Abstract

In this work we consider a quantum network consisting of nodes and entangled states connecting them. In every node there is a single player. The players at the intermediate nodes carry out measurements to produce an entangled state between the initial and final node. Here we address the problem that how much classical as well as quantum information can be sent from initial node to final node. In this context, we present strong theorems which state that how the teleportation capability of this remotely prepared state is linked up with the fidelities of teleportation of the resource states. Similarly, we analyze the super dense coding capacity of this remotely prepared state in terms of the capacities of the resource entangled states. However, we first obtain the relations involving the amount of entanglement of the resource states with the final state in terms concurrence. These relations are quite similar to the bounds obtained in references [G. Gour, Phys. Rev. A 71, 012318 (2005); G. Gour, B.C. Sanders, Phys. Rev. Lett. 93, 260501 (2005)]. More specifically, in an arbitrary quantum network when two nodes are not connected, our result shows how much information, both quantum and classical can be transmitted between these nodes. We show that the amount of transferable information depends on the capacities of the inter connecting entangled resources. These results have a tremendous future application in the context of determining the optimal path in a quantum network to send the maximal possible information.

Abstract

This paper initiates formal analysis of a simple, distributed algorithm for community detection on networks. We analyze an algorithm that we call Max-LPA, both in terms of its convergence time and in terms of the “quality” of the communities detected. Max-LPA is an instance of a class of community detection algorithms called label propagation algorithms. As far as we know, most analysis of label propagation algorithms thus far has been empirical in nature and in this paper we seek a theoretical understanding of label propagation algorithms. In our main result, we define a clustered version of Erdös-Rényi random graphs with clusters V 1, V 2, …, V k where the probability p, of an edge connecting nodes within a cluster V i is higher than p′, the probability of an edge connecting nodes in distinct clusters. We show that even with fairly general restrictions on p and p′ ( p=Ω(1n1/4−ϵ) for any ε > 0, p′ = O(p 2), where n is the number of nodes), Max-LPA detects the clusters V 1, V 2, …, V n in just two rounds. Based on this and on empirical results, we conjecture that Max-LPA can correctly and quickly identify communities on clustered Erdös-Rényi graphs even when the clusters are much sparser, i.e., with p=clognn for some c > 1.

Abstract

In this paper, we propose an approach for the analysis and detection of acoustic events in speech signals using the Bessel series expansion. The acoustic events analyzed are the voice onset time (VOT) and the glottal closure instants (GCIs). The hypothesis is that the Bessel functions with their damped sinusoid-like basis functions are better suited for representing the speech signals than the sinusoidal basis functions used in the conventional Fourier representation. The speech signal is band-pass filtered by choosing the appropriate range of Bessel coefficients to obtain a narrow-band signal, which is decomposed further into amplitude modulated (AM) and frequency modulated (FM) components. The discrete energy separation algorithm (DESA) is used to compute the amplitude envelope (AE) of the narrow-band AM-FM signal. Events such as the consonant and vowel beginnings in an unvoiced stop consonant vowel (SCV) and the GCIs are derived by processing the AE of the signal. The proposed approach for the detection of the VOT using the Bessel expansion is shown to perform better than the conventional Fourier representation. The performance of the proposed GCI detection method using the Bessel series expansion is compared against some of the existing methods for various noise environments and signal-to-noise ratios.

Abstract

In this work, we propose a solution with sub-logarithmic query time for counting the number of maximal points in an axis parallel query rectangle. The problem has been previously studied in [3]and [5]. To the best of our knowledge, this is the first sub-logarithmic query time solution for the problem. Our model of computation is the word RAM with word size of Θ(logn) bits.

Abstract

In this paper, we show that certain phrases although not present in a given question/query, play a very important role in answering the question. Exploring the role of such phrases in answering questions not only reduces the dependency on matching question phrases for extracting answers, but also improves the quality of the extracted answers. Here matching question phrases means phrases which co-occur in given question and candidate answers. To achieve the above discussed goal, we introduce a bigram-based word graph model populated with semantic and topical relatedness of terms in the given document. Next, we apply an improved version of ranking with a prior-based approach, which ranks all words in the candidate document with respect to a set of root words (ie non-stopwords present in the question and in the candidate document). As a result, terms logically related to the root words are scored higher than terms that are not related to the root words. Experimental results show that our devised system performs better than state-of-the-art for the task of answering Why-questions.

