Learning on non-stationary data with re-weighting

Many real-world learning scenarios face the challenge of slow concept drift, where data distributions change gradually over time. In this setting, we pose the problem of learning temporally sensitive importance weights for training data, in order to optimize predictive accuracy. We propose a class of temporal reweighting functions that can capture multiple timescales of change in the data, as well as instance-specific characteristics. We formulate a bi-level optimization criterion, and an associated meta-learning algorithm, by which these weights can be learned. In particular, our formulation trains an auxiliary network to output weights as a function of training instances, thereby compactly representing the instance weights. We validate our temporal reweighting scheme on a large real-world dataset of 39M images spread over a 9 year period. Our extensive experiments demonstrate the necessity of instance-based temporal reweighting in the dataset, and achieve significant improvements to classical batch-learning approaches. Further, our proposal easily generalizes to a streaming setting and shows significant gains compared to recent continual learning methods

Selective classification using a robust meta-learning approach

Selective classification involves identifying the subset of test samples that a model can classify with high accuracy, and is important for applications such as automated medical diagnosis. We argue that this capability of identifying uncertain samples is valuable for training classifiers as well, with the aim of building more accurate classifiers. We unify these dual roles by training a single auxiliary meta-network to output an importance weight as a function of the instance. This measure is used at train time to reweight training data, and at test-time to rank test instances for selective classification. A second, key component of our proposal is the meta-objective of minimizing dropout variance (the variance of classifier output when subjected to random weight dropout) for training the metanetwork. We train the classifier together with its metanetwork using a nested objective of minimizing classifier loss on training data and meta-loss on a separate meta-training dataset. We outperform current state-of-the-art on selective classification by substantial margins--for instance, upto 1.9% AUC and 2% accuracy on a real-world diabetic retinopathy dataset. Finally, our meta-learning framework extends naturally to unsupervised domain adaptation, given our unsupervised variance minimization meta-objective. We show cumulative absolute gains of 3.4% / 3.3% accuracy and AUC over the other baselines in domain shift settings on the Retinopathy dataset using unsupervised domain adaptation

Experimental demonstration of 25 GHz wideband chaos in symmetric dual port EDFRL

We study dynamics of chaos in dual port erbium-doped fiber ring laser (EDFRL). The laser consists of two erbium-doped fibers, intracavity filters at 1549.30 nm, isolators, and couplers. At both ports, the laser transitions into the chaotic regime for pump currents greater than 100 mA via period doubling route. We calculate the Lyapunov exponents using Rosenstein’s algorithm. We obtain positive values for the largest Lyapunov exponent (≈0.2) for embedding dimensions 5, 7, 9 and 11 indicating chaos. We compute the power spectrum of the photocurrents at the output ports of the laser. We observe a bandwidth of ≈ 25 GHz at both ports. This ultra wideband nature of chaos obtained has potential applications in high speed random number generation and communication

Transporte autorregulador de fármacos: un sistema inteligente de administración de fármacos

Introduction: Main objective of any pharmaceutical scientist is to develop drug delivery system that is safe, effective, stable, having good patient compliance and fulfill the requirements of customers. Lead to a great interest of research to develop the drug delivery system that will enable to supply drug «on-demand » basis. These «stimuli-responsive and intelligent» systems have been designed to deliver the drug on various times or at various sites in the body, according to a stimulus that is either endogenous or externally applied. Objectives: This paper aim to review various researches in the field of self regulatory drug delivery systems in tabular form so one can utilize these finding for further development of in intelligent drug delivery systems. Method: Various physicochemical principles and chemical schemes have been applied by researchers to get release pattern of drug as per requirement of body. Such devices can be used for intelligent drug delivery needed for the treatment of many diseases like diabetes. Results & Discussion: This type of intelligent system firstly sense the signals caused by diseaseà judge the magnitude of signalsà and then release the drug in direct response. Conclusion: In this article we have discuss various innovations in the field of self regulatory drug delivery Systems and suggest that here is a lot of research scope in this field.Introducción: El objetivo principal de cualquier científico farmacéutico es desarrollar un sistema de administración de fármacos que sea seguro, efectivo, estable, que cumpla con los requisitos del paciente y cumpla con los requisitos de los clientes. Llevar a un gran interés de investigación para desarrollar el sistema de entrega de medicamentos que permitirá suministrar medicamentos «a demanda». Estos sistemas «sensibles a estímulos e inteligentes» han sido diseñados para administrar el farmaco en varios momentos o en varios sitios en el cuerpo, de acuerdo con un estímulo endógeno o aplicado externamente. Objetivos: Este artículo tiene como objetivo revisar diversas investigaciones en el campo de los sistemas autorreguladores de administración de fármacos en forma tabular para que uno pueda utilizar estos hallazgos para un mayor desarrollo de sistemas inteligentes de administración de fármacos. Método: Los investigadores han aplicado varios principios fisicoquímicos y esquemas químicos para obtener el patrón de liberación del fármaco según las necesidades del cuerpo. Dichos dispositivos se pueden usar para la administración inteligente de medicamentos necesarios para el tratamiento de muchas enfermedades, como la diabetes. Resultados y discusión: Este tipo de sistema inteligente primero detecta las señales causadas por la enfermedad, juzga la magnitud de las señales y luego libera la droga en respuesta directa. Conclusión: En este artículo, hemos discutido varias innovaciones en el campo de los sistemas autorreguladores de administración de fármacos y sugerimos que aquí hay mucho campo de investigación en este campo

