uploads the local gradients of GNN to a server for aggregation, which are further sent to user clients for updating local GNN models. Since local gradients may contain private information, we apply local differential privacy techniques to the local gradients to protect user privacy. In addition, in order to protect the items that users have interactions with, we propose to incorporate randomly sampled items as pseudo interacted items for anonymity. To incorporate high-order user-item interactions, we propose a user-item graph expansion method that can find neighboring users with co-interacted items and exchange their embeddings for expanding the local user-item graphs in a privacy-preserving way. Extensive experiments on six benchmark datasets validate that our approach can achieve competitive results with existing centralized GNN-based recommendation methods and meanwhile effectively protect user privacy.

cs.IR↗

arXiv2020

Quantifying the Privacy-Utility Trade-offs in COVID-19 Contact Tracing Apps

How to contain the spread of the COVID-19 virus is a major concern for most countries. As the situation continues to change, various countries are making efforts to reopen their economies by lifting some restrictions and enforcing new measures to prevent the spread. In this work, we review some approaches that have been adopted to contain the COVID-19 virus such as contact tracing, clusters identification, movement restrictions, and status validation. Specifically, we classify available techniques based on some characteristics such as technology, architecture, trade-offs (privacy vs utility), and the phase of adoption. We present a novel approach for evaluating privacy using both qualitative and quantitative measures of privacy-utility assessment of contact tracing applications. In this new method, we classify utility at three (3) distinct levels: no privacy, 100% privacy, and at k where k is set by the system providing the utility or privacy.

cs.CY↗

arXiv2020

Self-Supervised Tuning for Few-Shot Segmentation

Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples. It is a challenging task since the dense prediction can only be achieved under the guidance of latent features defined by sparse annotations. Existing meta-learning method tends to fail in generating category-specifically discriminative descriptor when the visual features extracted from support images are marginalized in embedding space. To address this issue, this paper presents an adaptive tuning framework, in which the distribution of latent features across different episodes is dynamically adjusted based on a self-segmentation scheme, augmenting category-specific descriptors for label prediction. Specifically, a novel self-supervised inner-loop is firstly devised as the base learner to extract the underlying semantic features from the support image. Then, gradient maps are calculated by back-propagating self-supervised loss through the obtained features, and leveraged as guidance for augmenting the corresponding elements in embedding space. Finally, with the ability to continuously learn from different episodes, an optimization-based meta-learner is adopted as outer loop of our proposed framework to gradually refine the segmentation results. Extensive experiments on benchmark PASCAL-$5^{i}$ and COCO-$20^{i}$ datasets demonstrate the superiority of our proposed method over state-of-the-art.

cs.CV↗

arXiv2020

PriSTE: From Location Privacy to Spatiotemporal Event Privacy

Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting a user's location at each time point or a sequence of locations with different timestamps (i.e., a trajectory). We argue that existing LPPMs are not capable of protecting the sensitive information in user's spatiotemporal activities, such as "visited hospital in the last week" or "regularly commuting between Address 1 and Address 2 every morning and afternoon" (it is easy to infer that Addresses 1 and 2 may be home and office). We define such privacy as \textit{Spatiotemporal Event Privacy}, which can be formalized as Boolean expressions between location and time predicates. To understand how much spatiotemporal event privacy that existing LPPMs can provide, we first formally define spatiotemporal event privacy by extending the notion of differential privacy, and then provide a framework for calculating the spatiotemporal event privacy loss of a given LPPM under attackers who have knowledge of user's mobility pattern. We also show a case study of utilizing our framework to convert the state-of-the-art mechanism for location privacy, i.e., Planner Laplace Mechanism for Geo-indistinguishability, into one protecting spatiotemporal event privacy. Our experiments on real-life and synthetic data verified that the proposed method is effective and efficient.

cs.DB↗