Who Spoke What? A Latent Variable Framework for the Joint Decoding of Multiple Speakers and their Keywords

In this paper, we present a latent variable (LV) framework to identify all the speakers and their keywords given a multi-speaker mixture signal. We introduce two separate LVs to denote active speakers and the keywords uttered. The dependency of a spoken keyword on the speaker is modeled through a conditional probability mass function. The distribution of the mixture signal is expressed in terms of the LV mass functions and speaker-specific-keyword models. The proposed framework admits stochastic models, representing the probability density function of the observation vectors given that a particular speaker uttered a specific keyword, as speaker-specific-keyword models. The LV mass functions are estimated in a Maximum Likelihood framework using the Expectation Maximization (EM) algorithm. The active speakers and their keywords are detected as modes of the joint distribution of the two LVs. In mixture signals, containing two speakers uttering the keywords simultaneously, the proposed framework achieves an accuracy of 82% for detecting both the speakers and their respective keywords, using Student's-t mixture models as speaker-specific-keyword models.Comment: 6 pages, 2 figures Submitted to : IEEE Signal Processing Letter

Are metabolic syndrome, obstructive sleep apnoea & syndrome Z sequential?-a hypothesis

Background & Objectives: The metabolic syndrome (MS) is a risk factor for development of cardiovascular disease and is closely associated with obstructive sleep apnoea (OSA). Co-occurrence of both OSA and MS is called syndrome Z. It has been hypothesized that the OSA may be a manifestation of MS. We collected data on polysomnography (PSG) and biochemical investigations on middle aged urban Indians during a community based study in South Delhi while studying prevalence of obstructive sleep apnoea and analysed to find out the ages at which the OSA, MS and syndrome Z exist in these subjects. Methods: A 2-stage, cross-sectional, population-based study in subjects of either gender between 30-65 yr of age in 4 different socio-economic zones of the South Delhi, India, was performed earlier (from April 2005 through June 2007). In-hospital, supervised PSG studies were performed and biochemical investigations for the MS using National Cholesterol Education Programmme Adult Treatment Panel (NCEP ATP) III criteria were carried out. In this communication, the data were further analysed to estimate the prevalences of MS alone, OSA alone and syndrome Z and average ages of 3 conditions. Results: Three hundred and fifty one subjects had satisfactory PSG studies. The MS alone was present in 105 [29.9%; (95% CI 25.1-34.7)] while OSA alone was present in 24 [6.8%; (95% CI 4.2-9.5)] subjects and the syndrome Z was present in 70 [19.9%; (95% CI 15.8-24.1)] subjects. Median ages of normal subjects, and subjects with MS, OSA and syndrome Z were 40, 43, 43 and 47 yr respectively. Minimum ages of normal subjects, and subjects with MS, OSA and syndrome Z were 30, 30, 32 and 32 yr respectively. Interpretation & Conclusions: When body mass index (BMI) was normal, the increasing median ages of these conditions indicated that the MS may be the first event followed by OSA and eventually syndrome Z develops. With BMI >25 or >30 no clear-cut difference was noted, indicating that the BMI itself could have an independent role in MS, OSA and syndrome Z

Stipe anatomical studies on selected pteridophytes of South India

Present study is based on the stipe anatomy of 13 selected species of pteridophytes of South India. Detailed description, key to the taxa based on stipe anatomy, photographs and descriptions are provided

Robust parameters for automatic segmentation of speech

Automatic segmentation of speech ir on important problem that is useful in speed recognition, synthesis and coding. We explore in this paper: the robust parameter set, weightingfunction and distance measure for reliable segmentation of noisy speech. It is found that the MFCC parometers, successful in speech recognition. holds the best promise far robust segmentation also. We also explored a variery of symmetric and asymmetric weighting lifter, from which it is found that a symmetric lifter of the form $1+{Asin^{1/2}}{(\pi n/L)}$, ${0}\leq{n}\leq{L-1}$, for MFCC dimension L, is most effective. With regard to distance measure. the direct $L_2$ norm is found adequate

