{"title":"Successive Refinement to Caching for Dynamic Content","authors":"P. Sen, M. Gastpar","doi":"10.1109/ISIT.2019.8849619","DOIUrl":"https://doi.org/10.1109/ISIT.2019.8849619","url":null,"abstract":"To reduce the network load during peak hours, servers deliver partial data to users during the off-peak time of the network before the actual requests are known, which is known as caching. This paper studies a single user caching problem in which the file contents are subject to dynamic modifications with respect to a certain probability distribution. To cope with the dynamical nature of the file contents, a successive refinement approach to caching is presented: partial information of the original data is cached first and then if there is a modification, a refinement to the previously cached data is delivered to the user. Given a fixed cache memory, there is a tension between the rates of two cache descriptions. The problem of optimal caching strategies is formulated through a successive Gray-Wyner network, the optimal rate region of which is characterized. Some lower and upper bounds on the performance of optimal caching strategies are developed and shown to actually yield closed form solutions for certain classes of file contents.","PeriodicalId":6708,"journal":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","volume":"9 1","pages":"2484-2488"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88586842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
U. Hadar, Jingbo Liu, Yury Polyanskiy, O. Shayevitz
{"title":"Error Exponents in Distributed Hypothesis Testing of Correlations","authors":"U. Hadar, Jingbo Liu, Yury Polyanskiy, O. Shayevitz","doi":"10.1109/ISIT.2019.8849426","DOIUrl":"https://doi.org/10.1109/ISIT.2019.8849426","url":null,"abstract":"We study a distributed hypothesis testing problem where two parties observe i.i.d. samples from two ρ-correlated standard normal random variables X and Y. The party that observes the X-samples can communicate R bits per sample to the second party, that observes the Y-samples, in order to test between two correlation values. We investigate the best possible type-II error subject to a fixed type-I error, and derive an upper (impossibility) bound on the associated type-II error exponent. Our techniques include representing the conditional Y-samples as a trajectory of the Ornstein-Uhlenbeck process, and bounding the associated KL divergence using the subadditivity of the Wasserstein distance and the Gaussian Talagrand inequality.","PeriodicalId":6708,"journal":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","volume":"20 1","pages":"2674-2678"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72809618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Learning Feature Nonlinearities with Regularized Binned Regression","authors":"Samet Oymak, M. Mahdavi, Jiasi Chen","doi":"10.1109/ISIT.2019.8849541","DOIUrl":"https://doi.org/10.1109/ISIT.2019.8849541","url":null,"abstract":"For various applications, the relations between the dependent and independent variables are highly nonlinear. Consequently, for large scale complex problems, neural networks and regression trees are commonly preferred over linear models such as Lasso. This work proposes learning the feature nonlinearities by binning feature values and finding the best fit in each quantile using non-convex regularized linear regression. The algorithm first captures the dependence between neighboring quantiles by enforcing smoothness via piecewise-constant/linear approximation and then selects a sparse subset of good features. We prove that the proposed algorithm is statistically and computationally efficient. In particular, it achieves linear rate of convergence while requiring near-minimal number of samples. Evaluations on real datasets demonstrate that algorithm is competitive with current state-of-the-art and accurately learns feature nonlinearities.","PeriodicalId":6708,"journal":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","volume":"8 1","pages":"1452-1456"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84974262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Information and Energy Transmission with Experimentally-Sampled Harvesting Functions","authors":"Daewon Seo, L. Varshney","doi":"10.1109/ISIT.2019.8849689","DOIUrl":"https://doi.org/10.1109/ISIT.2019.8849689","url":null,"abstract":"This paper considers the problem of simultaneous information and energy transmission (SIET), where the energy harvesting function is only known experimentally at sample points. We investigate the performance loss due to this partial knowledge of the harvesting function in terms of transmitted energy and information. In particular, we assume harvesting functions are a class of Sobolev space and consider two cases, where experimental samples are either taken noiselessly or in the presence of noise. Using constructive function approximation and regression methods for noiseless and noisy samples respectively, we show that the worst loss in energy transmission vanishes asymptotically as the number of samples increases. Similarly, the loss in information rate vanishes in the interior of the energy domain, however, does not always vanish at maximal energy.","PeriodicalId":6708,"journal":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","volume":"86 1","pages":"126-130"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84376230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kangwook Lee, Hoon Kim, Kyungmin Lee, Changho Suh, K. Ramchandran