Abstract

Field study of thermal comfort was conducted in six naturally ventilated hostel buildings of composite climate considering Class-II protocol of field measurement during summer 2011. Total 429 survey samples of same age group (average 19.6 years) were collected including objective and subjective measurements. Statistical analysis of student's responses and measured thermal environment variables was performed to determine existing indoor environmental conditions and priority of using behavioural controls. Thermal comfort indices were also calculated and compared to the student's perceptions. Neutral temperature was found to be 30.15 °C through regression analysis, with an average clothing of 0.41 Clo (min. 0.19 Clo, max 0.82 Clo). Results have shown a wide bandwidth of neutral temperatures (25.9–33.8 °C) for the hostel buildings which is higher than national/international standards of thermal comfort …

Abstract

Thermal comfort in building results from a complex set of physical conditions, occupant sensations and preferences as well as occupants behaviour therefore thermally comfortable environment leads to better energy efficiency in buildings. A field study was carried out at five star rated (Certified by Bureau of Energy Efficiency, BEE) air conditioned office building (day time occupancy) located at Jaipur, composite climate of India. The purpose of conducting this study was to evaluate the existing thermal environmental conditions and then to propose the required thermal comfort conditions and determine energy saving potential considering adaptive thermostat control strategy. Questionnaire was developed for conducting the field survey including objective and subjective information as per class˗ II protocol of field measurements. Statistical analysis of occupant responses and recorded environmental variables was performed to determine the required comfortable temperature in the building and this temperature was used as thermostat set point of the air conditioner. The office building was modelled using eQUEST simulation program and calibrated with measured annual energy consumption. Result revealed that majority of the occupants felt thermally comfortable at proposed room temperature of 27.5oC as against the existing practice of maintaining 20-22oC at which most of the occuapants were complaining overcooling and discomfort. Proposed set point temperature of air conditioner also resulted in building energy saving of about 7.86%.

Abstract

Resource management in modern data centers has become a challenging task due to the tremendous growth of data centers. In large virtual data centers, performance of applications is highly dependent on the communication bandwidth available among virtual machines. Traditional algorithms either do not consider network I/O details of the applications or are computationally intensive. We address the problem of identifying the virtual machine clusters based on the network traffic and placing them intelligently in order to improve the application performance and optimize the network usage in large data center. We propose a greedy consolidation algorithm that ensures the number of migrations is small and the placement decisions are fast, which makes it practical for large data centers. We evaluated our approach on real world traces from private and academic data centers, using simulation and compared the existing algorithms on various parameters like scheduling time, performance improvement and number of migrations. We observed a ~70% savings of the interconnect bandwidth and overall ~60% improvements in the applications performances. Also, these improvements were produced within a fraction of scheduling time and number of migrations.

Abstract

The availability of increasingly richer applications is providing surprisingly wide range of functionalities and new use cases on mobile devices. Even tough mobile devices are becoming increasingly more powerful, the resource utilization of richer application can overwhelm resources on these devices. At the same time, ubiquitous connectivity of mobile devices also opens up the possibility of leveraging cloud resources. Seamless and flexible path to mobile cloud computing requires recognizing opportunities where the application execution on cloud instead of mobile device. In this paper we propose a cloud aware scheduler for application offloading from mobile devices to clouds. We used learning based algorithm for predicting the gain attainable using performance monitoring and high level features. We evaluated prototype of our system on various workloads and under various conditions.

Abstract

Content discovery is fast becoming the preferred tool for user engagement on the web. Discovery allows users to get educated and entertained about their topics of interest. StumbleUpon is the largest personalized content discovery engine on the Web, delivering more than 1 billion personalized recommendations per month. As a recommendation system one of the primary metrics we track is whether the user returns (retention) to use the product after their initial experience (session) with StumbleUpon