SearcharxivSearch

arXiv subjects

Weihong Wang

Publications and source records attributed to Weihong Wang.

10 recordsLinked to original sources

Voltage-Controlled Phosphate Precipitation Gating in Solid-State Nanopore Memristors

Nanofluidic memristors preserve a record of electrical activity via ion migration and alterations in conductance that depend on the history of the device s state. These characteristics make them suitable for aqueous, energy efficient, and biologically compatible neuromorphic systems. To establish the viability of fluidic memristors for mimicking the brain s dynamic behavior, a more thorough understanding of the memristive materials and the underlying switching processes is required. In this study, we systematically examined a recently introduced memristive device based on inpore chemical reactions, where the combined influence of electrolyte composition and pore architecture on precipitation gated memory remains poorly understood. To address this, we constructed an asymmetric electrochemical system using CaCl2 and phosphate solutions separated by SiNx solid state nanopores. We explored how variations in pH, phosphate concentration, pore geometry, and voltage pulsing regimens affect the electrical characteristics and memristive performance. Comparison of the single pore and the array showed that parallel pores produced smoother pH and concentration dependent hysteresis and pulse responses, whereas the single pore retained larger, nonmonotonic changes.

physics.app-ph

The Masks We (Think We) Wear: Privacy Threats of Browser-Extension Wallets in the Web3 Ecosystem

Cryptocurrency wallets are the primary interface for managing pseudonymous blockchain addresses, viewing balances, and interacting with Web3 applications. Although users typically assume that their addresses remain independent of each other unless intentionally revealed, modern wallets routinely communicate with both blockchain infrastructure and decentralized applications (dApps), generating network-side and web-side signals that may undermine this assumption. In this paper, we identify and formalize five privacy threats that arise directly from wallets interacting with the network and the web browser. Using large-scale dynamic measurements of 85 of the most popular Chrome Web Store browser-extension wallets (representing 35.16 million users), we observe that routine remote procedure call (RPC) operations leak structural links between a user's addresses; that the majority of Ethereum wallets implement permission revocation inconsistently and continue to expose previously revoked addresses across sessions; and that many wallets inject their provider interfaces into cross-origin iframes, enabling passive cross-site tracking beyond dApps and potentially real-world identity deanonymization without user interaction. Taken together, our results show that these wallet behaviors leak sensitive information that can be used to link multiple addresses to the same user, track wallet users across sessions and sites, and connect their browsing activity to their on-chain wealth. We discuss practical mitigations and show that many of these threats can be substantially reduced through improved wallet implementation, stronger privacy considerations in ecosystem standards, and stricter controls over provider exposure. Our results highlight the need for standardized, privacy-preserving wallet architectures.

cs.CR

Plasmonic nanopore to monitor in-pore chemistry

In solid-state nanopores, achieving reliable control over pore aperture opening and closing (gating) remains a major challenge. Gating can be driven by the applied voltage involving electrically tunable chemical reactions, achieved by selecting appropriate compounds within the nanopore volume. In particular, cyclic precipitation and dissolution of metal phosphates can be triggered by regulating cation transport through an applied transmembrane voltage, thereby enabling reversible pore gating. Under negative bias, metal phosphate precipitates form inside the pore, obstructing ion flow and reducing current. Switching the polarity dissolves the precipitates, restoring ionic conductance. This process effectively produces a nanofluidic diode characterized by a remarkably high rectification ratio. To probe these localized chemical reactions more directly, we employed a plasmonic nanopore that generates strong confined fields, enabling surface-enhanced Raman scattering (SERS) measurements within the nanopore volume during cyclic gating. These measurements not only validate the proposed in-pore chemistry but also highlight the potential of plasmonic nanopores as powerful tools for monitoring nanoscale chemical processes with high spatial resolution.

physics.app-ph

Depermissioning Web3: a Permissionless Accountable RPC Protocol for Blockchain Networks

