Clustering Time Series from Mixture Polynomial Models with Discretised Data

Clustering time series is an active research area with applications in many fields. One common feature of time series is the likely presence of outliers. These uncharacteristic data can significantly effect the quality of clusters formed. This paper evaluates a method of over-coming the detrimental effects of outliers. We describe some of the alternative approaches to clustering time series, then specify a particular class of model for experimentation with k-means clustering and a correlation based distance metric. For data derived from this class of model we demonstrate that discretising the data into a binary series of above and below the median improves the clustering when the data has outliers. More specifically, we show that firstly discretisation does not significantly effect the accuracy of the clusters when there are no outliers and secondly it significantly increases the accuracy in the presence of outliers, even when the probability of outlier is very low

Pairwise Confusion for Fine-Grained Visual Classification

Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity. While prior work has addressed intra-class variation using localization and segmentation techniques, inter-class similarity may also affect feature learning and reduce classification performance. In this work, we address this problem using a novel optimization procedure for the end-to-end neural network training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces overfitting by intentionally {introducing confusion} in the activations. With PC regularization, we obtain state-of-the-art performance on six of the most widely-used FGVC datasets and demonstrate improved localization ability. {PC} is easy to implement, does not need excessive hyperparameter tuning during training, and does not add significant overhead during test time.Comment: Camera-Ready version for ECCV 201

Finding the optimal design of a passive microfluidic mixer.

The ability to thoroughly mix two fluids is a fundamental need in microfluidics. While a variety of different microfluidic mixers have been designed by researchers, it remains unknown which (if any) of these mixers are optimal (that is, which designs provide the most thorough mixing with the smallest possible fluidic resistance across the mixer). In this work, we automatically designed and rationally optimized a microfluidic mixer. We accomplished this by first generating a library of thousands of different randomly designed mixers, then using the non-dominated sorting genetic algorithm II (NSGA-II) to optimize the random chips in order to achieve Pareto efficiency. Pareto efficiency is a state of allocation of resources (e.g. driving force) from which it is impossible to reallocate so as to make any one individual criterion better off (e.g. pressure drop) without making at least one individual criterion (e.g. mixing performance) worse off. After 200 generations of evolution, Pareto efficiency was achieved and the Pareto-optimal front was found. We examined designs at the Pareto-optimal front and found several design criteria that enhance the mixing performance of a mixer while minimizing its fluidic resistance; these observations provide new criteria on how to design optimal microfluidic mixers. Additionally, we compared the designs from NSGA-II with some popular microfluidic mixer designs from the literature and found that designs from NSGA-II have lower fluidic resistance with similar mixing performance. As a proof of concept, we fabricated three mixer designs from 200 generations of evolution and one conventional popular mixer design and tested the performance of these four mixers. Using this approach, an optimal design of a passive microfluidic mixer is found and the criteria of designing a passive microfluidic mixer are established

Structure propagation for zero-shot learning

The key of zero-shot learning (ZSL) is how to find the information transfer model for bridging the gap between images and semantic information (texts or attributes). Existing ZSL methods usually construct the compatibility function between images and class labels with the consideration of the relevance on the semantic classes (the manifold structure of semantic classes). However, the relationship of image classes (the manifold structure of image classes) is also very important for the compatibility model construction. It is difficult to capture the relationship among image classes due to unseen classes, so that the manifold structure of image classes often is ignored in ZSL. To complement each other between the manifold structure of image classes and that of semantic classes information, we propose structure propagation (SP) for improving the performance of ZSL for classification. SP can jointly consider the manifold structure of image classes and that of semantic classes for approximating to the intrinsic structure of object classes. Moreover, the SP can describe the constrain condition between the compatibility function and these manifold structures for balancing the influence of the structure propagation iteration. The SP solution provides not only unseen class labels but also the relationship of two manifold structures that encode the positive transfer in structure propagation. Experimental results demonstrate that SP can attain the promising results on the AwA, CUB, Dogs and SUN databases

Interference in hyperbolic space

The interference in a phase space algorithm of Schleich and Wheeler [Nature 326, 574 (1987)] is extended to the hyperbolic space underlying the group SU(1,1). The extension involves introducing the notion of weighted areas. Analytic expressions for the asymptotic forms for overlaps between the eigenstates of the generators of su(1,1) thus obtained are found to be in excellent agreement with the numerical results.[S1050-2947(98)08602-8]

A simple particle-size distribution model for granular materials

© 2018, Canadian Science Publishing. All rights reserved. Particle-size distribution (PSD) is a fundamental soil property that plays an important role in soil classification and soil hydromechanical behaviour. A continuous mathematical model representing the PSD curve facilitates the quantification of particle breakage, which often takes place when granular soils are compressed or sheared. This paper proposes a simple and continuous PSD model for granular soils involving particle breakage. The model has two parameters and is able to represent different types of continuous PSD curves. It is found that one model parameter is closely related to the coefficient of nonuniformity (Cu) and the coefficient of curvature (Cc), while the other represents a characteristic particle diameter. A database of 53 granular soils with 154 varying PSD curves is analyzed to evaluate the performance of the proposed PSD model, as well as that of three other PSD models in the literature. The results show that the proposed model has improved overall performance and captures the typical trends in PSD evolution during particle breakage. In addition, the proposed model is also used for assessing the internal stability of 27 widely graded soils