Recovery of acerbic anaerobic digester for biogas production from pomegranate shells using organic loading approach

86-94Anaerobic digestion of pomegranate shells was conducted in 25 L bioreactor operating at 35±0.5°C. The digester showed a reasonable amount of biogas (0.71 m3/kg VS fed) and methane (55.7%) with stable pH and acid: alkali profiles when operated at organic loading rate (OLR) from 1.0 to 3.0 kg VS/day/m−3. The reactor exhibited stable performance with methane yield of 0.44 m3/kg VS fed and reduction of 38.5% volatile solids (VS) As organic loading rate increased to 3.5 kg VS/day/m−3, accumulation of volatile fatty acid (VFA; 2797 ppm), mainly propionic acid (1617 ppm) was noticeable. The digester turned sour (pH 4.32) with lower biogas (2.5 Ld−1) and methane (30.80%) production, reflecting the case of overloading. Reversal of organic loading rate from 3.5 to 3.0 kg VS/day/m−3 gradually restored the upset anaerobic digester to normal profile in 4 weeks as judged from a gradual increase in biogas (6.5 Ld−1) and methane (58.4%)

Feedback Consolidation Algorithms for ABR Point-to-Multipoint Connections in ATM Networks

ABR traffic management for point-to-multipoint connections controls the source rate to the minimum rate supported by all the branches of the multicast tree. A number of algorithms have been developed for extending ABR congestion avoidance algorithms to perform feedback consolidation at the branch points. This paper discusses various design options and implementation alternatives for the consolidation algorithms, and proposes a number of new algorithms. The performance of the proposed algorithms and the previous algorithms is compared under a variety of conditions. Results indicate that the algorithms we propose eliminate the consolidation noise (caused if the feedback is returned before all branches respond), while exhibiting a fast transient response.Comment: Proceedings of IEEE INFOCOM 1998, March 1998, volume 3, pp. 1004-101

Clinical challenges with excipients in insulin formulations and role of concentrated insulin

Most of the insulin formulations in clinical use contain phenol, meta-cresol or both as excipients. These excipients in insulin preparations provide stability and have antimicrobial properties. However, they are reported to be associated with undesirable side-effects especially localised allergic reactions. Amount of excipients injected per unit dose of insulin is a major determining factor in causation of these reactions. This review discusses the excipients in different insulin formulations available in India with potential of precipitating undesirable effects and the use of concentrated insulins to reduce these complications. To avoid the detrimental effects associated with excipients, removal of preservatives or use of insulin preparations devoid of excipients can be an option. Besides these approaches, one approach that can be considered is the use of concentrated insulin to reduce the volume of insulin dose and thereby the excipients. Concentrated insulins address the high insulin requirements of the growing population of patients with type 2 diabetes who require higher insulin doses. Concentrated insulins help in reduction of dose volume as well as amount of excipients injected per unit dose of insulin. U200 (concentrated r-DNA Human Insulin Premix 30/70-200 IU/ml) can be advantageous with better absorption from smaller quantity injected, lesser variability in absorption, lesser pain and discomfort due to smaller quantity, lesser chances of hypoglycaemia all of which can lead to better patient compliance. Thus, concentrated insulin U200 can be one of the alternatives to prevent/reduce clinical complications with excipients in insulins

Recovery of acerbic anaerobic digester for biogas production from pomegranate shells using organic loading approach

Anaerobic digestion of pomegranate shells was conducted in 25 L bioreactor operating at 35±0.5°C. The digester showed a reasonable amount of biogas (0.71 m3/kg VS fed) and methane (55.7%) with stable pH and acid: alkali profiles when operated at organic loading rate (OLR) from 1.0 to 3.0 kg VS/day/m−3. The reactor exhibited stable performance with methane yield of 0.44 m3/kg VS fed and reduction of 38.5% volatile solids (VS) As organic loading rate increased to 3.5 kg VS/day/m−3, accumulation of volatile fatty acid (VFA; 2797 ppm), mainly propionic acid (1617 ppm) was noticeable. The digester turned sour (pH 4.32) with lower biogas (2.5 Ld−1) and methane (30.80%) production, reflecting the case of overloading. Reversal of organic loading rate from 3.5 to 3.0 kg VS/day/m−3 gradually restored the upset anaerobic digester to normal profile in 4 weeks as judged from a gradual increase in biogas (6.5 Ld−1) and methane (58.4%)