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

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

Concomitant medication use and clinical outcome of repetitive Transcranial Magnetic Stimulation (rTMS) treatment of Major Depressive Disorder.

BackgroundRepetitive Transcranial Magnetic Stimulation (rTMS) is commonly administered to Major Depressive Disorder (MDD) patients taking psychotropic medications, yet the effects on treatment outcomes remain unknown. We explored how concomitant medication use relates to clinical response to a standard course of rTMS.MethodsMedications were tabulated for 181 MDD patients who underwent a six-week rTMS treatment course. All patients received 10 Hz rTMS administered to left dorsolateral prefrontal cortex (DLPFC), with 1 Hz administered to right DLPFC in patients with inadequate response to and/or intolerance of left-sided stimulation. Primary outcomes were change in Inventory of Depressive Symptomatology Self Report (IDS-SR30) total score after 2, 4, and 6 weeks.ResultsUse of benzodiazepines was associated with less improvement at week 2, whereas use of psychostimulants was associated with greater improvement at week 2 and across 6 weeks. These effects were significant controlling for baseline variables including age, overall symptom severity, and severity of anxiety symptoms. Response rates at week 6 were lower in benzodiazepine users versus non-users (16.4% vs. 35.5%, p = 0.008), and higher in psychostimulant users versus non-users (39.2% vs. 22.0%, p = 0.02).ConclusionsConcomitant medication use may impact rTMS treatment outcome. While the differences reported here could be considered clinically significant, results were not corrected for multiple comparisons and findings should be replicated before clinicians incorporate the evidence into clinical practice. Prospective, hypothesis-based treatment studies will aid in determining causal relationships between medication treatments and outcome

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

A matter of words: NLP for quality evaluation of Wikipedia medical articles

Automatic quality evaluation of Web information is a task with many fields of applications and of great relevance, especially in critical domains like the medical one. We move from the intuition that the quality of content of medical Web documents is affected by features related with the specific domain. First, the usage of a specific vocabulary (Domain Informativeness); then, the adoption of specific codes (like those used in the infoboxes of Wikipedia articles) and the type of document (e.g., historical and technical ones). In this paper, we propose to leverage specific domain features to improve the results of the evaluation of Wikipedia medical articles. In particular, we evaluate the articles adopting an "actionable" model, whose features are related to the content of the articles, so that the model can also directly suggest strategies for improving a given article quality. We rely on Natural Language Processing (NLP) and dictionaries-based techniques in order to extract the bio-medical concepts in a text. We prove the effectiveness of our approach by classifying the medical articles of the Wikipedia Medicine Portal, which have been previously manually labeled by the Wiki Project team. The results of our experiments confirm that, by considering domain-oriented features, it is possible to obtain sensible improvements with respect to existing solutions, mainly for those articles that other approaches have less correctly classified. Other than being interesting by their own, the results call for further research in the area of domain specific features suitable for Web data quality assessment

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

Arranged marriages in people with epilepsy: A pilot knowledge, attitudes and practices survey from India

Introduction: Marriage is a socially challenging barrier in the personal lives of people with epilepsy worldwide. However, it is during arranges marriages, which are common in South Asian communities, that epilepsy is most profoundly stigmatizing. We hypothesized that the felt stigma associated with epilepsy during arranged marriages affects women more frequently and intensely. // Materials and methods: A pilot study in married (n = 38) and unmarried PWE (n = 58) and general public (n = 150) to explore gender-based differences in the stigma associated with epilepsy during arranged marriages. // Results: Majority unmarried PWE (87%) considered arranged marriage as the best way to realize their matrimonial plans. More unmarried women (72%) apprehended problems in adhering to their epilepsy medications regime after marriage (p 0.009) and 50% apprehended victimization in marriage on account of epilepsy (p 0.001). Moreover, 41% of the married women with epilepsy felt that the disclosure had a negative impact on their married life (p 0.047). // Conclusions: South Asian WWE experienced more felt stigma than men before and after arranged marriages and this might impact a number of health related psychosocial outcomes. The lack of past experience with epilepsy was associated with a number of misplaced beliefs about and attitudes towards epilepsy

Horizon effects with surface waves on moving water

Surface waves on a stationary flow of water are considered, in a linear model that includes the surface tension of the fluid. The resulting gravity-capillary waves experience a rich array of horizon effects when propagating against the flow. In some cases three horizons (points where the group velocity of the wave reverses) exist for waves with a single laboratory frequency. Some of these effects are familiar in fluid mechanics under the name of wave blocking, but other aspects, in particular waves with negative co-moving frequency and the Hawking effect, were overlooked until surface waves were investigated as examples of analogue gravity [Sch\"utzhold R and Unruh W G 2002 Phys. Rev. D 66 044019]. A comprehensive presentation of the various horizon effects for gravity-capillary waves is given, with emphasis on the deep water/short wavelength case kh>>1 where many analytical results can be derived. A similarity of the state space of the waves to that of a thermodynamic system is pointed out.Comment: 30 pages, 15 figures. Minor change

The prediction of fatigue using speech as a biosignal

Automatic systems for estimating operator fatigue have application in safety-critical environments. We develop and evaluate a system to detect fatigue from speech recordings collected from speakers kept awake over a 60-hour period. A binary classification system (fatigued/not-fatigued) based on time spent awake showed good discrimination, with 80 % unweighted accuracy using raw features, and 90 % with speaker-normalized features. We describe the data collection, feature analysis, machine learning and cross-validation used in the study. Results are promising for real-world applications in domains such as aerospace, transportation and mining where operators are in regular verbal communication as part of their normal working activities