Writing fashion’s future: the gods and monsters of the Anthropocene

This paper identifies two narratives of the Anthropocene and explores how they play out in the realm of future-looking fashion production. Each narrative draws on mythic comparisons to gods and monsters to express humanity’s dilemmas, albeit from different perspectives. The first is a Malthusian narrative of collapse and scarcity, brought about by the monstrous, unstoppable nature of human technology set loose on the natural world. In this vein, philosopher Slavoj Zizek (2010) draws on Biblical analogies, likening ecological crisis to one of the four horsemen of the apocalypse. To find a myth to suit the present times, novelist A.S Byatt (2011) proposes Ragnarök, a Norse myth in which the gods destroy themselves. In contrast, the second narrative is one of technological cornucopia. Stewart Brand (2009, 27), self-described ‘eco-pragmatist’ writes, ‘we are as gods and we have to get good at it’. In his view, human technologies offer the only hope to mitigating the problems caused by human technology – Brand suggests harnessing nuclear power, bioengineering of crops and the geoengineering of the planet as the way forward. Similarly, the French philosopher Bruno Latour (2012, 274), exhorts us to “love our monsters”, likening our technologies to Doctor Frankenstein’s monster – set loose upon the world, and then reviled by his creator. For both Brand and Latour, human technology may be monstrous, but it must also be turned toward solutions. Within this schema, hopeful visions of the future of fashion are similarly divided. In the techno-enabled cornucopian future, the fashion industry embraces wearable technology, speed and efficiency. Technologies such as waterless dyeing, 3D printing and self-cleaning garments shift fashion into a new era of cleaner production. Meanwhile, in the narrative of scarcity, a more cautious approach sees fashion return to a new localism and valuing of the hand-made in a time of shrinking resources. Through discussion of future-looking fashion designers, brands, and activists, this paper explores how they may align along a spectrum to one of these two grand narratives of the future. The paper will discuss how these narratives may unconsciously shape the perspective of both producers and users around the fashion of today and the fashion of tomorrow. This paper poses the question: what stories can be written for fashion’s future in the Anthropocene, and are they fated, or can they be re-written?

International contact lens prescribing in 2014

Since the late 1990s, the International Contact Lens Prescribing Survey Consortium has prospectively gathered information about 285,000 contact lens fits from more than 50 countries. This article presents our 14th annual summary of current trends published in Contact Lens Spectrum. With only minor differences in the distribution of our surveys among markets, we have continued to adopt the same approach throughout the past 18 years. Through national coordinators, we approach contact lens prescribers in each country and ask them to record information about the first 10 patients whom they fit with contact lenses after receipt of our survey form. The information collected is generic, and respondents are weighted to reflect the volume of contact lens fits undertaken by each. For this 2014 report, we present information about 25,179 contact lens fits from 32 countries...

International contact lens prescribing in 2015

We have been collecting data on worldwide contact lens prescribing habits for almost 20 years. Over this time period, we have amassed prospective information about 315,000 contact lens fits in 59 countries. This article marks our 15th report in Contact Lens Spectrum and features a breakdown of more than 23,000 contact lens fits in 34 markets. As in previous years, our international network of coordinators distributed survey forms to eyecare practitioners in their market who then recorded generic information about the first 10 patients fit with contact lenses after receipt. Information is gathered about patient age and gender; whether the contact lenses are prescribed as a new fit or a refit; contact lens material, design, and replacement frequency; number of intended days per week of use; wearing modality; and care system. Contact lens fits are weighted to reflect the number of fits undertaken by each eyecare practitioner. The study data were entered and processed at the University of Manchester and at the University of Waterloo.

Location and scale mixtures of Gaussians with flexible tail behaviour: Properties, inference and application to multivariate clustering

The family of location and scale mixtures of Gaussians has the ability to generate a number of flexible distributional forms. The family nests as particular cases several important asymmetric distributions like the Generalized Hyperbolic distribution. The Generalized Hyperbolic distribution in turn nests many other well known distributions such as the Normal Inverse Gaussian. In a multivariate setting, an extension of the standard location and scale mixture concept is proposed into a so called multiple scaled framework which has the advantage of allowing different tail and skewness behaviours in each dimension with arbitrary correlation between dimensions. Estimation of the parameters is provided via an EM algorithm and extended to cover the case of mixtures of such multiple scaled distributions for application to clustering. Assessments on simulated and real data confirm the gain in degrees of freedom and flexibility in modelling data of varying tail behaviour and directional shape.