Geometry Meets Vectors: Approximation Algorithms for Multidimensional Packing

We study the generalized multidimensional bin packing problem (GVBP) that generalizes both geometric packing and vector packing. Here, we are given $n$ rectangular items where the $i^{\textrm{th}}$ item has width $w(i)$, height $h(i)$, and $d$ nonnegative weights $v_1(i), v_2(i), \ldots, v_{d}(i)$. Our goal is to get an axis-parallel non-overlapping packing of the items into square bins so that for all $j \in [d]$, the sum of the $j^{\textrm{th}}$ weight of items in each bin is at most 1. This is a natural problem arising in logistics, resource allocation, and scheduling. Despite being well studied in practice, surprisingly, approximation algorithms for this problem have rarely been explored. We first obtain two simple algorithms for GVBP having asymptotic approximation ratios $6(d+1)$ and $3(1 + \ln(d+1) + \varepsilon)$. We then extend the Round-and-Approx (R&A) framework [Bansal-Khan, SODA'14] to wider classes of algorithms, and show how it can be adapted to GVBP. Using more sophisticated techniques, we obtain better approximation algorithms for GVBP, and we get further improvement by combining them with the R&A framework. This gives us an asymptotic approximation ratio of $2(1+\ln((d+4)/2))+\varepsilon$ for GVBP, which improves to $2.919+\varepsilon$ for the special case of $d=1$. We obtain further improvement when the items are allowed to be rotated. We also present algorithms for a generalization of GVBP where the items are high dimensional cuboids

DSPatch: Dual Spatial Pattern Prefetcher

High main memory latency continues to limit performance of modern high-performance out-of-order cores. While DRAM latency has remained nearly the same over many generations, DRAM bandwidth has grown significantly due to higher frequencies, newer architectures (DDR4, LPDDR4, GDDR5) and 3D-stacked memory packaging (HBM). Current state-of-the-art prefetchers do not do well in extracting higher performance when higher DRAM bandwidth is available. Prefetchers need the ability to dynamically adapt to available bandwidth, boosting prefetch count and prefetch coverage when headroom exists and throttling down to achieve high accuracy when the bandwidth utilization is close to peak. To this end, we present the Dual Spatial Pattern Prefetcher (DSPatch) that can be used as a standalone prefetcher or as a lightweight adjunct spatial prefetcher to the state-of-the-art delta-based Signature Pattern Prefetcher (SPP). DSPatch builds on a novel and intuitive use of modulated spatial bit-patterns. The key idea is to: (1) represent program accesses on a physical page as a bit-pattern anchored to the first "trigger" access, (2) learn two spatial access bit-patterns: one biased towards coverage and another biased towards accuracy, and (3) select one bit-pattern at run-time based on the DRAM bandwidth utilization to generate prefetches. Across a diverse set of workloads, using only 3.6KB of storage, DSPatch improves performance over an aggressive baseline with a PC-based stride prefetcher at the L1 cache and the SPP prefetcher at the L2 cache by 6% (9% in memory-intensive workloads and up to 26%). Moreover, the performance of DSPatch+SPP scales with increasing DRAM bandwidth, growing from 6% over SPP to 10% when DRAM bandwidth is doubled.Comment: This work is to appear in MICRO 201

Guaranteeing Envy-Freeness under Generalized Assignment Constraints

We study fair division of goods under the broad class of generalized assignment constraints. In this constraint framework, the sizes and values of the goods are agent-specific, and one needs to allocate the goods among the agents fairly while further ensuring that each agent receives a bundle of total size at most the corresponding budget of the agent. Since, in such a constraint setting, it may not always be feasible to partition all the goods among the agents, we conform -- as in recent works -- to the construct of charity to designate the set of unassigned goods. For this allocation framework, we obtain existential and computational guarantees for envy-free (appropriately defined) allocation of divisible and indivisible goods, respectively, among agents with individual, additive valuations for the goods. We deem allocations to be fair by evaluating envy only with respect to feasible subsets. In particular, an allocation is said to be feasibly envy-free (FEF) iff each agent prefers its bundle over every (budget) feasible subset within any other agent's bundle (and within the charity). The current work establishes that, for divisible goods, FEF allocations are guaranteed to exist and can be computed efficiently under generalized assignment constraints. In the context of indivisible goods, FEF allocations do not necessarily exist, and hence, we consider the fairness notion of feasible envy-freeness up to any good (FEFx). We show that, under generalized assignment constraints, an FEFx allocation of indivisible goods always exists. In fact, our FEFx result resolves open problems posed in prior works. Further, for indivisible goods and under generalized assignment constraints, we provide a pseudo-polynomial time algorithm for computing FEFx allocations, and a fully polynomial-time approximation scheme (FPTAS) for computing approximate FEFx allocations.Comment: 29 page