Sharif Digital Repository

We consider a content delivery network where multiple servers are connected to multiple cache-enabled clients. Clients request their corresponding contents from the servers and servers collaboratively transmit packets to fulfill all the requests. It is assumed that some contents are stored in the caches in off-peak time of the network without knowing the actual requests, the so called cache content placement phase. The goal is to minimize the worst case delay in the content delivery phase. Considering a random linear network, we propose a coding strategy which exploits servers' multiplexing gains as well as caches' global and local coding gains. The main idea in our coding scheme is to... 

In this paper, we consider multiple cache-enabled clients connected to multiple servers through an intermediate network. We design several topology-aware coding strategies for such networks. Based on the topology richness of the intermediate network, and types of coding operations at internal nodes, we define three classes of networks, namely, dedicated, flexible, and linear networks. For each class, we propose an achievable coding scheme, analyze its coding delay, and also compare it with an information theoretic lower bound. For flexible networks, we show that our scheme is order-optimal in terms of coding delay and, interestingly, the optimal memory-delay curve is achieved in certain... 

Lander-Waterman's coverage bound establishes the total number of reads required to cover the whole genome of size G bases. In fact, their bound is a direct consequence of the well-known solution to the coupon collector's problem which proves that for such genome, the total number of bases to be sequenced should be O(G ln G). Although the result leads to a tight bound, it is based on a tacit assumption that the set of reads are first collected through a sequencing process and then are processed through a computation process, i.e., there are two different machines: one for sequencing and one for processing. In this paper, we present a significant improvement compared to Lander-Waterman's... 

Considering cache enabled networks, optimal content placement minimizing the total cost of communication in such networks is studied, leading to a surprising fundamental 0–1 law for non-redundant cache placement strategies, where the total cache sizes associated with each file does not exceed the file size. In other words, for such strategies we prove that any nonredundant cache placement strategy can be transformed, with no additional cost, to a strategy in which at every node, each file is either cached completely or not cached at all. Moreover, we obtain a sufficient condition under which the optimal cache placement strategy is in fact non-redundant. This result together with the 0–1 law... 

Considering cache enabled networks, optimal content placement minimizing the total cost of communication in such networks is studied, leading to a surprising fundamental 0-1 law for non-redundant cache placement strategies, where the total cache sizes associated with each file does not exceed the file size. In other words, for such strategies, we prove that any non-redundant cache placement strategy can be transformed, with no additional cost, to a strategy in which at every node, each file is either cached completely or not cached at all. Moreover, we obtain a sufficient condition under which the optimal cache placement strategy is in fact non-redundant. This result together with the 0-1... 

A statistical algorithm is introduced to improve the false inference of active loci, in the population in which members are admixture. The algorithm uses an advanced clustering algorithm based on a Bayesian approach. The proposed algorithm simultaneously infers the hidden structure of the population. In this regard, the Monte Carlo Markov Chain (MCMC) algorithm has been used to evaluate the posterior probability distribution of the model parameters. The proposed algorithm is implemented in a bundle, and then its performance is widely evaluated in a number of artificial databases. The accuracy of the clustering algorithm is compared with the STRUCTURE method based on certain criterion. © 2019... 

A Bernoulli Mixture Model (BMM) is a finite mixture of random binary vectors with independent dimensions. The problem of clustering BMM data arises in a variety of real-world applications, ranging from population genetics to activity analysis in social networks. In this paper, we analyze the clusterability of BMMs from a theoretical perspective, when the number of clusters is unknown. In particular, we stipulate a set of conditions on the sample complexity and dimension of the model in order to guarantee the Probably Approximately Correct (PAC)-clusterability of a dataset. To the best of our knowledge, these findings are the first non-asymptotic bounds on the sample complexity of learning or... 

In this paper, we have proposed, designed, implemented, and characterized a low-cost camera-based microarray scanner which is capable of imaging fluorescently-labeled DNA or Protein microarrays. The proposed system is designed to simultaneously measure two different fluorescent dyes using two parallel channels which increase the overall scan speed. We have shown that the wide dynamic range of system makes it able to detect fluorophore densities from 100-106 molecule/μm2. In each capture, a 5.6 mm × 3.7 mm field is imaged on a 22.3 mm × 14.9 mm (18 megapixels) CMOS sensor. Therefore, the microarray can be scanned with ∼ 1μm2 spatial resolution which is high enough to distinguish borders of... 

Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the associated sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease-associated factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability... 