In blockchain networks, so-called "full nodes" serve data to and relay transactions from clients through an RPC interface. This serving layer enables integration of "Web3" data, stored on blockchains, with "Web2" mobile or web applications that cannot directly participate as peers in a blockchain network. In practice, the serving layer is dominated by a small number of centralized services ("node providers") that offer permissioned access to RPC endpoints. Clients register with these providers because they offer reliable and convenient access to blockchain data: operating a full node themselves requires significant computational and storage resources, and public (permissionless) RPC nodes lack financial incentives to serve large numbers of clients with consistent performance. Permissioned access to an otherwise permissionless blockchain network raises concerns regarding the privacy, integrity, and availability of data access. To address this, we propose a Permissionless Accountable RPC Protocol (PARP). It enables clients and full nodes to interact pseudonymously while keeping both parties accountable. PARP leverages "light client" schemes for essential data integrity checks, combined with fraud proofs, to keep full nodes honest and accountable. It integrates payment channels to facilitate micro-payments, holding clients accountable for the resources they consume and providing an economic incentive for full nodes to serve. Our prototype implementation for Ethereum demonstrates the feasibility of PARP, and we quantify its overhead compared to the base RPC protocol.

cs.CR

Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging

Medical imaging provides essential visual insights for diagnosis, and multimodal large language models (MLLMs) are increasingly utilized for its analysis due to their strong generalization capabilities; however, the underlying factors driving this generalization remain unclear. Current research suggests that multi-task training outperforms single-task as different tasks can benefit each other, but they often overlook the internal relationships within these tasks. To analyze this phenomenon, we attempted to employ compositional generalization (CG), which refers to the models' ability to understand novel combinations by recombining learned elements, as a guiding framework. Since medical images can be precisely defined by Modality, Anatomical area, and Task, naturally providing an environment for exploring CG, we assembled 106 medical datasets to create Med-MAT for comprehensive experiments. The experiments confirmed that MLLMs can use CG to understand unseen medical images and identified CG as one of the main drivers of the generalization observed in multi-task training. Additionally, further studies demonstrated that CG effectively supports datasets with limited data and confirmed that MLLMs can achieve CG across classification and detection tasks, underscoring its broader generalization potential. Med-MAT is available at https://github.com/FreedomIntelligence/Med-MAT.

cs.CV

A Scalable State Sharing Protocol for Low-Resource Validator Nodes in Blockchain Networks

The perpetual growth of data stored on popular blockchains such as Ethereum leads to significant scalability challenges and substantial storage costs for operators of full nodes. Increasing costs may lead to fewer independently operated nodes in the network, which poses risks to decentralization (and hence network security), but also pushes decentralized app developers towards centrally hosted API services. This paper introduces a new protocol that allows validator nodes to participate in a blockchain network without the need to store the full state of the network on each node. The key idea is to use the blockchain network as both a replicated state machine and as a distributed storage system. By distributing states across nodes and enabling efficient data retrieval through a Kademlia-inspired routing protocol, we reduce storage costs for validators. Cryptographic proofs (such as Merkle proofs) are used to allow nodes to verify data stored by other nodes without having to trust those nodes directly. While the protocol trades off data storage for increased network bandwidth, we show how gossiping and caching can minimize the increased bandwidth needs. To validate our state sharing protocol, we conduct an extensive quantitative analysis of Ethereum's data storage and data access patterns. Our findings indicate that while our protocol significantly lowers storage needs, it comes with an increased bandwidth usage ranging from 1.5 MB to 5 MB per block, translating to an additional monthly bandwidth of 319 GB to 1,065 GB. Despite this, the size remains small enough such that it can be passed to all nodes and validated within Ethereum's 12-second block validation window. Further analysis shows that Merkle proofs are the most significant contributor to the additional bandwidth. To address this concern, we also analyze the impact of switching to the more space-efficient Verkle Proofs.

cs.DC

Static Application Security Testing of Consensus-Critical Code in the Cosmos Network