{"title":"Synthesizing Differentially Private Datasets using Random Mixing","authors":"Kangwook Lee, Hoon Kim, Kyungmin Lee, Changho Suh, K. Ramchandran","doi":"10.1109/ISIT.2019.8849381","DOIUrl":"https://doi.org/10.1109/ISIT.2019.8849381","url":null,"abstract":"The goal of differentially private data publishing is to release a modified dataset so that its privacy can be ensured while allowing for efficient learning. We propose a new data publishing algorithm in which a released dataset is formed by mixing ` randomly chosen data points and then perturbing them with an additive noise. Our privacy analysis shows that as ` increases, noise with smaller variance is sufficient to achieve a target privacy level. In order to quantify the usefulness of our algorithm, we adopt the accuracy of a predictive model trained with our synthetic dataset, which we call the utility of the dataset. By characterizing the utility of our dataset as a function of `, we show that one can learn both linear and nonlinear predictive models so that they yield reasonably good prediction accuracies. Particularly, we show that there exists a sweet spot on ` that maximizes the prediction accuracy given a required privacy level, or vice versa. We also demonstrate that given a target privacy level, our datasets can achieve higher utility than other datasets generated with the existing data publishing algorithms.","PeriodicalId":6708,"journal":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","volume":"24 1","pages":"542-546"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90094469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Fereydounian, Xingran Chen, Hamed Hassani, S. S. Bidokhti
{"title":"Non-asymptotic Coded Slotted ALOHA","authors":"Mohammad Fereydounian, Xingran Chen, Hamed Hassani, S. S. Bidokhti","doi":"10.1109/ISIT.2019.8849696","DOIUrl":"https://doi.org/10.1109/ISIT.2019.8849696","url":null,"abstract":"Coding for random access communication is a key challenge in Internet of Things applications. In this paper, the well-known scheme of Coded Slotted Aloha (CSA) is considered and its performance is analyzed in the non-asymptotic regime where the frame length and the number of users are finite. A density evolution framework is provided to describe the dynamics of decoding, and fundamental limits are found on the maximum channel load (i.e., the number of active users per time slot) that allows reliable communication (successful decoding). Finally, scaling laws are established, describing the non-asymptotic relation between the probability of error, the number of users, and the channel load.","PeriodicalId":6708,"journal":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","volume":"2 1","pages":"111-115"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79499822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xin Xiao, B. Vasic, Shu Lin, K. Abdel-Ghaffar, W. Ryan
{"title":"Quasi-Cyclic LDPC Codes for Correcting Multiple Phased Bursts of Erasures","authors":"Xin Xiao, B. Vasic, Shu Lin, K. Abdel-Ghaffar, W. Ryan","doi":"10.1109/ISIT.2019.8849853","DOIUrl":"https://doi.org/10.1109/ISIT.2019.8849853","url":null,"abstract":"This paper presents designs and constructions of two classes of binary quasi-cyclic LDPC codes for correcting multiple random phased-bursts of erasures over the binary erasure channel. The erasure correction of codes in both classes is characterized by the cycle and adjacency structure of their Tanner graphs. Erasure correction of these codes is a very simple process which requires only modulo-2 additions. The codes in the second class are capable of correcting locally and globally distributed phased-bursts of erasures with a two-phase iterative erasure-correction process.","PeriodicalId":6708,"journal":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","volume":"52 1","pages":"71-75"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74262149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Berta, Francesco Borderi, Omar Fawzi, V. Scholz
{"title":"Quantum Coding via Semidefinite Programming","authors":"M. Berta, Francesco Borderi, Omar Fawzi, V. Scholz","doi":"10.1109/ISIT.2019.8849325","DOIUrl":"https://doi.org/10.1109/ISIT.2019.8849325","url":null,"abstract":"We derive converging hierarchies of efficiently computable semidefinite programming outer bounds on the optimal fidelity for the transmission of quantum information over noisy quantum channels. Based on positive partial transpose conditions we give a sufficient criterion for the exact convergence at any given level of the hierarchies. The worst case convergence speed of our hierarchies is quantified via positive semidefinite representable outer approximations on the set of separable Choi states, which are based on novel finite de Finetti theorems for quantum channels.","PeriodicalId":6708,"journal":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","volume":"1 1","pages":"260-264"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77288044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On the size of pairwise-colliding permutations","authors":"J. Körner, Chandra Nair, David Ng","doi":"10.1109/ISIT.2019.8849602","DOIUrl":"https://doi.org/10.1109/ISIT.2019.8849602","url":null,"abstract":"A structured code that improves the previously best known exponential asymptotic lower bound for the maximum cardinality of a pairwise-colliding set of permutations is presented. The main contribution is an explicit construction of an infinite recursion of pairwise-colliding sets of partial-permutations.","PeriodicalId":6708,"journal":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","volume":"18 1","pages":"2354-2358"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78459394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Speeding up decoding a code with a non-trivial automorphism group up to an exponential factor","authors":"Rodolfo Canto Torres, J. Tillich","doi":"10.1109/ISIT.2019.8849628","DOIUrl":"https://doi.org/10.1109/ISIT.2019.8849628","url":null,"abstract":"We give an algorithm that is able to speed up the decoding of a code with a non-trivial automorphism group, by summing for the word that has to be decoded, all its entries belonging to a same orbit and decoding the resulting word in a reduced code. For a certain range of parameters, this results in a decoding that is faster by an exponential factor in the codelength when compared to the best algorithms for decoding generic linear codes. This algorithm is then used to break several proposals of public-key cryptosystems based on codes with a non-trivial automorphism group.","PeriodicalId":6708,"journal":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","volume":"80 1","pages":"1927-1931"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83958654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}