Centralized coded caching and delivery is studied for a partially-connected fog radio access network (F-RAN), whereby a set of H edge nodes (ENs) (without caches), connected to a cloud server via orthogonal fronthaul links, serve K users over the wireless edge. The cloud server is assumed to hold a library of N files, each of size F bits; and each user, equipped with a cache of size MF bits, is connected to a distinct set of r ENs; or equivalently, the wireless edge from the ENs to the users is modeled as a partial interference channel. The objective is to minimize the normalized delivery time (NDT), which refers to the worst case delivery latency, when each user requests a single file from... 

We propose a novel way of spreading data over multiple servers in Content Delivery Networks. Such approach provides a framework to explore trade-off between three key parameters of a content delivery network; namely, delay, storage size and communication cost. After presenting proper models for such trade-off, fundamental limits and lower and upper bounds on parameters of interest are provided for basic one-and two-dimensional spatial server arrangements. The observation that both delay and storage size can be reduced at the expense of moderate communication cost can be of great importance in a number of practical scenarios of interest. © 2018 IEEE  

In this paper, we study the problem of causal features detection in a linear model. In a mathematical model, we consider a dataset of N samples, each represented by a sequence of G binary features. Associated to each sample, there is a binary label. It is assumed that the labels are related to a latent subset of the features, called causal features, via a linear function. More precisely, in our model, each label is the result of a noisy observation of a linear function of the causal features. We assume that the number of the causal features is bounded by L, where L is a given positive integer. In this paper, our objective is to detect the set of the causal features. In this way, at the... 

In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our... 

Current techniques in sequencing a genome allow a service provider (e.g. a sequencing company) to have full access to the genome information, and thus the privacy of individuals regarding their lifetime secret is violated. In this paper, we introduce the problem of private DNA sequencing, where the goal is to keep the DNA sequence private to the sequencer. We propose an architecture, where the task of reading fragments of DNA and the task of DNA assembly are separated, the former is done at the sequencer(s), and the later is completed at a local trusted data collector. To satisfy the privacy constraint at the sequencer and reconstruction condition at the data collector, we create an... 

DNA sequencing has faced a huge demand since it was first introduced as a service to the public. This service is often offloaded to the sequencing companies who will have access to full knowledge of individuals' sequences, a major violation of privacy. To address this challenge, we propose a solution, which is based on separating the process of reading the fragments of sequences, which is done at a sequencing machine, and assembling the reads, which is done at a trusted local data collector. To confuse the sequencer, in a pooled sequencing scenario, in which multiple sequences are going to be sequenced simultaneously, for each target individual, we add fragments of one non-target individual,... 

In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our... 

It is of fundamental importance to find algorithms obtaining optimal performance for learning of statistical models in distributed and communication limited systems. Aiming at characterizing the optimal strategies, we consider learning of Gaussian Processes (GP) in distributed systems as a pivotal example. We first address a very basic problem: how many bits are required to estimate the inner-products of some Gaussian vectors across distributed machines? Using information theoretic bounds, we obtain an optimal solution for the problem which is based on vector quantization. Two suboptimal and more practical schemes are also presented as substitutes for the vector quantization scheme. In... 

The objective of a genome-wide association study (GWAS) is to associate subsequences of individuals' genomes to the observable characteristics called phenotypes (e.g., high blood pressure). Motivated by the GWAS problem, in this paper we introduce the information-theoretic problem of associated subsequence retrieval, where a dataset of N (possibly high-dimensional) sequences of length G, and their corresponding observable (binary) characteristics is given. The sequences are chosen independently and uniformly at random from XG , where X is a finite alphabet. The observable (binary) characteristic is only related to a specific unknown subsequence of length L of the sequences, called associated... 

Background: Current development of sequencing technologies is towards generating longer and noisier reads. Evidently, accurate alignment of these reads play an important role in any downstream analysis. Similarly, reducing the overall cost of sequencing is related to the time consumption of the aligner. The tradeoff between accuracy and speed is the main challenge in designing long read aligners. Results: We propose Meta-aligner which aligns long and very long reads to the reference genome very efficiently and accurately. Meta-aligner incorporates available short/long aligners as subcomponents and uses statistics from the reference genome to increase the performance. Meta-aligner estimates... 

Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on the whole gene expression profiles (GEPs). The accuracies of simple classification algorithms such as linear discriminators or support vector machines are limited due to the complexity of biological systems. We used deep neural networks to analyze 1040 GEPs from 16 different human tissues and cell types. After comparing different architectures, we identified a specific structure...