Executive and perceptual attention play different roles in visual working memory: Evidence from suffix and strategy effects

Four experiments studied the interfering effects of a to-be-ignored ‘stimulus suffix’ on cued recall of feature bindings for a series of objects. When each object was given equal weight (Experiment 1) or rewards favored recent items (Experiments 2 and 4), a recency effect emerged that was selectively reduced by a suffix. The reduction was greater for a ‘plausible’ suffix with features drawn from the same set as the memory items, in which case a feature of the suffix was frequently recalled as an intrusion error. Changing pay-offs to reward recall of early items led to a primacy effect alongside recency (Experiments 3 and 4). Primacy, like recency, was reduced by a suffix and the reduction was greater for a suffix with plausible features, such features often being recalled as intrusion errors. Experiment 4 revealed a trade-off such that increased primacy came at the cost of a reduction in recency. These observations show that priority instructions and recency combine to determine a limited number of items that are the most accessible for immediate recall and yet at the same time the most vulnerable to interference. We interpret this outcome in terms of a labile, limited capacity ‘privileged state’ controlled by both central executive processes and perceptual attention. We suggest further that this privileged state can be usefully interpreted as the focus of attention in the episodic buffer

Evaluation of alternative horizontal well designs for gas production from hydrate deposits in the Shenhu area, South China Sea

Gas hydrate deposits were confirmed in the Shenhu Area, the north slope of South China Sea during a drilling expedition in 2007. Hydrate deposits in the area are distributed in disseminated forms in forams-rich clay sediments with permeable overburden and underburden layers. Production of gas from such a type of hydrate deposits is very challenging. In this study, we develop a numerical approach for investigation of gas production strategies by horizontal wells and preliminary estimation of the production potential based on the limited data that are currently available. Numerical models are built to represent the typical hydrate deposits in the area, including the thickness of the Hydrate-Bearing Layer (HBL), hydrate saturation, water depth, temperature at the sea floor, initial thermal gradient and pressure distribution. The models are used to simulate the different production schemes and well designs. In this paper, production strategies of horizontal well system with combination of depressurization and thermal stimulation are investigated through numerical models. Gas production potential from the deposits and effectiveness of the different production methods are evaluated. The simulation results indicate that with current technology, gas production from Shenhu hydrate deposits may not be economically efficient for all the production strategies we have investigated. Copyright 2010, Society of Petroleum Engineers

Low-Symmetry Rhombohedral GeTe Thermoelectrics

High-symmetry thermoelectric materials usually have the advantage of very high band degeneracy, while low-symmetry thermoelectrics have the advantage of very low lattice thermal conductivity. If the symmetry breaking of band degeneracy is small, both effects may be realized simultaneously. Here we demonstrate this principle in rhombohedral GeTe alloys, having a slightly reduced symmetry from its cubic structure, to realize a record figure of merit (zT ∼ 2.4) at 600 K. This is enabled by the control of rhombohedral distortion in crystal structure for engineering the split low-symmetry bands to be converged and the resultant compositional complexity for simultaneously reducing the lattice thermal conductivity. Device ZT as high as 1.3 in the rhombohedral phase and 1.5 over the entire working temperature range of GeTe alloys make this material the most efficient thermoelectric to date. This work paves the way for exploring low-symmetry materials as efficient thermoelectrics. Thermoelectric materials enable a heat flow to be directly converted to a flow of charge carriers for generating electricity. The crystal structure symmetry is one of the most fundamental parameters determining the properties of a crystalline material including thermoelectrics. The common belief currently held is that high-symmetry materials are usually good for thermoelectrics, leading to great efforts having historically been focused on GeTe alloys in a high-symmetry cubic structure. Here we show a slight reduction of crystal structure symmetry of GeTe alloys from cubic to rhombohedral, enabling a rearrangement in electronic bands for more transporting channels of charge carriers and many imperfections for more blocking centers of heat-energy carriers (phonons). This leads to the discovery of rhombohedral GeTe alloys as the most efficient thermoelectric materials to date, opening new possibilities for low-symmetry thermoelectric materials. Cubic GeTe thermoelectrics have been historically focused on, while this work utilizes a slight symmetry-breaking strategy to converge the split valence bands, to reduce the lattice thermal conductivity and therefore realize a record thermoelectric performance, all enabled in GeTe in a rhombohedral structure. This not only promotes GeTe alloys as excellent materials for thermoelectric power generation below 800 K, but also expands low-symmetry materials as efficient thermoelectrics

What Limits the Rate Capability of Li-S Batteries during Discharge: Charge Transfer or Mass Transfer?

Li-S batteries exhibit poor rate capability under lean electrolyte conditions required for achieving high practical energy densities. In this contribution, we argue that the rate capability of commercially-viable Li-S batteries is mainly limited by mass transfer rather than charge transfer during discharge. We first present experimental evidence showing that the charge-transfer resistance of Li-S batteries and hence the cathode surface covered by Li2S are proportional to the state-of-charge (SoC) and not to the current, directly contradicting previous theories. We further demonstrate that the observed Li-S behaviors for different discharge rates are qualitatively captured by a zero-dimensional Li-S model with transport-limited reaction currents. This is the first Li-S model to also reproduce the characteristic overshoot in voltage at the beginning of charge, suggesting its cause is the increase in charge transfer resistance brought by Li2S precipitation