Sharif Digital Repository

The ability to accurately classify temporal relation between events is an important task for a large number of natural language processing applications such as Question Answering (QA), Summarization, and Information Extraction. This paper presents a weakly-supervised machine learning approach for classification of temporal relation between events. In the first stage, the algorithm learns a general classifier from an annotated corpus. Then, it applies the hypothesis of "one type of temporal relation per discourse" and expands the scope of "discourse" from a single document to a cluster of topically-related documents. By combining the global information of such a cluster with local decisions... 

In this paper, we aim to improve meeting summarization performance using discourse specific information. Since there are intrinsically different characteristics in utterances in different types of function segments, e.g., Monologue segments versus Discussion ones, we propose a new summarization framework where different summarizers are used for different segment types. For monologue segments, we adopt the integer linear programming-based summarization method; whereas for discussion segments, we use a graph-based method to incorporate speaker information. Performance of our proposed method is evaluated using the standard AMI meeting corpus. Results show a good improvement over previous... 

Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD) modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing... 

Inter-wire coupling capacitances can lead to crosstalk fault that is strongly dependent on the transition patterns appearing on the wires. These transition patterns can cause mutual influences between adjacent wires of NoCs and as a result threaten the reliability of data transfer seriously. To increase the reliability of NoCs against the crosstalk fault, Forbidden Pattern Free (FPFs) codes are used. To generate FPF codes, numerical systems are among the overhead-efficient mechanisms. The algorithms of numerical systems have direct effect on the amounts of the codec overheads including power consumption, area occupation and performance of NoCs. To find an overhead-efficient numerical system,... 

Nonlinear dimensionality reduction methods have demonstrated top-notch performance in many pattern recognition and image classification tasks. Despite their popularity, they suffer from highly expensive time and memory requirements, which render them inapplicable to large-scale datasets. To leverage such cases we propose a new method called "Path-Based Isomap". Similar to Isomap, we exploit geodesic paths to find the low-dimensional embedding. However, instead of preserving pairwise geodesic distances, the low-dimensional embedding is computed via a path-mapping algorithm. Due to the much fewer number of paths compared to number of data points, a significant improvement in time and memory... 

In this paper, an iterative method for block-sparse recovery is suggested for target parameters estimation in a distributed MIMO radar system. The random sampling has been used as the sensing scheme in the receivers. The simulation results prove that the proposed method is superior to the other state-of-the-art techniques in the accuracy of the target estimation task. © 2016 IEEE  

Limited endurance problem and low cell reliability are main challenges of phase change memory (PCM) as an alternative to DRAM. To further prolong the lifetime of a PCM device, there exist a number of techniques that can be grouped in two categories: 1) reducing the write rate to PCM cells, and 2) handling cell failures when faults occur. Our experiments confirm that during write operations, an extensive non-uniformity in bit ips is exhibited. To reduce this non-uniformity, we present byte-level shifting scheme (BLESS) which reduces write pressure over hot cells of blocks. Additionally, this shifting mechanism can be used for error recovery purpose by using the MLC capability of PCM and... 

Online media play an important role in information societies by providing a convenient infrastructure for different processes. Information diffusion that is a fundamental process taking place on social and information networks has been investigated in many studies. Research on information diffusion in these networks faces two main challenges: (1) In most cases, diffusion takes place on an underlying network, which is latent and its structure is unknown. (2) This latent network is not fixed and changes over time. In this article, we investigate the diffusion network extraction (DNE) problem when the underlying network is dynamic and latent. We model the diffusion behavior (existence... 

High static power consumption and insufficient scalability of the commonly used DRAM main memory technology appear to be tough challenges in upcoming years. Hence, adopting new technologies, i.e. non-volatile memories (NVMs), is a proper choice. NVMs tolerate a low number of write operations while having good scalability and low static power consumption. Due to the non-destructive nature of a read operation and the long latency of a write operation in NVMs, designers use read-before-write (RBW) mechanism to mask the unchanged bits during write operation in order to reduce bit flips. Based on this observation that some specific locations of blocks are responsible for the majority of bit... 

Most of the supervised classification algorithms are proposed to classify newly seen instances based on their learned label space. However, in the case of data streams, conceptevolution is inevitable. In this paper we propose a support vector based approach for classification beyond the learned label space in data streams with regard to other challenges in data streams like concept-drift and infinite-length. We maintain the boundaries of observed classes through the stream by utilizing a support vector based method (SVDD). Newly arrived instances located outside these boundaries will be analyzed by constructing neighborhood graph to detect the emergence of a class beyond the learned label... 

Although the read disturb STT-RAM approximation technique improves performance, it consists of an approximate read plus an approximate write both at the same time. So it may degrade the application Quality of Result (QoR) considerably. On the other hand, the incorrect read decision approximation technique improves power without corrupting the stored data. We adopt an opportunity study for instruction-based distinction of read implementation to take advantage of both of the approximation techniques, while enhancing application’s QoR. We evaluated the proposed method using a set of state of the art benchmarks. The experimental results show that our method allows to increase application’s QoR... 

