5,291 research outputs found
Financial Computational Intelligence
Artificial intelligence decision support system is always a popular topic in providing the human with an optimized decision recommendation when operating under uncertainty in complex environments. The particular focus of our discussion is to compare different methods of artificial intelligence decision support systems in the investment domain – the goal of investment decision-making is to select an optimal portfolio that satisfies the investor’s objective, or, in other words, to maximize the investment returns under the constraints given by investors. In this study we apply several artificial intelligence systems like Influence Diagram (a special type of Bayesian network), Decision Tree and Neural Network to get experimental comparison analysis to help users to intelligently select the best portfoliArtificial intelligence, neural network, decision tree, bayesian network
Towards patient-specific cardiovascular modeling system using the immersed boundary technique
<p>Abstract</p> <p>Background</p> <p>Previous research shows that the flow dynamics in the left ventricle (LV) reveal important information about cardiac health. This information can be used in early diagnosis of patients with potential heart problems. The current study introduces a patient-specific cardiovascular-modelling system (CMS) which simulates the flow dynamics in the LV to facilitate physicians in early diagnosis of patients before heart failure.</p> <p>Methods</p> <p>The proposed system will identify possible disease conditions and facilitates early diagnosis through hybrid computational fluid dynamics (CFD) simulation and time-resolved magnetic resonance imaging (4-D MRI). The simulation is based on the 3-D heart model, which can simultaneously compute fluid and elastic boundary motions using the immersed boundary method. At this preliminary stage, the 4-D MRI is used to provide an appropriate comparison. This allows flexible investigation of the flow features in the ventricles and their responses.</p> <p>Results</p> <p>The results simulate various flow rates and kinetic energy in the diastole and systole phases, demonstrating the feasibility of capturing some of the important characteristics of the heart during different phases. However, some discrepancies exist in the pulmonary vein and aorta flow rate between the numerical and experimental data. Further studies are essential to investigate and solve the remaining problems before using the data in clinical diagnostics.</p> <p>Conclusions</p> <p>The results show that by using a simple reservoir pressure boundary condition (RPBC), we are able to capture some essential variations found in the clinical data. Our approach establishes a first-step framework of a practical patient-specific CMS, which comprises a 3-D CFD model (without involving actual hemodynamic data yet) to simulate the heart and the 4-D PC-MRI system. At this stage, the 4-D PC-MRI system is used for verification purpose rather than input. This brings us closer to our goal of developing a practical patient-specific CMS, which will be pursued next. We anticipate that in the future, this hybrid system can potentially identify possible disease conditions in LV through comprehensive analysis and facilitates physicians in early diagnosis of probable cardiac problems.</p
Integrin-mediated membrane blebbing is dependent on the NHE1 and NCX1 activities.
Integrin-mediated signal transduction and membrane blebbing have been well studied to modulate cell adhesion, spreading and migration^1-6^. However, the relationship between membrane blebbing and integrin signaling has not been explored. Here we show that integrin-ligand interaction induces membrane blebbing and membrane permeability change. We found that sodium-proton exchanger 1 (NHE1) and sodium-calcium exchanger 1 (NCX1) are located in the membrane blebbing sites and inhibition of NHE1 disrupts membrane blebbing and decreases membrane permeability change. However, inhibition of NCX1 enhances cell blebbing to cause cell swelling which is correlated with an intracellular sodium accumulation induced by NHE17. These data suggest that sodium influx induced by NHE1 is a driving force for membrane blebbing growth, while sodium efflux induced by NCX1 in a reverse mode causes membrane blebbing retraction. Together, these data reveal a novel function of NHE1 and NCX1 in membrane permeability change and blebbing and provide the link for integrin signaling and membrane blebbing
Design Distribution and Evaluation Model for Collaborative Design Chain
A collaborative design chain incorporates the different design activities performed by various design teams that may be located at different geographical locations. In a collaborative design chain, the different parts of a product can be designed by different design teams in a collaborative way. There exist different ways for distributing the different parts to the multiple design teams. If different ways are used for distributing the different part, the time for completing the design and the final functions of the product may vary. In this research, a design evaluation model for evaluating the collaborative design chain is presented. The presented new model is aimed at finding the best way for distributing the different parts to the suitable design teams such that the designed functional value of the product can be maximized. Also, the design cost composed of design operation cost and design communication cost in collaborative design is minimized. An optimized design distribution and evaluation model is presented by maximizing the total design value which is defined as the designed functional value minus the design operation cost and the design communication cost. Implementation and test results are presented
