134 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Towards Optimal Depth-Reductions for Algebraic Formulas
Classical results of Brent, Kuck and Maruyama (IEEE Trans. Computers 1973)
and Brent (JACM 1974) show that any algebraic formula of size s can be
converted to one of depth O(log s) with only a polynomial blow-up in size. In
this paper, we consider a fine-grained version of this result depending on the
degree of the polynomial computed by the algebraic formula. Given a homogeneous
algebraic formula of size s computing a polynomial P of degree d, we show that
P can also be computed by an (unbounded fan-in) algebraic formula of depth
O(log d) and size poly(s). Our proof shows that this result also holds in the
highly restricted setting of monotone, non-commutative algebraic formulas. This
improves on previous results in the regime when d is small (i.e., d<<s). In
particular, for the setting of d=O(log s), along with a result of Raz (STOC
2010, JACM 2013), our result implies the same depth reduction even for
inhomogeneous formulas. This is particularly interesting in light of recent
algebraic formula lower bounds, which work precisely in this ``low-degree" and
``low-depth" setting. We also show that these results cannot be improved in the
monotone setting, even for commutative formulas
Proof-Carrying Data From Arithmetized Random Oracles
Proof-carrying data (PCD) is a powerful cryptographic primitive that allows mutually distrustful parties to perform distributed computation in an efficiently verifiable manner. Known constructions of PCD are obtained by recursively-composing SNARKs or related primitives. SNARKs with desirable properties such as transparent setup are constructed in the random oracle model. However, using such SNARKs to construct PCD requires heuristically instantiating the oracle and using it in a non-black-box way. Chen, Chiesa and Spooner (EC\u2722) constructed SNARKs in the low-degree random oracle model, circumventing this issue, but instantiating their model in the real world appears difficult.
In this paper, we introduce a new model: the arithmetized random oracle model (AROM). We provide a plausible standard-model (software-only) instantiation of the AROM, and we construct PCD in the AROM, given only a standard-model collision-resistant hash function. Furthermore, our PCD construction is for arbitrary-depth compliance predicates. We obtain our PCD construction by showing how to construct SNARKs in the AROM for computations that query the oracle, given an accumulation scheme for oracle queries in the AROM. We then construct such an accumulation scheme for the AROM.
We give an efficient lazy sampling algorithm (an emulator) for the ARO up to some error. Our emulator enables us to prove the security of cryptographic constructs in the AROM and that zkSNARKs in the ROM also satisfy zero-knowledge in the AROM. The algorithm is non-trivial, and relies on results in algebraic query complexity and the combinatorial nullstellensatz
State of the Art Report : Verified Computation
This report describes the state of the art in verifiable computation. The problem being solved is the following: The Verifiable Computation Problem (Verifiable Computing Problem) Suppose we have two computing agents. The first agent is the verifier, and the second agent is the prover. The verifier wants the prover to perform a computation. The verifier sends a description of the computation to the prover. Once the prover has completed the task, the prover returns the output to the verifier. The output will contain proof. The verifier can use this proof to check if the prover computed the output correctly. The check is not required to verify the algorithm used in the computation. Instead, it is a check that the prover computed the output using the computation specified by the verifier. The effort required for the check should be much less than that required to perform the computation. This state-of-the-art report surveys 128 papers from the literature comprising more than 4,000 pages. Other papers and books were surveyed but were omitted. The papers surveyed were overwhelmingly mathematical. We have summarised the major concepts that form the foundations for verifiable computation. The report contains two main sections. The first, larger section covers the theoretical foundations for probabilistically checkable and zero-knowledge proofs. The second section contains a description of the current practice in verifiable computation. Two further reports will cover (i) military applications of verifiable computation and (ii) a collection of technical demonstrators. The first of these is intended to be read by those who want to know what applications are enabled by the current state of the art in verifiable computation. The second is for those who want to see practical tools and conduct experiments themselves
Towards Optimal Depth-Reductions for Algebraic Formulas
Classical results of Brent, Kuck and Maruyama (IEEE Trans. Computers 1973) and Brent (JACM 1974) show that any algebraic formula of size s can be converted to one of depth O(log s) with only a polynomial blow-up in size. In this paper, we consider a fine-grained version of this result depending on the degree of the polynomial computed by the algebraic formula.
Given a homogeneous algebraic formula of size s computing a polynomial P of degree d, we show that P can also be computed by an (unbounded fan-in) algebraic formula of depth O(log d) and size poly(s). Our proof shows that this result also holds in the highly restricted setting of monotone, non-commutative algebraic formulas.
This improves on previous results in the regime when d is small (i.e., d = s^o(1)). In particular, for the setting of d = O(log s), along with a result of Raz (STOC 2010, JACM 2013), our result implies the same depth reduction even for inhomogeneous formulas. This is particularly interesting in light of recent algebraic formula lower bounds, which work precisely in this "low-degree" and "low-depth" setting.
We also show that these results cannot be improved in the monotone setting, even for commutative formulas
LIPIcs, Volume 244, ESA 2022, Complete Volume
LIPIcs, Volume 244, ESA 2022, Complete Volum
Vector Semantics
This open access book introduces Vector semantics, which links the formal theory of word vectors to the cognitive theory of linguistics. The computational linguists and deep learning researchers who developed word vectors have relied primarily on the ever-increasing availability of large corpora and of computers with highly parallel GPU and TPU compute engines, and their focus is with endowing computers with natural language capabilities for practical applications such as machine translation or question answering. Cognitive linguists investigate natural language from the perspective of human cognition, the relation between language and thought, and questions about conceptual universals, relying primarily on in-depth investigation of language in use. In spite of the fact that these two schools both have âlinguisticsâ in their name, so far there has been very limited communication between them, as their historical origins, data collection methods, and conceptual apparatuses are quite different. Vector semantics bridges the gap by presenting a formal theory, cast in terms of linear polytopes, that generalizes both word vectors and conceptual structures, by treating each dictionary definition as an equation, and the entire lexicon as a set of equations mutually constraining all meanings
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