To securely transmit sensitive information into the future, Time-Lock Puzzles
(TLPs) have been developed. Their applications include scheduled payments,
timed commitments, e-voting, and sealed-bid auctions. Homomorphic TLP is a key
variant of TLP that enables computation on puzzles from different clients. This
allows a solver/server to tackle only a single puzzle encoding the
computation's result. However, existing homomorphic TLPs lack support for
verifying the correctness of the computation results. We address this
limitation by introducing Tempora-Fusion, a TLP that allows a server to perform
homomorphic linear combinations of puzzles from different clients while
ensuring verification of computation correctness. This scheme avoids
asymmetric-key cryptography for verification, thus paving the way for efficient
implementations. We discuss our scheme's application in various domains, such
as federated learning, scheduled payments in online banking, and e-voting.

为了安全地将敏感信息传输到未来，我们引入了 Tempora-Fusion，这是一种允许服务器对来自不同客户的拼图进行同态线性组合并确保计算正确性验证的 TLP 方案。该方案避免了非对称密钥密码学的使用，从而为高效实现铺平了道路。我们讨论了该方案在联邦学习、在线银行中的定期支付和电子投票等各个领域的应用。

时间融合：具有高效可验证同态线性组合的时间锁谜题

Tempora-Fusion: Time-Lock Puzzle with Efficient Verifiable Homomorphic  Linear Combination

We introduce a learning-based framework to optimize tensor programs for deep
learning workloads. Efficient implementations of tensor operators, such as
matrix multiplication and high dimensional convolution, are key enablers of
effective deep learning systems. However, existing systems rely on manually
optimized libraries such as cuDNN where only a narrow range of server class
GPUs are well-supported. The reliance on hardware-specific operator libraries
limits the applicability of high-level graph optimizations and incurs
significant engineering costs when deploying to new hardware targets. We use
learning to remove this engineering burden. We learn domain-specific
statistical cost models to guide the search of tensor operator implementations
over billions of possible program variants. We further accelerate the search by
effective model transfer across workloads. Experimental results show that our
framework delivers performance competitive with state-of-the-art hand-tuned
libraries for low-power CPU, mobile GPU, and server-class GPU.

该论文介绍了一个基于深度学习工作负载的张量程序优化学习框架，其通过学习领域特定的统计成本模型来引导张量操作实现的搜索，进而加速搜索，实现性能与手动调整的图书馆竞争力相当。

学习优化张量程序

Learning to Optimize Tensor Programs

Engineering efficient implementations of compact and succinct structures is a
time-consuming and challenging task, since there is no standard library of
easy-to- use, highly optimized, and composable components. One consequence is
that measuring the practical impact of new theoretical proposals is a difficult
task, since older base- line implementations may not rely on the same basic
components, and reimplementing from scratch can be very time-consuming. In this
paper we present a framework for experimentation with succinct data structures,
providing a large set of configurable components, together with tests,
benchmarks, and tools to analyze resource requirements. We demonstrate the
functionality of the framework by recomposing succinct solutions for document
retrieval.

本文提出一种用于 experiment 和评价 succinct 数据结构的框架，可配置成注重准确资源估计、提供测试和工具以及提出的 solutions 用于 Document 检索等应用。