Towards learning free naive bayes nearest neighbor-based domain adaptation

As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed that by casting the problem into the image-to-class recognition framework, the domain adaptation problem is significantly alleviated [23]. Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow. Experiments on standard benchmarks used in the literature show that our approach (a) is competitive with the current state of the art on small scale problems, and (b) achieves the current state of the art as the number of classes and sources grows, with minimal computational requirements. © Springer International Publishing Switzerland 2015

Multi-objective optimization of RF circuit blocks via surrogate models and NBI and SPEA2 methods

Multi-objective optimization techniques can be categorized globally into deterministic and evolutionary methods. Examples of such methods are the Normal Boundary Intersection (NBI) method and the Strength Pareto Evolutionary Algorithm (SPEA2), respectively. With both methods one explores trade-offs between conflicting performances. Surrogate models can replace expensive circuit simulations so enabling faster computation of circuit performances. As surrogate models of behavioral parameters and performance outcomes, we consider look-up tables with interpolation and Neural Network models

Keeping a Foot in the Door: Neoliberal Ideology in Subjects Who Opt Out of a Corporate Career

It is well researched that ideals of freedom and self-fulfillment through work are perpetuated by the neoliberal ideology that permeates subjective reasoning, meaning-making, and everyday practices. While these ideals may seem attractive and enticing to the subject, their pursuit usually leads to less secure working contracts and conditions. Thus, organizations can continue to enforce economic principles and increase pressure on workers while, at the same time, the mechanisms of liberalization and individualization make subjects — not organizations — responsible for their own success and existential survival, and for creating meaningful and happy lives. Striving to design and optimize their own personal and professional trajectories, subjects perpetuate these ideals and thus adopt the socially-validated view that opting out of a salaried job in favor of self-employment is the zenith of self-actualization. Existing research on the phenomenon of opting out emphasizes gender differences around this issue, i.e., women opt out to stay home, whereas men — if their role is even considered — do so to enhance their careers. However, this research is sparse and lacks a contextualized understanding of the phenomenon, such that we still know very little about who opts out and why. Following an explorative approach, this study looks at 20 single-case stories of subjects who opted out from corporate career tracks. We find that the decision to opt out worked out well for diligent subjects with high self-esteem, who already had successful career trajectories and who — independently of gender — viewed it as an act to free oneself from, and a fundamental critique of, corporate working conditions and values. We analyze this finding through the theoretical lens of critical psychology in order to shed light on the phenomenon of opting out and the extent to which individuals can pursue meaningfulness in life and work within the scope of neoliberal conditions, i.e., in contexts where liberal principles remain applicable to the living and working conditions achieved by subjects after they have left the corporate world

Solving Visual Madlibs with Multiple Cues

This paper focuses on answering fill-in-the-blank style multiple choice questions from the Visual Madlibs dataset. Previous approaches to Visual Question Answering (VQA) have mainly used generic image features from networks trained on the ImageNet dataset, despite the wide scope of questions. In contrast, our approach employs features derived from networks trained for specialized tasks of scene classification, person activity prediction, and person and object attribute prediction. We also present a method for selecting sub-regions of an image that are relevant for evaluating the appropriateness of a putative answer. Visual features are computed both from the whole image and from local regions, while sentences are mapped to a common space using a simple normalized canonical correlation analysis (CCA) model. Our results show a significant improvement over the previous state of the art, and indicate that answering different question types benefits from examining a variety of image cues and carefully choosing informative image sub-regions

An evaluation of KL-optimum designs to discriminate between rival copula models = Una valutazione della capacita del disegno KL-ottimo di discriminare tra modelli copula rivali

Nel corso degli ultimi anni il problema di discriminare tra modelli rivaliha prodotto una grande quantit`a di ricerche. A seconda della tipologia di modellirivali (annidati, non annidati, lineari o non lineari), diversi criteri sono stati pro-posti con l\u2019obiettivo di selezionare il disegno ottimo per la discriminazione. Tra ipi`u noti ricordiamo i criteri Ds-, T- e KL-. Per quanto ci consta, in letteratura nonesistono studi relativi alla valutazione della loro effettiva capacit`a discriminatoria.In questo lavoro, attraverso uno studio di simulazione in cui abbiamo applicato iltest del rapporto di verosimiglianza per modelli non annidati, abbiamo analizzatole prestazioni del disegno KL-ottimo per discriminare tra modelli bivariati la cuistruttura di dipendenza`e descritta attraverso una funzione copula.The problem of model discrimination has prompted a great amount ofresearch over last years. According to the specific characteristics of the rival models(nested, non-nested, linear or not) different optimum criteria have been proposedto select design points with the aim to discriminate between rival models. Ds-, T-and KL-criteria are the most known. Up to our knowledge, in the literature there isnot any study to evaluate the performance of these discrimination criteria. In thiswork, via a simulation study and focusing on rival copula models, we analyze theperformance of the KL-optimum design applying the likelihood ratio test for non-nested models

Optimal design to discriminate between rival copula models for a bivariate binary response

We consider a bivariate logistic model for a binary response, and we assume that two rival dependence structures are possible. Copula functions are very useful tools to model different kinds of dependence with arbitrary marginal distributions. We consider Clayton and Gumbel copulae as competing association models. The focus is on applications in testing a new drug looking at both efficacy and toxicity outcomes. In this context, one of the main goals is to find the dose which maximizes the probability of efficacy without toxicity, herein called P-optimal dose. If the P-optimal dose changes under the two rival copulae, then it is relevant to identify the proper association model. To this aim, we propose a criterion (called PKL) which enables us to find the optimal doses to discriminate between the rival copulae, subject to a constraint that protects patients against dangerous doses. Furthermore, by applying the likelihood ratio test for non-nested models, via a simulation study we confirm that the PKL-optimal design is really able to discriminate between the rival copulae

Robust transfer function identification via an enhanced magnitude vector fitting algorithm

The study introduces an enhanced version of the magnitude vector fitting (magVF) algorithm, a robust procedure for the identification of a transfer function from magnitude frequency domain data. The approach is based on the rational approximation of the magnitude square function with enforcement of symmetric poles and zeros, followed by the elimination of poles and zeros located in the right half-plane. The obtained transfer function is stable and of minimum-phase shift type. Robustness and accuracy of the basic magVF algorithm are enhanced by enforcing that the magnitude square rational function is non-negative definite and by introducing a new method to remove purely imaginary conjugate poles from the approximation. Practical application of the proposed approach is demonstrated for measured transformer responses and transmission line propagation functions

High-J CO line emission from young stellar objects: from ISO to FIRST

we present the CO pure rotational spectrum at high J (Jup14) obtained with the Long Wavelength Spectrometer (LWS) on board of the ISO satellite towards molecular outflows exciting sources in nearby star formation regions. The physical conditions, derived using an LVG model for the line emission, indicate the presence of warm and dense gas, probably shock excited. The model fits show that often the bulk of this CO emission is expected in the spectral range that will be covered by FIRST, indicating the potentiality of this satellite to trace the warm component of gas emission in young stellar objects

Shedding Light on Diatom Photonics by means of Digital Holography

Diatoms are among the dominant phytoplankters in the worl's ocean, and their external silica investments, resembling artificial photonics crystal, are expected to play an active role in light manipulation. Digital holography allowed studying the interaction with light of Coscinodiscus wailesii cell wall reconstructing the light confinement inside the cell cytoplasm, condition that is hardly accessible via standard microscopy. The full characterization of the propagated beam, in terms of quantitative phase and intensity, removed a long-standing ambiguity about the origin of the light. The data were discussed in the light of living cell behavior in response to their environment