A Hint-Based Random Access Protocol for mMTC in 5G Mobile Network
With the increasing popularity of machine-type communication (MTC) devices, several new challenges are encountered by the legacy long term evolution (LTE) system. One critical issue is that a massive number of MTC devices trying to conduct random access procedures may cause significant collisions and long delays. In this work, we present a new random access mechanism by splitting the contention-based preambles in LTE into two logically disjoint parts, one for the user equipment (UE) being paged and the other for the UEs not being paged. Since the IDs of paged UEs are known by the base station, a novel hash-based random access, which we call hint, is possible. The main idea is to pre-allocate preambles to paged UEs in a contention-free manner and confines non-paged UEs to contend in a separate region. We further build a mathematical model to find the optimal ratio of pre-allocated preambles. Extensive simulations are conducted to validate our results
Interpretations of Domain Adaptations via Layer Variational Analysis
Transfer learning is known to perform efficiently in many applications
empirically, yet limited literature reports the mechanism behind the scene.
This study establishes both formal derivations and heuristic analysis to
formulate the theory of transfer learning in deep learning. Our framework
utilizing layer variational analysis proves that the success of transfer
learning can be guaranteed with corresponding data conditions. Moreover, our
theoretical calculation yields intuitive interpretations towards the knowledge
transfer process. Subsequently, an alternative method for network-based
transfer learning is derived. The method shows an increase in efficiency and
accuracy for domain adaptation. It is particularly advantageous when new domain
data is sufficiently sparse during adaptation. Numerical experiments over
diverse tasks validated our theory and verified that our analytic expression
achieved better performance in domain adaptation than the gradient descent
method.Comment: Published at ICLR 202
INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization
Domain Generalization (DG) aims to learn a generalizable model on the unseen
target domain by only training on the multiple observed source domains.
Although a variety of DG methods have focused on extracting domain-invariant
features, the domain-specific class-relevant features have attracted attention
and been argued to benefit generalization to the unseen target domain. To take
into account the class-relevant domain-specific information, in this paper we
propose an Information theory iNspired diSentanglement and pURification modEl
(INSURE) to explicitly disentangle the latent features to obtain sufficient and
compact (necessary) class-relevant feature for generalization to the unseen
domain. Specifically, we first propose an information theory inspired loss
function to ensure the disentangled class-relevant features contain sufficient
class label information and the other disentangled auxiliary feature has
sufficient domain information. We further propose a paired purification loss
function to let the auxiliary feature discard all the class-relevant
information and thus the class-relevant feature will contain sufficient and
compact (necessary) class-relevant information. Moreover, instead of using
multiple encoders, we propose to use a learnable binary mask as our
disentangler to make the disentanglement more efficient and make the
disentangled features complementary to each other. We conduct extensive
experiments on four widely used DG benchmark datasets including PACS,
OfficeHome, TerraIncognita, and DomainNet. The proposed INSURE outperforms the
state-of-art methods. We also empirically show that domain-specific
class-relevant features are beneficial for domain generalization.Comment: 10 pages, 4 figure
r-Hint: A message-efficient random access response for mMTC in 5G networks
Massive Machine Type Communication (mMTC) has attracted increasing attention due to the explosive growth of IoT devices. Random Access (RA) for a large number of mMTC devices is especially difficult since the high signaling overhead between User Equipments (UEs) and an eNB may overwhelm the available spectrum resources. To address this issue, we propose “respond by hint” (r-Hint), an ID-free handshaking protocol for contention-based RA in mMTC. The core idea of r-Hint is to avoid sequentially notifying contending UEs of their IDs by broadcasting a hint in the RA Response (RAR). To do so, we exploit the concept of prime factorization and hashing to encode the hint such that UEs can extract their required information accordingly. Our simulation results show that r-Hint reduces the RAR message size by 20%–40%. Such reduction can be translated to around 50% improvement of spectrum efficiency in LTE-M
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