Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders

Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework. Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training

Research in interactive scene analysis

An interactive scene interpretation system (ISIS) was developed as a tool for constructing and experimenting with man-machine and automatic scene analysis methods tailored for particular image domains. A recently developed region analysis subsystem based on the paradigm of Brice and Fennema is described. Using this subsystem a series of experiments was conducted to determine good criteria for initially partitioning a scene into atomic regions and for merging these regions into a final partition of the scene along object boundaries. Semantic (problem-dependent) knowledge is essential for complete, correct partitions of complex real-world scenes. An interactive approach to semantic scene segmentation was developed and demonstrated on both landscape and indoor scenes. This approach provides a reasonable methodology for segmenting scenes that cannot be processed completely automatically, and is a promising basis for a future automatic system. A program is described that can automatically generate strategies for finding specific objects in a scene based on manually designated pictorial examples

Blind Normalization of Speech From Different Channels

We show how to construct a channel-independent representation of speech that has propagated through a noisy reverberant channel. This is done by blindly rescaling the cepstral time series by a non-linear function, with the form of this scale function being determined by previously encountered cepstra from that channel. The rescaled form of the time series is an invariant property of it in the following sense: it is unaffected if the time series is transformed by any time-independent invertible distortion. Because a linear channel with stationary noise and impulse response transforms cepstra in this way, the new technique can be used to remove the channel dependence of a cepstral time series. In experiments, the method achieved greater channel-independence than cepstral mean normalization, and it was comparable to the combination of cepstral mean normalization and spectral subtraction, despite the fact that no measurements of channel noise or reverberations were required (unlike spectral subtraction).Comment: 25 pages, 7 figure

Cohesion, team mental models, and collective efficacy: Towards an integrated framework of team dynamics in sport

A nomological network on team dynamics in sports consisting of a multi-framework perspective is introduced and tested. The aim was to explore the interrelationship among cohesion, team mental models (TMM), collective-efficacy (CE), and perceived performance potential (PPP). Three hundred and forty college-aged soccer players representing 17 different teams (8 female and 9 male) participated in the study. They responded to surveys on team cohesion, TMM, CE and PPP. Results are congruent with the theoretical conceptualization of a parsimonious view of team dynamics in sports. Specifically, cohesion was found to be an exogenous variable predicting both TMM and CE beliefs. TMM and CE were correlated and predicted PPP, which in turn accounted for 59% of the variance of objective performance scores as measured by teams’ season record. From a theoretical standpoint, findings resulted in a parsimonious view of team dynamics, which may represent an initial step towards clarifying the epistemological roots and nomological network of various team-level properties. From an applied standpoint, results suggest that team expertise starts with the establishment of team cohesion. Following the establishment of cohesiveness, teammates are able to advance team-related schemas and a collective sense of confidence. Limitations and key directions for future research are outlined

Rational approximation and arithmetic progressions

A reasonably complete theory of the approximation of an irrational by rational fractions whose numerators and denominators lie in prescribed arithmetic progressions is developed in this paper. Results are both, on the one hand, from a metrical and a non-metrical point of view and, on the other hand, from an asymptotic and also a uniform point of view. The principal novelty is a Khintchine type theorem for uniform approximation in this context. Some applications of this theory are also discussed

Design of the ILC RTML extraction lines

The ILC [1] Damping Ring to the Main Linac beamline (RTML) contains three extraction lines (EL). Each EL can be used both for an emergency abort dumping of the beam and tune-up continual train-by-train extraction. Two of the extraction lines are located downstream of the first and second stages of the RTML bunch compressor, and must accept both compressed and uncompressed beam with energy spreads of 2.5% and 0.15%, respectively. In this paper we report on an optics design that allowed minimizing the length of the extraction lines while offsetting the beam dumps from the main line by the distance required for acceptable radiation levels in the service tunnel. The proposed extraction lines can accommodate beams with different energy spreads while at the same time providing the beam size acceptable for the aluminum dump window

Kepler Presearch Data Conditioning I - Architecture and Algorithms for Error Correction in Kepler Light Curves

Kepler provides light curves of 156,000 stars with unprecedented precision. However, the raw data as they come from the spacecraft contain significant systematic and stochastic errors. These errors, which include discontinuities, systematic trends, and outliers, obscure the astrophysical signals in the light curves. To correct these errors is the task of the Presearch Data Conditioning (PDC) module of the Kepler data analysis pipeline. The original version of PDC in Kepler did not meet the extremely high performance requirements for the detection of miniscule planet transits or highly accurate analysis of stellar activity and rotation. One particular deficiency was that astrophysical features were often removed as a side-effect to removal of errors. In this paper we introduce the completely new and significantly improved version of PDC which was implemented in Kepler SOC 8.0. This new PDC version, which utilizes a Bayesian approach for removal of systematics, reliably corrects errors in the light curves while at the same time preserving planet transits and other astrophysically interesting signals. We describe the architecture and the algorithms of this new PDC module, show typical errors encountered in Kepler data, and illustrate the corrections using real light curve examples.Comment: Submitted to PASP. Also see companion paper "Kepler Presearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction" by Jeff C. Smith et a

Social Effects in Science: Modelling Agents for a Better Scientific Practice

Science is a fundamental human activity and we trust its results because it has several error-correcting mechanisms. Its is subject to experimental tests that are replicated by independent parts. Given the huge amount of information available, scientists have to rely on the reports of others. This makes it possible for social effects to influence the scientific community. Here, an Opinion Dynamics agent model is proposed to describe this situation. The influence of Nature through experiments is described as an external field that acts on the experimental agents. We will see that the retirement of old scientists can be fundamental in the acceptance of a new theory. We will also investigate the interplay between social influence and observations. This will allow us to gain insight in the problem of when social effects can have negligible effects in the conclusions of a scientific community and when we should worry about them.Comment: 14 pages, 5 figure