Blockchains require deterministic execution in order to reach consensus. This is often guaranteed in languages designed to write smart contracts, such as Solidity. Application-specific blockchains or ``appchains'' allow the blockchain application logic to be written using general-purpose programming languages, giving developers more flexibility but also additional responsibilities. In particular, developers must ensure that their blockchain application logic does not contain any sources of non-determinism. Any source of non-determinism may be a potential source of vulnerabilities. This paper focuses on the use of Static Application Security Testing (SAST) tools to detect such sources of non-determinism at development time. We focus on Cosmos, a prominent open-source project that lets developers build interconnected networks of application-specific blockchains. Cosmos provides a Software Development Kit (SDK) that allows these chains to be implemented in the Go programming language. We create a corpus of 11 representative Cosmos-based appchains to analyze for sources of non-determinism in Go. As part of our study, we identified cosmos-sdk-codeql, a set of CodeQL code analysis rules for Cosmos applications. We find that these rules generate many false positives and propose a refactored set of rules that more precisely detects sources of non-determinism only in code that runs as part of the blockchain logic. We demonstrate a significant increase in the precision of the rules, making the SAST tool more effective and hence potentially contributing to enhanced security for Cosmos-based blockchains.

cs.CR

An Incentive-Compatible Mechanism for Decentralized Storage Network

The dominance of a few big companies in the storage market arising various concerns including single point of failure, privacy violation, and oligopoly. To eliminate the dependency on such a centralized storage architecture, several Decentralized Storage Network (DSN) schemes such as Filecoin, Sia, and Storj have been introduced. DSNs leverage blockchain technology to create a storage platform such that the micro storage providers can also participate in the storage market. To verify the accurate data storage by the storage providers during a storage contract, DSNs apply a Proof of Storage (PoS) scheme to continuously inspect the storage service. However, continuous verification of the storage provider imposes an extra cost to the network and therefore end-users. Moreover, DSN's PoS verification is vulnerable to a service denying attack in which the storage provider submits valid PoS to the network while denying the service to the client. Considering the benefits and existing challenges of DSNs, this paper introduces a novel incentive-compatible DSN scheme. In this scheme, the PoS is conducted only if the client submits a challenge request. We model the storage service as a repeated dynamic game and set the players' payoffs such that the storage provider's dominant strategy is to honestly follow the storage contract. Our proposed mechanism leverages the smart-contract and oracle network to govern the storage agreement between the client and storage provider efficiently. Furthermore, our scheme is independent of a specific blockchain platform but can be plugged into any blockchain platform with smart-contract execution capability. As a proof of concept, we have implemented our scheme using solidity language and chainlink oracle network. The performance analysis demonstrates the applicability of our scheme.

cs.GT

Energy consumption forecasting using a stacked nonparametric Bayesian approach

In this paper, the process of forecasting household energy consumption is studied within the framework of the nonparametric Gaussian Process (GP), using multiple short time series data. As we begin to use smart meter data to paint a clearer picture of residential electricity use, it becomes increasingly apparent that we must also construct a detailed picture and understanding of consumer's complex relationship with gas consumption. Both electricity and gas consumption patterns are highly dependent on various factors, and the intricate interplay of these factors is sophisticated. Moreover, since typical gas consumption data is low granularity with very few time points, naive application of conventional time-series forecasting techniques can lead to severe over-fitting. Given these considerations, we construct a stacked GP method where the predictive posteriors of each GP applied to each task are used in the prior and likelihood of the next level GP. We apply our model to a real-world dataset to forecast energy consumption in Australian households across several states. We compare intuitively appealing results against other commonly used machine learning techniques. Overall, the results indicate that the proposed stacked GP model outperforms other forecasting techniques that we tested, especially when we have a multiple short time-series instances.

cs.LG

Learning to Guide: Guidance Law Based on Deep Meta-learning and Model Predictive Path Integral Control

In this paper, we present a novel guidance scheme based on model-based deep reinforcement learning (RL) technique. With model-based deep RL method, a deep neural network is trained as a predictive model of guidance dynamics which is incorporated into a model predictive path integral (MPPI) control framework. However the traditional MPPI framework assumes the actual environment similar to the training dataset for the deep neural network which is impractical in practice with different maneuvering of target, other perturbations and actuator failures. To address this problem, our method utilize meta-learning technique to make the deep neural dynamics model adapt to such changes online. With this approach we can alleviate the performance deterioration of standard MPPI control caused by the difference between actual environment and training data. Then, a novel guidance law for a varying velocity interceptor intercepting maneuvering target with desired terminal impact angle under actuator failure is constructed based on aforementioned techniques. Simulation and experiment results under different cases show the effectiveness and robustness of the proposed guidance law in achieving successful interceptions of maneuvering target.

cs.RO