A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: Application to robust clustering

We propose a family of multivariate heavy-tailed distributions that allow variable marginal amounts of tailweight. The originality comes from introducing multidimensional instead of univariate scale variables for the mixture of scaled Gaussian family of distributions. In contrast to most existing approaches, the derived distributions can account for a variety of shapes and have a simple tractable form with a closed-form probability density function whatever the dimension. We examine a number of properties of these distributions and illustrate them in the particular case of Pearson type VII and t tails. For these latter cases, we provide maximum likelihood estimation of the parameters and illustrate their modelling flexibility on simulated and real data clustering examples.

Alice Davis Menken papers, undated, 1880-1936.

Primarily correspondence, reports, etc. relating to activities with the Society of New York State Women, Jewish Welfare Board, Jewish Protectory and Aid Society (later called Jewish Board of Guardians), New York City Woman's Night Court, Hudson State Training School, New York State Reformatory for Women, Society for Political Study, Daughters of American Revolution, Progressive Party, Mayor's Committee of Women on National Defense, New York, Congregation Shearith Israel, Florence Crittendon League, Committee of Fourteen and the Inwood House; includes also journals, diaries and other material relating to personal life, and a biographical sketch submitted to the Jewish Biographical Bureau, and copies of published and manuscript articles. Contains a "Survey of Reformatory and Correctional Institutions and Agencies As Related To The Problem of Commercialized Vice" in manuscript, submitted in August, 1919 and also material relating to program and activities of New York Training School For Community Center Workers.

Phase separation in critical and off-critical fluids: specific experimental behaviours at criticality

The phase separation in fluids close to a critical point can be observed in the form of either an interconnected pattern (critical case) or a disconnected pattern (off-critical case). These two regimes have been investigated in different ways. First, a sharp change in pattern is shown to occur very close to the critical point when the composition is varied. No crossover has been observed between the t1 behaviour (interconnected) and a t1/3 behaviour (disconnected), where t is time. This latter growth law, which occurs in the case of compact droplets, will be discussed. Second, it has been observed that a growing interconnected pattern leaves a signature in the form of small droplets. The origin of such a distribution will be discussed in terms of coalescence of domains. No distribution of this kind is observed in the off-critical case.

Cramer-Rao bounds for source localization in shallow ocean with generalized Gaussian noise

Localization of underwater acoustic sources is a problem of great interest in the area of ocean acoustics. There exist several algorithms for source localization based on array signal processing.It is of interest to know the theoretical performance limits of these estimators. In this paper we develop expressions for the Cramer-Rao-Bound (CRB) on the variance of direction-of-arrival(DOA) and range-depth estimators of underwater acoustic sources in a shallow range-independent ocean for the case of generalized Gaussian noise. We then study the performance of some of the popular source localization techniques,through simulations, for DOA/range-depth estimation of underwater acoustic sources in shallow ocean by comparing the variance of the estimators with the corresponding CRBs.

Design of MMSE filterbank precoder and equalizer for MIMO frequency selective channels

In this paper, we consider the problem of designing minimum mean squared error (MMSE) filterbank precoder and equalizer for multiple input multiple output (MIMO) frequency selective channels. We derive the conditions to be satisfied by the optimal precoder-equalizer pair, and provide an iterative algorithm for solving them. The optimal design is very general, in that it is not constrained by channel dimensions, channel order, channel rank, or the input constellation. We also discuss some pertinent difierences between the filterbank approach and the space-time approach to the design of optimal precoder and equalizer. Simulation results demonstrate that the proposed design performs better than the space-time systems while supporting a higher data rate.

