Quantum secret sharing between multiparty and multiparty with four states

An protocol of quantum secret sharing between multiparty and multiparty with four states is presented. We show that this protocol can make the Trojan horse attack with a multi-photon signal, the fake-signal attack with EPR pairs, the attack with single photons, and the attack with invisible photons to be nullification. In addition, we also give the upper bounds of the average success probabilities for dishonest agent eavesdropping encryption using the fake-signal attack with any two-particle entangled states.Comment: 7 page

Quantum secret sharing between m-party and n-party with six states

We propose a quantum secret sharing scheme between $m$-party and $n$-party using three conjugate bases, i.e. six states. A sequence of single photons, each of which is prepared in one of the six states, is used directly to encode classical information in the quantum secret sharing process. In this scheme, each of all $m$ members in group 1 choose randomly their own secret key individually and independently, and then directly encode their respective secret information on the states of single photons via unitary operations, then the last one (the $m$th member of group 1) sends $1/n$ of the resulting qubits to each of group 2. By measuring their respective qubits, all members in group 2 share the secret information shared by all members in group 1. The secret message shared by group 1 and group 2 in such a way that neither subset of each group nor the union of a subset of group 1 and a subset of group 2 can extract the secret message, but each whole group (all the members of each group) can. The scheme is asymptotically 100% in efficiency. It makes the Trojan horse attack with a multi-photon signal, the fake-signal attack with EPR pairs, the attack with single photons, and the attack with invisible photons to be nullification. We show that it is secure and has an advantage over the one based on two conjugate bases. We also give the upper bounds of the average success probabilities for dishonest agent eavesdropping encryption using the fake-signal attack with any two-particle entangled states. This protocol is feasible with present-day technique.Comment: 7 page

Optimal Controlled teleportation via several kinds of three-qubit states

The probability of successfully controlled teleportating an unknown qubit using a general three-particle state is investigated. We give the analytic expressions of maximal probabilities of successfully controlled teleportating an unknown qubit via several kinds of tripartite states including a tripartite GHZ state and a tripartite W-state.Comment: 15 page

Human-Robot Interaction of a Craniotomy Robot Based on Fuzzy Model Reference Learning Control

In this paper, we design a variable admittance controller and propose a variable admittance human-robot cooperative control method based on fuzzy model reference learning. The method is intended to improve the flexible adaptive capability of the robot to assist the surgeon in accomplishing different stages of the task during a craniotomy. First, the method establishes the autoregressive integrated moving average-Kalman filtering-blood pressure (ARIMA-Kalman-BP) model for the drag force prediction by taking the features of natural human arm motion as the reference model of fuzzy learning control, which solves the problem of the features of natural human arm motion being difficult to model. Then the tuning parameter rules for variable virtual damping and virtual mass of the fuzzy conductivity controller are trained by the learning mechanism. Subsequently, the variable conductivity control method based on the tuning of virtual damping and virtual mass parameters is developed by using the robot acceleration and the robot velocity as inputs, and the robot desired velocity and desired acceleration as outputs. The experimental results show that the method can meet the requirement of flexibility; the maximum error of human-machine cooperative velocity is 0.0014 m/s, and the maximum error of human-machine cooperative acceleration is lower than 0.0021 m/s2. Compared with the fuzzy control based on the variable admittance parameter alone, this method has better tracking velocity and acceleration

KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?

Recommender systems (RSs) are designed to provide personalized recommendations to users. Recently, knowledge graphs (KGs) have been widely introduced in RSs to improve recommendation accuracy. In this study, however, we demonstrate that RSs do not necessarily perform worse even if the KG is downgraded to the user-item interaction graph only (or removed). We propose an evaluation framework KG4RecEval to systematically evaluate how much a KG contributes to the recommendation accuracy of a KG-based RS, using our defined metric KGER (KG utilization efficiency in recommendation). We consider the scenarios where knowledge in a KG gets completely removed, randomly distorted and decreased, and also where recommendations are for cold-start users. Our extensive experiments on four commonly used datasets and a number of state-of-the-art KG-based RSs reveal that: to remove, randomly distort or decrease knowledge does not necessarily decrease recommendation accuracy, even for cold-start users. These findings inspire us to rethink how to better utilize knowledge from existing KGs, whereby we discuss and provide insights into what characteristics of datasets and KG-based RSs may help improve KG utilization efficiency

Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion Detection

During ultrasonic scanning processes, real-time lesion detection can assist radiologists in accurate cancer diagnosis. However, this essential task remains challenging and underexplored. General-purpose real-time object detection models can mistakenly report obvious false positives (FPs) when applied to ultrasound videos, potentially misleading junior radiologists. One key issue is their failure to utilize negative symptoms in previous frames, denoted as negative temporal contexts (NTC). To address this issue, we propose to extract contexts from previous frames, including NTC, with the guidance of inverse optical flow. By aggregating extracted contexts, we endow the model with the ability to suppress FPs by leveraging NTC. We call the resulting model UltraDet. The proposed UltraDet demonstrates significant improvement over previous state-of-the-arts and achieves real-time inference speed. To facilitate future research, we will release the code, checkpoints, and high-quality labels of the CVA-BUS dataset used in our experiments.Comment: 10 pages, 4 figures, MICCAI 2023 Early Accep

VeriFi:Towards Verifiable Federated Unlearning

Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving participant has the right to request to delete its private data from the global model. However, unlearning itself may not be enough to implement RTBF unless the unlearning effect can be independently verified, an important aspect that has been overlooked in the current literature. In this paper, we prompt the concept of verifiable federated unlearning, and propose VeriFi, a unified framework integrating federated unlearning and verification that allows systematic analysis of the unlearning and quantification of its effect, with different combinations of multiple unlearning and verification methods. In VeriFi, the leaving participant is granted the right to verify (RTV), that is, the participant notifies the server before leaving, then actively verifies the unlearning effect in the next few communication rounds. The unlearning is done at the server side immediately after receiving the leaving notification, while the verification is done locally by the leaving participant via two steps: marking (injecting carefully-designed markers to fingerprint the leaver) and checking (examining the change of the global model's performance on the markers). Based on VeriFi, we conduct the first systematic and large-scale study for verifiable federated unlearning, considering 7 unlearning methods and 5 verification methods. Particularly, we propose a more efficient and FL-friendly unlearning method, and two more effective and robust non-invasive-verification methods. We extensively evaluate VeriFi on 7 datasets and 4 types of deep learning models. Our analysis establishes important empirical understandings for more trustworthy federated unlearning

Exploring hypotheses on karez initiation and decline in the Flaming Mountains from a photo modelling campaign

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