Multiple human 3D pose estimation is a challenging task. It is mainly because of large variations in the scale and pose of humans, fast motions, multiple persons in the scene, and arbitrary number of visible body parts due to occlusion or truncation. Some of these ambiguities can be resolved by using multiview images. This is due to the fact that more evidences of body parts would be available in multiple views. In this work, a novel method for multiple human 3D pose estimation using evidences in multiview images is proposed. The proposed method utilizes a fully connected pairwise conditional random field that contains two types of pairwise terms. The first pairwise term encodes the spatial... 

One of the most challenging tasks in computer vision is human action recognition. The recent development of depth sensors has created new opportunities in this field of research. In this paper, a novel supervised spatio-temporal kernel descriptor (SSTKDes) is proposed from RGB-depth videos to establish a discriminative and compact feature representation of actions. To enhance the descriptive and discriminative ability of the descriptor, extracted primary kernel-based features are transformed into a new space by exploiting a supervised training strategy; i.e., large margin nearest neighbor (LMNN). The LMNN highly reduces the error of a nearest neighbor classifier by minimizing the intra-class... 

We present a novel corpus for automatic punctuation prediction in persian texts. punctuation prediction is an important task in automatic speech recognition (ASR). The output of ASR systems is typically a raw sequence of words with no punctuation marks; this makes the text difficult or even impossible to make sense of for humans and also for any text processing unit. In this work, we have assembled a state-of-the-art Persian corpus to train and test a punctuation prediction model. To the best of our knowledge, this is the first ever corpus specifically designed for punctuation prediction in Persian texts. The corpus is a modification of a manually part-of-speech (POS) tagged Persian one,... 

In this paper, the problem of convolutive source separation via multimodal soft Nonnegative Matrix Co-Factorization (NMCF) is addressed. Different aspects of a phenomenon may be recorded by sensors of different types (e.g., audio and video of human speech), and each of these recorded signals is called a modality. Since the underlying phenomenon of the modalities is the same, they have some similarities. Especially, they usually have similar time changes. It means that changes in one of them usually correspond to changes in the other one. So their active or inactive periods are usually similar. Assuming this similarity, it is expected that the activation coefficient matrices of their... 

Server workloads benefit from execution on many-core processors due to their massive request-level parallelism. A key characteristic of server workloads is the large instruction footprints. While a shared last-level cache (LLC) captures the footprints, it necessitates a low-latency network-on-chip (NOC) to minimize the core stall time on accesses serviced by the LLC. As strict quality-of-service requirements preclude the use of lean cores in server processors, we observe that even state-of-the-art single-cycle multi-hop NOCs are far from ideal because they impose significant NOC-induced delays on the LLC access latency, and diminish performance. Most of the NOC delay is due to per-hop... 

In this research, a new metaheuristic optimization algorithm, inspired by biological nervous systems and artificial neural networks (ANNs) is proposed for solving complex optimization problems. The proposed method, named as neural network algorithm (NNA), is developed based on the unique structure of ANNs. The NNA benefits from complicated structure of the ANNs and its operators in order to generate new candidate solutions. In terms of convergence proof, the relationship between improvised exploitation and each parameter under asymmetric interval is derived and an iterative convergence of NNA is proved theoretically. In this paper, the NNA with its interconnected computing unit is examined... 

Adaptive thresholding methods have proved to yield a high signal-to-noise ratio (SNR) and fast convergence in sparse signal recovery. The robustness of a class of iterative sparse recovery algorithms, such as the iterative method with adaptive thresholding, has been found to outperform the state-of-art methods in respect of reconstruction quality, convergence speed, and sensitivity to noise. In this study, the authors introduce a new method for compressed sensing, using the sensing matrix and measurements. In our method, they iteratively threshold the signal and project the thresholded signal onto the translated null space of the sensing matrix. The threshold level is assigned adaptively.... 

In recent years, high interest in using Virtual Machines (VMs) in data centers and cloud computing has significantly increased the demand for high-performance data storage systems. A straightforward approach to providing a high-performance storage system is using Solid-State Drives (SSDs). Inclusion of SSDs in storage systems, however, imposes significantly higher cost compared to Hard Disk Drives (HDDs). Recent studies suggest using SSDs as a caching layer for HDD-based storage subsystems in virtualized platforms. Such studies neglect to address the endurance and cost of SSDs, which can significantly affect the efficiency of I/O caching. Moreover, previous studies only configure the cache... 

A single cell downlink scenario is considered where a multiple-antenna base station delivers contents to cache-enabled user terminals. Using the ideas from multi-server coded caching (CC) scheme developed for wired networks, a joint design of CC and general multicast beamforming is considered to benefit from spatial multiplexing gain, improved interference management and the global CC gain, simultaneously. The proposed multicast beamforming strategies utilize the multiantenna multicasting opportunities provided by the CC technique and optimally balance the detrimental impact of both noise and inter-stream interference from coded messages transmitted in parallel. The proposed scheme is shown...