234 research outputs found
Heteroclinic Orbits for a Discrete Pendulum Equation
About twenty years ago, Rabinowitz showed firstly that there exist
heteroclinic orbits of autonomous Hamiltonian system joining two equilibria. A
special case of autonomous Hamiltonian system is the classical pendulum
equation. The phase plane analysis of pendulum equation shows the existence of
heteroclinic orbits joining two equilibria, which coincide with the result of
Rabinowitz. However, the phase plane of discrete pendulum equation is similar
to that of the classical pendulum equation, which suggests the existence of
heteroclinic orbits for discrete pendulum equation also. By using variational
method and delicate analysis technique, we show that there indeed exist
heteroclinic orbits of discrete pendulum equation joining every two adjacent
points of
Using MicroPET Imaging in Quantitative Verification of Acupuncture Effect in Ischemia Stroke Treatment
While acupuncture has survived several thousand years’ evolution of medical practice, its function still remains as a myth from the view point of modern medicine. Our goal in this paper is to quantitatively understand the function of acupuncture in ischemia stroke treatment. We carried out a comparative study using the Sprague Dawley rat animal model. We induced the focal cerebral ischemia in the rats using the middle cerebral artery occlusion (MCAO) procedure. For each rat from the real acupuncture group (n = 40), sham acupoint treatment group (n = 54), and blank control group (n = 16), we acquired 3-D FDG-microPET images at baseline, after MCAO, and after treatment (i.e., real acupuncture, sham acupoint treatment, or resting according to the group assignment), respectively. After verifying that the injured area is in the right hemisphere of the cerebral cortex in the brain by using magnetic resonance imaging(MRI) and triphenyl tetrazolium cchloride (TTC)-staining, we directly compared the glucose metabolism in the right hemisphere of each rat. We carried out t-test and permutation test on the image data. Both tests demonstrated that acupuncture had a more positive effect than non-acupoint stimulus and blank control (P < 0.025) in increasing the glucose metabolic level in the stroke-injured area in the brain, while there was no statistically significant difference between non-acupoint stimulus and blank control (P>0.15). The immediate positive effect of acupuncture over sham acupoint treatment and blank control is verified using our experiments. The long-term benefit of acupuncture needs to be further studied
A Model for the Mixed-Design of Data-Intensive and Control-Oriented Embedded Systems
This paper presents a model and its semantics for the design of embedded systems that contain data-intensive parts such as multimedia applications, and require adaptivity w.r.t. criteria such as platform resources or quality of service (QoS). The proposed solution relies on a combination of: i) the repetitive model of computation dedicated to the design of high-performance embedded systems and ii) reactive control features based on finite state machines and modes. It is defined within a framework, called Gaspard, that implements automatic transformations that lead to various target languages, e.g., synchronous languages, SystemC, VHDL. The new model offers the adequate expressive power to describe complex behaviors of high-performance embedded systems. It also reconciles execution models dedicated to regular computations and control-oriented models that rather lead to irregular computations
CHITNet: A Complementary to Harmonious Information Transfer Network for Infrared and Visible Image Fusion
Current infrared and visible image fusion (IVIF) methods go to great lengths
to excavate complementary features and design complex fusion strategies, which
is extremely challenging. To this end, we rethink the IVIF outside the box,
proposing a complementary to harmonious information transfer network (CHITNet).
It reasonably transfers complementary information into harmonious one, which
integrates both the shared and complementary features from two modalities.
