1,979 research outputs found
A Note on the Probability of Rectangles for Correlated Binary Strings
Consider two sequences of independent and identically distributed fair
coin tosses, and , which are
-correlated for each , i.e. .
We study the question of how large (small) the probability can be among all sets of a given cardinality.
For sets it is well known that the largest (smallest)
probability is approximately attained by concentric (anti-concentric) Hamming
balls, and this can be proved via the hypercontractive inequality (reverse
hypercontractivity). Here we consider the case of . By
applying a recent extension of the hypercontractive inequality of
Polyanskiy-Samorodnitsky (J. Functional Analysis, 2019), we show that Hamming
balls of the same size approximately maximize in
the regime of . We also prove a similar tight lower bound, i.e.
show that for the pair of opposite Hamming balls approximately
minimizes the probability
The Role of Correlated Noise in Quantum Computing
This paper aims to give an overview of the current state of fault-tolerant
quantum computing, by surveying a number of results in the field. We show that
thresholds can be obtained for a simple noise model as first proved in [AB97,
Kit97, KLZ98], by presenting a proof for statistically independent noise,
following the presentation of Aliferis, Gottesman and Preskill [AGP06]. We also
present a result by Terhal and Burkard [TB05] and later improved upon by
Aliferis, Gottesman and Preskill [AGP06] that shows a threshold can still be
obtained for local non-Markovian noise, where we allow the noise to be weakly
correlated in space and time. We then turn to negative results, presenting work
by Ben-Aroya and Ta-Shma [BT11] who showed conditional errors cannot be
perfectly corrected. We end our survey by briefly mentioning some more
speculative objections, as put forth by Kalai [Kal08, Kal09, Kal11]
An Optimal Lower Bound on the Communication Complexity of Gap-Hamming-Distance
We prove an optimal lower bound on the randomized communication
complexity of the much-studied Gap-Hamming-Distance problem. As a consequence,
we obtain essentially optimal multi-pass space lower bounds in the data stream
model for a number of fundamental problems, including the estimation of
frequency moments.
The Gap-Hamming-Distance problem is a communication problem, wherein Alice
and Bob receive -bit strings and , respectively. They are promised
that the Hamming distance between and is either at least
or at most , and their goal is to decide which of these is the
case. Since the formal presentation of the problem by Indyk and Woodruff (FOCS,
2003), it had been conjectured that the naive protocol, which uses bits of
communication, is asymptotically optimal. The conjecture was shown to be true
in several special cases, e.g., when the communication is deterministic, or
when the number of rounds of communication is limited.
The proof of our aforementioned result, which settles this conjecture fully,
is based on a new geometric statement regarding correlations in Gaussian space,
related to a result of C. Borell (1985). To prove this geometric statement, we
show that random projections of not-too-small sets in Gaussian space are close
to a mixture of translated normal variables
Quantum states cannot be transmitted efficiently classically
We show that any classical two-way communication protocol with shared
randomness that can approximately simulate the result of applying an arbitrary
measurement (held by one party) to a quantum state of qubits (held by
another), up to constant accuracy, must transmit at least bits.
This lower bound is optimal and matches the complexity of a simple protocol
based on discretisation using an -net. The proof is based on a lower
bound on the classical communication complexity of a distributed variant of the
Fourier sampling problem. We obtain two optimal quantum-classical separations
as easy corollaries. First, a sampling problem which can be solved with one
quantum query to the input, but which requires classical queries
for an input of size . Second, a nonlocal task which can be solved using
Bell pairs, but for which any approximate classical solution must communicate
bits.Comment: 24 pages; v3: accepted version incorporating many minor corrections
and clarification
Simulation Theorems via Pseudorandom Properties
We generalize the deterministic simulation theorem of Raz and McKenzie
[RM99], to any gadget which satisfies certain hitting property. We prove that
inner-product and gap-Hamming satisfy this property, and as a corollary we
obtain deterministic simulation theorem for these gadgets, where the gadget's
input-size is logarithmic in the input-size of the outer function. This answers
an open question posed by G\"{o}\"{o}s, Pitassi and Watson [GPW15]. Our result
also implies the previous results for the Indexing gadget, with better
parameters than was previously known. A preliminary version of the results
obtained in this work appeared in [CKL+17]
Rotation-invariant features for multi-oriented text detection in natural images.
Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes
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