840 research outputs found
Evaluating Job Training in Two Chinese Cities
Recent years have seen a surge in the evidence on the impacts of active labor market programs for numerous countries. However, little evidence has been presented on the effectiveness of such programs in China. Recent economic reforms, associated massive lay-offs, and accompanying public retraining programs make China fertile ground for rigorous impact evaluations. This study evaluates retraining programs for laid-off workers in the cities of Shenyang and Wuhan using a comparison group design. To our knowledge, this is the first evaluation of its kind in China. The evidence suggests that retraining helped workers find jobs in Wuhan, but had little effect in Shenyang. However, in terms of earnings impacts, retraining appears to have increased earnings in Shenyang but not in Wuhan. The study raises questions about the overall effectiveness of retraining expenditures, and it offers some directions for policymakers about future interventions to help laid-off workers.Active labor market programs, job training, impact evaluation, propensity score matching, China
A framework for space-efficient string kernels
String kernels are typically used to compare genome-scale sequences whose
length makes alignment impractical, yet their computation is based on data
structures that are either space-inefficient, or incur large slowdowns. We show
that a number of exact string kernels, like the -mer kernel, the substrings
kernels, a number of length-weighted kernels, the minimal absent words kernel,
and kernels with Markovian corrections, can all be computed in time and
in bits of space in addition to the input, using just a
data structure on the Burrows-Wheeler transform of the
input strings, which takes time per element in its output. The same
bounds hold for a number of measures of compositional complexity based on
multiple value of , like the -mer profile and the -th order empirical
entropy, and for calibrating the value of using the data
On the NP-Hardness of Approximating Ordering Constraint Satisfaction Problems
We show improved NP-hardness of approximating Ordering Constraint
Satisfaction Problems (OCSPs). For the two most well-studied OCSPs, Maximum
Acyclic Subgraph and Maximum Betweenness, we prove inapproximability of
and .
An OCSP is said to be approximation resistant if it is hard to approximate
better than taking a uniformly random ordering. We prove that the Maximum
Non-Betweenness Problem is approximation resistant and that there are width-
approximation-resistant OCSPs accepting only a fraction of
assignments. These results provide the first examples of
approximation-resistant OCSPs subject only to P \NP
Impossibility of independence amplification in Kolmogorov complexity theory
The paper studies randomness extraction from sources with bounded
independence and the issue of independence amplification of sources, using the
framework of Kolmogorov complexity. The dependency of strings and is
, where
denotes the Kolmogorov complexity. It is shown that there exists a
computable Kolmogorov extractor such that, for any two -bit strings with
complexity and dependency , it outputs a string of length
with complexity conditioned by any one of the input
strings. It is proven that the above are the optimal parameters a Kolmogorov
extractor can achieve. It is shown that independence amplification cannot be
effectively realized. Specifically, if (after excluding a trivial case) there
exist computable functions and such that for all -bit strings and with , then
A High Quartets Distance Construction
Given two binary trees on N labeled leaves, the quartet distance between the trees is the number of disagreeing quartets. By permuting the leaves at random, the expected quartet distance between the two trees is 23(N4) . However, no strongly explicit construction reaching this bound asymptotically was known. We consider complete, balanced binary trees on N=2n leaves, labeled by n bits long sequences. Ordering the leaves in one tree by the prefix order, and in the other tree by the suffix order, we show that the resulting quartet distance is (23+o(1))(N4) , and it always exceeds the 23(N4) bound
Practical private database queries based on a quantum key distribution protocol
Private queries allow a user Alice to learn an element of a database held by
a provider Bob without revealing which element she was interested in, while
limiting her information about the other elements. We propose to implement
private queries based on a quantum key distribution protocol, with changes only
in the classical post-processing of the key. This approach makes our scheme
both easy to implement and loss-tolerant. While unconditionally secure private
queries are known to be impossible, we argue that an interesting degree of
security can be achieved, relying on fundamental physical principles instead of
unverifiable security assumptions in order to protect both user and database.
We think that there is scope for such practical private queries to become
another remarkable application of quantum information in the footsteps of
quantum key distribution.Comment: 7 pages, 2 figures, new and improved version, clarified claims,
expanded security discussio
A Formal Study of the Privacy Concerns in Biometric-Based Remote Authentication Schemes
With their increasing popularity in cryptosystems, biometrics have attracted more and more attention from the information security community. However, how to handle the relevant privacy concerns remains to be troublesome. In this paper, we propose a novel security model to formalize the privacy concerns in biometric-based remote authentication schemes. Our security model covers a number of practical privacy concerns such as identity privacy and transaction anonymity, which have not been formally considered in the literature. In addition, we propose a general biometric-based remote authentication scheme and prove its security in our security model
Order-Revealing Encryption and the Hardness of Private Learning
An order-revealing encryption scheme gives a public procedure by which two
ciphertexts can be compared to reveal the ordering of their underlying
plaintexts. We show how to use order-revealing encryption to separate
computationally efficient PAC learning from efficient -differentially private PAC learning. That is, we construct a concept
class that is efficiently PAC learnable, but for which every efficient learner
fails to be differentially private. This answers a question of Kasiviswanathan
et al. (FOCS '08, SIAM J. Comput. '11).
To prove our result, we give a generic transformation from an order-revealing
encryption scheme into one with strongly correct comparison, which enables the
consistent comparison of ciphertexts that are not obtained as the valid
encryption of any message. We believe this construction may be of independent
interest.Comment: 28 page
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