Specifically, to skillfully sidestep aggregating complementary information in
IVIF, we design a mutual information transfer (MIT) module to mutually
represent features from two modalities, roughly transferring complementary
information into harmonious one. Then, a harmonious information acquisition
supervised by source image (HIASSI) module is devised to further ensure the
complementary to harmonious information transfer after MIT. Meanwhile, we also
propose a structure information preservation (SIP) module to guarantee that the
edge structure information of the source images can be transferred to the
fusion results. Moreover, a mutual promotion training paradigm (MPTP) with
interaction loss is adopted to facilitate better collaboration among MIT,
HIASSI and SIP. In this way, the proposed method is able to generate fused
images with higher qualities. Extensive experimental results demonstrate the
superiority of our CHITNet over state-of-the-art algorithms in terms of visual
quality and quantitative evaluations
Assumption Generation for the Verification of Learning-Enabled Autonomous Systems
Providing safety guarantees for autonomous systems is difficult as these
systems operate in complex environments that require the use of
learning-enabled components, such as deep neural networks (DNNs) for visual
perception. DNNs are hard to analyze due to their size (they can have thousands
or millions of parameters), lack of formal specifications (DNNs are typically
learnt from labeled data, in the absence of any formal requirements), and
sensitivity to small changes in the environment. We present an assume-guarantee
style compositional approach for the formal verification of system-level safety
properties of such autonomous systems. Our insight is that we can analyze the
system in the absence of the DNN perception components by automatically
synthesizing assumptions on the DNN behaviour that guarantee the satisfaction
of the required safety properties. The synthesized assumptions are the weakest
in the sense that they characterize the output sequences of all the possible
DNNs that, plugged into the autonomous system, guarantee the required safety
properties. The assumptions can be leveraged as run-time monitors over a
deployed DNN to guarantee the safety of the overall system; they can also be
mined to extract local specifications for use during training and testing of
DNNs. We illustrate our approach on a case study taken from the autonomous
airplanes domain that uses a complex DNN for perception
Model Transformations from a Data Parallel Formalism towards Synchronous Languages
The increasing complexity of embedded system designs calls for high-level specification formalisms and for automated transformations towards lower-level descriptions. In this report, a metamodel and a transformation chain are defined from a high-level modeling framework, Gaspard, for data-parallel systems towards a formalism of synchronous equations. These equations are translated in synchronous data-flow languages, such as Lustre, Lucid synchrone and Signal, which provide designers with formal techniques and tools for validation. In order to benefit from the methodological advantages of re-usability and platform-independence, a Model-Driven Engineering approach is applied
Adaptivity in High-Performance Embedded Systems: a Reactive Control Model for Reliable and Flexible Design
International audienceSystem adaptivity is increasingly demanded in high-performance embedded systems, particularly in multimedia System-on-Chip (SoC), due to growing Quality of Service requirements. This paper presents a reactive control model that has been introduced in Gaspard, our framework dedicated to SoC hardware/software co-design. This model aims at expressing adaptivity as well as reconfigurability in systems performing data-intensive computations. It is generic enough to be used for description in the different parts of an embedded system, e.g. specification of how different data-intensive algorithms can be chosen according to some computation modes at the functional level; expression of how hardware components can be selected via the usage of a library of Intellectual Properties (IPs) according to execution performances. The transformation of this model towards synchronous languages is also presented, in order to allow an automatic code generation usable for formal verification, based of techniques such as model checking and controller synthesis as illustrated in the paper. This work, based on Model-Driven Engineering and the standard UML MARTE profile, has been implemented in Gaspard
Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-Identification
In visible-infrared video person re-identification (re-ID), extracting
features not affected by complex scenes (such as modality, camera views,
pedestrian pose, background, etc.) changes, and mining and utilizing motion
information are the keys to solving cross-modal pedestrian identity matching.
To this end, the paper proposes a new visible-infrared video person re-ID
method from a novel perspective, i.e., adversarial self-attack defense and
spatial-temporal relation mining. In this work, the changes of views, posture,
background and modal discrepancy are considered as the main factors that cause
the perturbations of person identity features. Such interference information
contained in the training samples is used as an adversarial perturbation. It
performs adversarial attacks on the re-ID model during the training to make the
model more robust to these unfavorable factors. The attack from the adversarial
perturbation is introduced by activating the interference information contained
in the input samples without generating adversarial samples, and it can be thus
called adversarial self-attack. This design allows adversarial attack and
defense to be integrated into one framework. This paper further proposes a
spatial-temporal information-guided feature representation network to use the
information in video sequences. The network cannot only extract the information
contained in the video-frame sequences but also use the relation of the local
information in space to guide the network to extract more robust features. The
proposed method exhibits compelling performance on large-scale cross-modality
video datasets. The source code of the proposed method will be released at
https://github.com/lhf12278/xxx.Comment: 11 pages,8 figure
Generation and Recombination for Multifocus Image Fusion with Free Number of Inputs
Multifocus image fusion is an effective way to overcome the limitation of
optical lenses. Many existing methods obtain fused results by generating
decision maps. However, such methods often assume that the focused areas of the
two source images are complementary, making it impossible to achieve
simultaneous fusion of multiple images. Additionally, the existing methods
ignore the impact of hard pixels on fusion performance, limiting the visual
quality improvement of fusion image. To address these issues, a combining
generation and recombination model, termed as GRFusion, is proposed. In
GRFusion, focus property detection of each source image can be implemented
independently, enabling simultaneous fusion of multiple source images and
avoiding information loss caused by alternating fusion. This makes GRFusion
free from the number of inputs. To distinguish the hard pixels from the source
images, we achieve the determination of hard pixels by considering the
inconsistency among the detection results of focus areas in source images.
Furthermore, a multi-directional gradient embedding method for generating full
focus images is proposed. Subsequently, a hard-pixel-guided recombination
mechanism for constructing fused result is devised, effectively integrating the
complementary advantages of feature reconstruction-based method and focused
pixel recombination-based method. Extensive experimental results demonstrate
the effectiveness and the superiority of the proposed method.The source code
will be released on https://github.com/xxx/xxx
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