Box Drawings for Learning with Imbalanced Data

The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes. We propose two machine learning algorithms to handle highly imbalanced classification problems. The classifiers constructed by both methods are created as unions of parallel axis rectangles around the positive examples, and thus have the benefit of being interpretable. The first algorithm uses mixed integer programming to optimize a weighted balance between positive and negative class accuracies. Regularization is introduced to improve generalization performance. The second method uses an approximation in order to assist with scalability. Specifically, it follows a \textit{characterize then discriminate} approach, where the positive class is characterized first by boxes, and then each box boundary becomes a separate discriminative classifier. This method has the computational advantages that it can be easily parallelized, and considers only the relevant regions of feature space

Voids in Materials: Adding Functionality during Additive Manufacturing

An often overlooked implication of controlling materials at ever smaller length scales is the control of the inevitable void spaces contained in those materials and components. This miniaturization is widespread and at these small length scales, properties become size dependent, trending toward more ideal material properties. This is corroborated by the extensive selection dispersed phases such as nanotubes and nanometer-scale particles that are available. The technique of additive manufacturing is gaining enormous attention as it offers the ability to make multifunctional components that cannot be made by traditional processing routes. In additive manufacturing, more precise control of material placement presents a unique opportunity to build functionality by the simultaneous control of solid material and voids at multiple length scales. We present an overview of the functionality of voids from the atomic to the millimeter scale, highlighting the current research involving the introduction of voids in additive manufacturing and present future opportunities to incorporate voids by specific additive manufacturing techniques to add functionality

Coated glass microballoons and syntactic foams thereof for environmental cleanup

Titania is of great interest to water purification applications mainly because of its nontoxic nature and its photocatalytic properties. In the presence of ultraviolet (UV) radiation (with energy equal to or greater than its band gap (EG = 3.02 eV) which translates to a wavelength less than or equal to 400 nm) titania exhibits semiconducting properties and creates electron-hole pairs. These electrons and holes give rise to ions, particularly hydroxyl radicals and various superoxides that can be useful in cleaning up a range of organic compounds in their liquid and gaseous phases. We have developed titania coated glass microballoons (GMBs) with high surface area. These hollow GMBs are made of borosilicate glass, have a density of 0.39 g/cm3, and an average diameter of 47µm. The objective is to use syntactic foams made of titania coated GMBs for water purification. This materials system is of great interest because it has the potential of a practical material with broad implications for improving the quality and quantity of drinking water. In this work, we describe the processing by sol-gel of titania-coated glass microballoons (GMBs), followed by making a functional foam for environmental applications by sintering. We will highlight the processing of coated GMBs starting with titanium isopropoxide precursor, the microstructure of the coated GMBs, and some critical materials related issues in environmental cleanup applications

Deep Over-sampling Framework for Classifying Imbalanced Data

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose Deep Over-sampling (DOS), a framework for extending the synthetic over-sampling method to exploit the deep feature space acquired by a convolutional neural network (CNN). Its key feature is an explicit, supervised representation learning, for which the training data presents each raw input sample with a synthetic embedding target in the deep feature space, which is sampled from the linear subspace of in-class neighbors. We implement an iterative process of training the CNN and updating the targets, which induces smaller in-class variance among the embeddings, to increase the discriminative power of the deep representation. We present an empirical study using public benchmarks, which shows that the DOS framework not only counteracts class imbalance better than the existing method, but also improves the performance of the CNN in the standard, balanced settings

Positive-, negative-, and orthogonal-phase-velocity propagation of electromagnetic plane waves in a simply moving medium

Planewave propagation in a simply moving, dielectric-magnetic medium that is isotropic in the co-moving reference frame, is classified into three different categories: positive-, negative-, and orthogonal-phase-velocity (PPV, NPV, and OPV). Calculations from the perspective of an observer located in a non-co-moving reference frame show that, whether the nature of planewave propagation is PPV or NPV (or OPV in the case of nondissipative mediums) depends strongly upon the magnitude and direction of that observer's velocity relative to the medium. PPV propagation is characterized by a positive real wavenumber, NPV propagation by a negative real wavenumber. OPV propagation only occurs for nondissipative mediums, but weakly dissipative mediums can support nearly OPV propagation

Disruption of nNOS-NOS1AP protein-protein interactions suppresses neuropathic pain in mice

Elevated N-methyl-D-aspartate receptor (NMDAR) activity is linked to central sensitization and chronic pain. However, NMDAR antagonists display limited therapeutic potential because of their adverse side effects. Novel approaches targeting the NR2B-PSD95-nNOS complex to disrupt signaling pathways downstream of NMDARs show efficacy in preclinical pain models. Here, we evaluated the involvement of interactions between neuronal nitric oxide synthase (nNOS) and the nitric oxide synthase 1 adaptor protein (NOS1AP) in pronociceptive signaling and neuropathic pain. TAT-GESV, a peptide inhibitor of the nNOS-NOS1AP complex, disrupted the in vitro binding between nNOS and its downstream protein partner NOS1AP but not its upstream protein partner postsynaptic density 95 kDa (PSD95). Putative inactive peptides (TAT-cp4GESV and TAT-GESVΔ1) failed to do so. Only the active peptide protected primary cortical neurons from glutamate/glycine-induced excitotoxicity. TAT-GESV, administered intrathecally (i.t.), suppressed mechanical and cold allodynia induced by either the chemotherapeutic agent paclitaxel or a traumatic nerve injury induced by partial sciatic nerve ligation. TAT-GESV also blocked the paclitaxel-induced phosphorylation at Ser15 of p53, a substrate of p38 MAPK. Finally, TAT-GESV (i.t.) did not induce NMDAR-mediated motor ataxia in the rotarod test and did not alter basal nociceptive thresholds in the radiant heat tail-flick test. These observations support the hypothesis that antiallodynic efficacy of an nNOS-NOS1AP disruptor may result, at least in part, from blockade of p38 MAPK-mediated downstream effects. Our studies demonstrate, for the first time, that disrupting nNOS-NOS1AP protein-protein interactions attenuates mechanistically distinct forms of neuropathic pain without unwanted motor ataxic effects of NMDAR antagonists

THE REPERTORY GRID INTERVIEW OF A DEPRESSED PATIENT

The construct system of a patient of Depression is investigated with the Rank Order Repertory Grid Technique, using principal component analysis to analyse the grid matrix. The investigation helps test clinical judgments and hypotheses concerning the patient and also suggests new directions for further clinical enquiry. This case study demonstrates how the grid technique can make possible a more comprehensive understanding of the cognitive processes of the patient