2,780 research outputs found
Chameleon fields and solar physics
In this article we discuss some aspects of solar physics from the standpoint
of the so-called chameleon fields (i.e. quantum fields, typically scalar, where
the mass is an increasing function of the matter density of the environment).
Firstly, we analyze the effects of a chameleon-induced deviation from standard
gravity just below the surface of the Sun. In particular, we develop solar
models which take into account the presence of the chameleon and we show that
they are inconsistent with the helioseismic data. This inconsistency presents
itself not only with the typical chameleon set-up discussed in the literature
(where the mass scale of the potential is fine-tuned to the meV), but also if
we remove the fine-tuning on the scale of the potential. Secondly, we point out
that, in a model recently considered in the literature (we call this model
"Modified Fujii's Model"), a conceivable interpretation of the solar
oscillations is given by quantum vacuum fluctuations of a chameleon.Comment: 17 pages including figure
Low In solubility and band offsets in the small- -GaO/(GaIn)O system
Based on first-principles calculations, we show that the maximum reachable
concentration in the (GaIn)O alloy in the low-
regime (i.e. In solubility in -GaO) is around 10%. We then
calculate the band alignment at the (100) interface between -GaO
and (GaIn)O at 12%, the nearest computationally treatable
concentration. The alignment is strongly strain-dependent: it is of type-B
staggered when the alloy is epitaxial on GaO, and type-A straddling in
a free-standing superlattice. Our results suggest a limited range of
applicability of low-In-content GaInO alloys.Comment: 3 pages, 3 figure
The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots
Deep networks have brought significant advances in robot perception, enabling
to improve the capabilities of robots in several visual tasks, ranging from
object detection and recognition to pose estimation, semantic scene
segmentation and many others. Still, most approaches typically address visual
tasks in isolation, resulting in overspecialized models which achieve strong
performances in specific applications but work poorly in other (often related)
tasks. This is clearly sub-optimal for a robot which is often required to
perform simultaneously multiple visual recognition tasks in order to properly
act and interact with the environment. This problem is exacerbated by the
limited computational and memory resources typically available onboard to a
robotic platform. The problem of learning flexible models which can handle
multiple tasks in a lightweight manner has recently gained attention in the
computer vision community and benchmarks supporting this research have been
proposed. In this work we study this problem in the robot vision context,
proposing a new benchmark, the RGB-D Triathlon, and evaluating state of the art
algorithms in this novel challenging scenario. We also define a new evaluation
protocol, better suited to the robot vision setting. Results shed light on the
strengths and weaknesses of existing approaches and on open issues, suggesting
directions for future research.Comment: This work has been submitted to IROS/RAL 201
Best Sources Forward: Domain Generalization through Source-Specific Nets
A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions. The problem has been formalized as a covariate shift among the probability distributions generating the training data (source) and the test data (target) and several domain adaptation methods have been proposed to address this issue. While these approaches have considered the single source-single target scenario, it is plausible to have multiple sources and require adaptation to any possible target domain. This last scenario, named Domain Generalization (DG), is the focus of our work. Differently from previous DG methods which learn domain invariant representations from source data, we design a deep network with multiple domain-specific classifiers, each associated to a source domain. At test time we estimate the probabilities that a target sample belongs to each source domain and exploit them to optimally fuse the classifiers predictions. To further improve the generalization ability of our model, we also introduced a domain agnostic component supporting the final classifier. Experiments on two public benchmarks demonstrate the power of our approach
AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs
The ability to categorize is a cornerstone of visual intelligence, and a key
functionality for artificial, autonomous visual machines. This problem will
never be solved without algorithms able to adapt and generalize across visual
domains. Within the context of domain adaptation and generalization, this paper
focuses on the predictive domain adaptation scenario, namely the case where no
target data are available and the system has to learn to generalize from
annotated source images plus unlabeled samples with associated metadata from
auxiliary domains. Our contributionis the first deep architecture that tackles
predictive domainadaptation, able to leverage over the information broughtby
the auxiliary domains through a graph. Moreover, we present a simple yet
effective strategy that allows us to take advantage of the incoming target data
at test time, in a continuous domain adaptation scenario. Experiments on three
benchmark databases support the value of our approach.Comment: CVPR 2019 (oral
Robust Place Categorization With Deep Domain Generalization
