Amit Weinstein

Broadcasting with Side Information

A sender holds a word x consisting of n blocks xi, each of t bits, and wishes to broadcast a codeword to m receivers, R1,...,Rm. Each receiver Ri is interested in one block, and has prior side information consisting of some subset of the other blocks. Let betat be the minimum number of bits that has to be transmitted when each block is of length t, and let beta be the limit beta=limtrarrinfinbetat/t. Informally, beta is the average communication cost per bit in each block (for long blocks). Finding the coding rate beta, for such an informed broadcast setting, generalizes several coding theoretic parameters related to Informed Source Coding on Demand, Index Coding and Network Coding. In this work we show that usage of large data blocks may strictly improve upon the trivial encoding which treats each bit in the block independently. To this end, we provide general bounds on betat, and prove that for any constant C there is an explicit broadcast setting in which beta = 2 but beta1> C. One of these examples answers a question of . In addition, we provide examples with the following counterintuitive direct-sum phenomena. Consider a union of several mutually independent broadcast settings. The optimal code for the combined setting may yield a significant saving in communication over concatenating optimal encodings for the individual settings. This result also provides new non-linear coding schemes which improve upon the largest known gap between linear and non-linear Network Coding, thus improving the results of. The proofs are based on a relation between this problem and results in the study of Witsenhausens rate, OR graph products, colorings of Cayley graphs, and the chromatic numbers of Kneser graphs.

Partially Symmetric Functions Are Efficiently Isomorphism-Testable

Given a Boolean function f, the f-isomorphism testing problem requires a randomized algorithm to distinguish functions that are identical to f up to relabeling of the input variables from functions that are far from being so. An important open question in property testing is to determine for which functions f we can test f-isomorphism with a constant number of queries. Despite much recent attention to this question, essentially only two classes of functions were known to be efficiently isomorphism testable: symmetric functions and juntas. We unify and extend these results by showing that all partially symmetric functions -- functions invariant to the reordering of all but a constant number of their variables -- are efficiently isomorphism-testable. This class of functions, first introduced by Shannon, includes symmetric functions, juntas, and many other functions as well. We conjecture that these functions are essentially the only functions efficiently isomorphism-testable. To prove our main result, we also show that partial symmetry is efficiently testable. In turn, to prove this result we had to revisit the junta testing problem. We provide a new proof of correctness of the nearly-optimal junta tester. Our new proof replaces the Fourier machinery of the original proof with a purely combinatorial argument that exploits the connection between sets of variables with low influence and intersecting families. Another important ingredient in our proofs is a new notion of symmetric influence. We use this measure of influence to prove that partial symmetry is efficiently testable and also to construct an efficient sample extractor for partially symmetric functions. We then combine the sample extractor with the testing-by-implicit-learning approach to complete the proof that partially symmetric functions are efficiently isomorphism-testable.

Semi-Strong Colouring of Intersecting Hypergraphs

For any c ≥ 2, a c-strong colouring of the hypergraph G is an assignment of colours to the vertices of G such that, for every edge e of G , the vertices of e are coloured by at least min{ c ,| e |} distinct colours. The hypergraph G is t-intersecting if every two edges of G have at least t vertices in common. A natural variant of a question of Erdős and Lovasz is: For fixed c ≥ 2 and t ≥ 1, what is the minimum number of colours that is sufficient to c -strong colour any t -intersecting hypergraphs? The purpose of this note is to describe some open problems related to this question.

On Active and Passive Testing

Given a property of Boolean functions, what is the minimum number of queries required to determine with high probability if an input function satisfies this property? This is a fundamental question in Property Testing, where traditionally the testing algorithm is allowed to pick its queries among the entire set of inputs. Balcan et al. have recently suggested to restrict the tester to take its queries from a smaller, typically random, subset of the inputs. This model is called active testing, in resemblance of active learning. Active testing gets more dicult as the size of the set we can query from decreases, and the extreme case is when it is exactly the number of queries we perform (so the algorithm actually has no choice). This is known as passive testing, or testing from random examples. In their paper, Balcan et al. have shown that active and passive testing of dictator functions is as hard as learning them, and requires (log n) queries (unlike the classic model, in which it can be done in a constant number of queries). We extend this result to k-linear functions, proving that passive and active testing of them requires ( k logn) queries, assuming k is not too large. Other classes of functions we consider are juntas, partially symmetric functions, linear functions, and low degree polynomials. For linear functions we provide tight bounds on the query complexity in both active and passive models (which asymptotically dier). The analysis for low degree polynomials is less complete and the exact query complexity is given only for passive testing. In both these cases, the query complexity for passive testing is essentially equivalent to that of learning. For juntas and partially symmetric functions, that is, functions that depend on a small number of variables and potentially also on the Hamming weight of the input, we provide some lower and upper bounds for the dierent models. When the functions depend on a constant number of variables, our analysis for both families is asymptotically tight. Moreover, the family of partially symmetric functions is the first example for which the query complexities in all these models are asymptotically dierent. Our methods combine algebraic, combinatorial, and probabilistic techniques, including the Talagrand concentration inequality and the Erdfis‐Rado results on -systems.

Simultaneous Communication in Noisy Channels

A sender wishes to broadcast a message of length n over an alphabet to r users, where each user i, 1 ≤ i ≤ r, should be able to receive one m, possible messages. The broadcast channel has noise for each of the users (possibly different noise for different users), who cannot distinguish between some pairs of letters. The vector (m₁, m₂,...,m_r)_(n) is said to be feasible if length n encoding and decoding schemes exist enabling every user to decode his message. A rate vector (R₁, R₂...,R_r) is feasible if there exists a sequence of feasible vectors (m₁, m₂...,m_r)_(n) such that R_i = lim_n→∞ Log₂ m_i/n for all i. We determine the feasible rate vectors for several different scenarios and investigate some of their properties. An interesting case discussed is when one user can only distinguish between all the letters in a subset of the alphabet. Tight restrictions on the feasible rate vectors for some specific noise types for the other users are provided. The simplest nontrivial cases of two users and alphabet of size three are fully characterized. To this end a more general previously known result, to which we sketch an alternative proof, is used. This problem generalizes the study of the Shannon capacity of a graph, by considering more than a single user.

Local Correction with Constant Error Rate

A Boolean function f on n variables is said to be q-locally correctable if, given a black-box access to a function g which is “close” to an isomorphism fσ(x)=fσ(x1,…,xn)=f(xσ(1),…,xσ(n)) of f, we can compute fσ(x) for any

Partially Symmetric Functions Are Efficiently Isomorphism Testable

x \in\mathbb{Z}_{2}^{n}

Local correction of juntas

with good probability using q queries to g. It is known that degree d polynomials are O(2d)-locally correctable, and that most k-juntas are O(klogk)-locally correctable, where the closeness parameter, or more precisely the distance between g and fσ, is required to be exponentially small (in d and k respectively).In this work we relax the requirement for the closeness parameter by allowing the distance between the functions to be a constant. We first investigate the family of juntas, and show that almost every k-junta is O(klog2k)-locally correctable for any distance ε<0.001. A similar result is shown for the family of partially symmetric functions, that is functions which are indifferent to any reordering of all but a constant number of their variables. For both families, the algorithms provided here use non-adaptive queries and are applicable to most but not all functions of each family (as it is shown to be impossible to locally correct all of them).Our approach utilizes the measure of symmetric influence introduced in the recent analysis of testing partial symmetry of functions.