Three-dimensional (3D) freehand ultrasound (US) reconstruction without using
any additional external tracking device has seen recent advances with deep
neural networks (DNNs). In this paper, we first investigated two identified
contributing factors of the learned inter-frame correlation that enable the
DNN-based reconstruction: anatomy and protocol. We propose to incorporate the
ability to represent these two factors - readily available during training - as
the privileged information to improve existing DNN-based methods. This is
implemented in a new multi-task method, where the anatomical and protocol
discrimination are used as auxiliary tasks. We further develop a differentiable
network architecture to optimise the branching location of these auxiliary
tasks, which controls the ratio between shared and task-specific network
parameters, for maximising the benefits from the two auxiliary tasks.
Experimental results, on a dataset with 38 forearms of 19 volunteers acquired
with 6 different scanning protocols, show that 1) both anatomical and protocol
variances are enabling factors for DNN-based US reconstruction; 2) learning how
to discriminate different subjects (anatomical variance) and predefined types
of scanning paths (protocol variance) both significantly improve frame
prediction accuracy, volume reconstruction overlap, accumulated tracking error
and final drift, using the proposed algorithm.

通过在训练过程中利用解剖和协议作为特权信息，将其作为辅助任务，以改进现有的基于深度神经网络的方法，实现无需其他外部追踪设备的三维自由手持超声重建。实验结果表明解剖和协议变异都是 DNN-based US reconstruction 的关键因素，并且通过所提出的算法可以显著提高帧预测准确性、体积重建重合度、累积跟踪错误和最终漂移。

无跟踪 3D 超声重建中的特权解剖学和协议识别

Privileged Anatomical and Protocol Discrimination in Trackerless 3D  Ultrasound Reconstruction

Speaker diarization, or the task of finding "who spoke and when", is now used
in almost every speech processing application. Nevertheless, its fairness has
not yet been evaluated because there was no protocol to study its biases one by
one. In this paper we propose a protocol and a scoring method designed to
evaluate speaker diarization fairness. This protocol is applied on a large
dataset of spoken utterances and report the performances of speaker diarization
depending on the gender, the age, the accent of the speaker and the length of
the spoken sentence. Some biases induced by the gender, or the accent of the
speaker were identified when we applied a state-of-the-art speaker diarization
method.

本研究提出了一个分析讲话者分离公平性的新协议和评分方法，对性别、年龄、口音及发音时长等因素对讲话者分离结果的影响进行了大规模数据集实验，发现讲话者口音和性别会对分离结果产生偏见。

衡量和评估讲话方规整度的方法

Towards Measuring and Scoring Speaker Diarization Fairness

Existing protocols for multilateral negotiation require a full consensus
among the negotiating parties. In contrast, we propose a protocol for
multilateral negotiation that allows partial consensus, wherein only a subset
of the negotiating parties can reach an agreement. We motivate problems that
require such a protocol and describe the protocol formally.

本文介绍了一种多边谈判协议的提议，其中允许部分一致，仅有一部分参与谈判的方可以达成共识。

MOPaC：多边谈判中的多重报价协议和部分共识

MOPaC: The Multiple Offers Protocol for Multilateral Negotiations with Partial Consensus

When collecting information, local differential privacy (LDP) alleviates
privacy concerns of users because their private information is randomized
before being sent it to the central aggregator. LDP imposes large amount of
noise as each user executes the randomization independently. To address this
issue, recent work introduced an intermediate server with the assumption that
this intermediate server does not collude with the aggregator. Under this
assumption, less noise can be added to achieve the same privacy guarantee as
LDP, thus improving utility for the data collection task.
This paper investigates this multiple-party setting of LDP. We analyze the
system model and identify potential adversaries. We then make two improvements:
a new algorithm that achieves a better privacy-utility tradeoff; and a novel
protocol that provides better protection against various attacks. Finally, we
perform experiments to compare different methods and demonstrate the benefits
of using our proposed method.

本文研究了多方设置的本地差分隐私，并分析了系统模型和潜在对手，提出了新的算法和协议以在隐私和效用之间获得更好的平衡，并通过实验证明了我们提出的方法的好处。