Traditional place categorization approaches in robot vision assume that training and test images have similar visual appearance. Therefore, any seasonal, illumination, and environmental changes typically lead to severe degradation in performance. To cope with this problem, recent works have been proposed to adopt domain adaptation techniques. While effective, these methods assume that some prior information about the scenario where the robot will operate is available at training time. Unfortunately, in many cases, this assumption does not hold, as we often do not know where a robot will be deployed. To overcome this issue, in this paper, we present an approach that aims at learning classification models able to generalize to unseen scenarios. Specifically, we propose a novel deep learning framework for domain generalization. Our method develops from the intuition that, given a set of different classification models associated to known domains (e.g., corresponding to multiple environments, robots), the best model for a new sample in the novel domain can be computed directly at test time by optimally combining the known models. To implement our idea, we exploit recent advances in deep domain adaptation and design a convolutional neural network architecture with novel layers performing a weighted version of batch normalization. Our experiments, conducted on three common datasets for robot place categorization, confirm the validity of our contribution
Learning Deep NBNN Representations for Robust Place Categorization
This paper presents an approach for semantic place categorization using data
obtained from RGB cameras. Previous studies on visual place recognition and
classification have shown that, by considering features derived from
pre-trained Convolutional Neural Networks (CNNs) in combination with part-based
classification models, high recognition accuracy can be achieved, even in
presence of occlusions and severe viewpoint changes. Inspired by these works,
we propose to exploit local deep representations, representing images as set of
regions applying a Na\"{i}ve Bayes Nearest Neighbor (NBNN) model for image
classification. As opposed to previous methods where CNNs are merely used as
feature extractors, our approach seamlessly integrates the NBNN model into a
fully-convolutional neural network. Experimental results show that the proposed
algorithm outperforms previous methods based on pre-trained CNN models and
that, when employed in challenging robot place recognition tasks, it is robust
to occlusions, environmental and sensor changes
Assessment Methods for Innovative Operational Measures and Technologies for Intermodal Freight Terminals
The topic of freight transport by rail, is a complex theme and, in recent years, a main issue of European policy. The legislation evolution
and the White Paper 2011 have demonstrated the European intention to re-launch this sector. The challenge is to promote the intermodal
transport system to the detriment of road freight transport. In this context, the intermodal freight terminals play a primary role for the
supply chain, they are the connection point between the various transport nodes and the nodal points where the freight are handled,
stored and transferred between different modes to final customer. To achieve the purpose, proposed by the EC, are necessary the
performances improvement of existing intermodal freight terminals and the development of innovative intermodal freight terminals.
Many terminal performances improvement is have been proposed and sometime experimented. They are based both on operational
measures (e.g. horizontal and parallel handling, faster and fully direct handling) and on innovative technologies (e.g. automatic system
for horizontal and parallel handling, automated gate for data exchange) inside the terminals, with often-contradictory results. The
research work described in this paper (developed within the EU project Capacity4Rail) focusses on the assessment of effects that these
innovations can have in the intermodal freight terminals. The innovative operational measures and technologies have been combined in
different scenarios, to be evaluated by a methodological approach including to other an analytical methods and simulation models. The
output of this assessment method are key performance indicators (KPI) setup according to terminals typologies the proposals and related
to different aspects (e.g. management, operation and organization. In the present work suitable KPIs (e.g. total/partial transit times) for to
evaluate have been applied. Finally, in addition to methodological framework illustrated, a real case of study will be illustrated: the
intermodal rail-road freight terminal Munich-Riem (Germany)
Kitting in the Wild through Online Domain Adaptation
Technological developments call for increasing perception and action capabilities of robots. Among other skills, vision systems that can adapt to any possible change in the working conditions are needed. Since these conditions are unpredictable, we need benchmarks which allow to assess the generalization and robustness capabilities of our visual recognition algorithms. In this work we focus on robotic kitting in unconstrained scenarios. As a first contribution, we present a new visual dataset for the kitting task. Differently from standard object recognition datasets, we provide images of the same objects acquired under various conditions where camera, illumination and background are changed. This novel dataset allows for testing the robustness of robot visual recognition algorithms to a series of different domain shifts both in isolation and unified. Our second contribution is a novel online adaptation algorithm for deep models, based on batch-normalization layers, which allows to continuously adapt a model to the current working conditions. Differently from standard domain adaptation algorithms, it does not require any image from the target domain at training time. We benchmark the performance of the algorithm on the proposed dataset, showing its capability to fill the gap between the performances of a standard architecture and its counterpart adapted offline to the given target domain
Helioseismology and screening of nuclear reactions in the Sun
We show that models for screening of nuclear reactions in the Sun can be tested by means of helioseismology. As well known, solar models using the weak screening factors are in agreement with data. We find that the solar model calculated with the anti screening factors of Tsytovitch is not consistent with helioseismology, both for the sound speed profile and for the depth of the convective envelope. Moreover, the difference between the no-screening and weak screening model is significant in comparison with helioseismic uncertainty. In other words, the existence of screening can be proved by means of helioseismology
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