Novel auditory motivated subband temporal envelope based fundamental frequency estimation algorithm

We address the problem of estimating the fundamental frequency of voiced speech. We present a novel solution motivated by the importance of amplitude modulation in sound processing and speech perception. The new algorithm is based on a cumulative spectrum computed from the temporal envelope of various subbands. We provide theoretical analysis to derive the new pitch estimator based on the temporal envelope of the bandpass speech signal. We report extensive experimental performance for synthetic as well as natural vowels for both realworld noisy and noise-free data. Experimental results show that the new technique performs accurate pitch estimation and is robust to noise. We also show that the technique is superior to the autocorrelation technique for pitch estimation.

A hybrid pre-whitening technique for detection of additive spread spectrum watermarks in audio signals

Pre-whitening techniques are employed in blind correlation detection of additive spread spectrum watermarks in audio signals to reduce the host signal interference. A direct deterministic whitening (DDW) scheme is derived in this paper from the frequency domain analysis of the time domain correlation process. Our experimental studies reveal that, the Savitzky-Golay Whitening (SGW), which is otherwise inferior to DDW technique, performs better when the audio signal is predominantly lowpass. The novelty of this paper lies in exploiting the complementary nature to the two whitening techniques to obtain a hybrid whitening (HbW) scheme. In the hybrid scheme the DDW and SGW techniques are selectively applied, based on short time spectral characteristics of the audio signal. The hybrid scheme extends the reliability of watermark detection to a wider range of audio signals.

MULTI-PITCH TRACKING USING GAUSSIAN MIXTURE MODEL WITH TIME VARYING PARAMETERS AND GRATING COMPRESSION TRANSFORM

Grating Compression Transform (GCT) is a two-dimensional analysis of speech signal which has been shown to be effective in multi-pitch tracking in speech mixtures. Multi-pitch tracking methods using GCT apply Kalman filter framework to obtain pitch tracks which requires training of the filter parameters using true pitch tracks. We propose an unsupervised method for obtaining multiple pitch tracks. In the proposed method, multiple pitch tracks are modeled using time-varying means of a Gaussian mixture model (GMM), referred to as TVGMM. The TVGMM parameters are estimated using multiple pitch values at each frame in a given utterance obtained from different patches of the spectrogram using GCT. We evaluate the performance of the proposed method on all voiced speech mixtures as well as random speech mixtures having well separated and close pitch tracks. TVGMM achieves multi-pitch tracking with 51% and 53% multi-pitch estimates having error <= 20% for random mixtures and all-voiced mixtures respectively. TVGMM also results in lower root mean squared error in pitch track estimation compared to that by Kalman filtering.

VISUAL OBJECT TRACKING VIA RANDOM FERNS BASED CLASSIFICATION

Designing a robust algorithm for visual object tracking has been a challenging task since many years. There are trackers in the literature that are reasonably accurate for many tracking scenarios but most of them are computationally expensive. This narrows down their applicability as many tracking applications demand real time response. In this paper, we present a tracker based on random ferns. Tracking is posed as a classification problem and classification is done using ferns. We used ferns as they rely on binary features and are extremely fast at both training and classification as compared to other classification algorithms. Our experiments show that the proposed tracker performs well on some of the most challenging tracking datasets and executes much faster than one of the state-of-the-art trackers, without much difference in tracking accuracy.

TIME VARYING LINEAR PREDICTION USING SPARSITY CONSTRAINTS

Time-varying linear prediction has been studied in the context of speech signals, in which the auto-regressive (AR) coefficients of the system function are modeled as a linear combination of a set of known bases. Traditionally, least squares minimization is used for the estimation of model parameters of the system. Motivated by the sparse nature of the excitation signal for voiced sounds, we explore the time-varying linear prediction modeling of speech signals using sparsity constraints. Parameter estimation is posed as a 0-norm minimization problem. The re-weighted 1-norm minimization technique is used to estimate the model parameters. We show that for sparsely excited time-varying systems, the formulation models the underlying system function better than the least squares error minimization approach. Evaluation with synthetic and real speech examples show that the estimated model parameters track the formant trajectories closer than the least squares approach.