166 research outputs found
CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement
Due to the model aging problem, Deep Neural Networks (DNNs) need updates to
adjust them to new data distributions. The common practice leverages
incremental learning (IL), e.g., Class-based Incremental Learning (CIL) that
updates output labels, to update the model with new data and a limited number
of old data. This avoids heavyweight training (from scratch) using conventional
methods and saves storage space by reducing the number of old data to store.
But it also leads to poor performance in fairness. In this paper, we show that
CIL suffers both dataset and algorithm bias problems, and existing solutions
can only partially solve the problem. We propose a novel framework, CILIATE,
that fixes both dataset and algorithm bias in CIL. It features a novel
differential analysis guided dataset and training refinement process that
identifies unique and important samples overlooked by existing CIL and enforces
the model to learn from them. Through this process, CILIATE improves the
fairness of CIL by 17.03%, 22.46%, and 31.79% compared to state-of-the-art
methods, iCaRL, BiC, and WA, respectively, based on our evaluation on three
popular datasets and widely used ResNet models
FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons
With Deep Neural Network (DNN) being integrated into a growing number of
critical systems with far-reaching impacts on society, there are increasing
concerns on their ethical performance, such as fairness. Unfortunately, model
fairness and accuracy in many cases are contradictory goals to optimize. To
solve this issue, there has been a number of work trying to improve model
fairness by using an adversarial game in model level. This approach introduces
an adversary that evaluates the fairness of a model besides its prediction
accuracy on the main task, and performs joint-optimization to achieve a
balanced result. In this paper, we noticed that when performing backward
propagation based training, such contradictory phenomenon has shown on
individual neuron level. Based on this observation, we propose FairNeuron, a
DNN model automatic repairing tool, to mitigate fairness concerns and balance
the accuracy-fairness trade-off without introducing another model. It works on
detecting neurons with contradictory optimization directions from accuracy and
fairness training goals, and achieving a trade-off by selective dropout.
Comparing with state-of-the-art methods, our approach is lightweight, making it
scalable and more efficient. Our evaluation on 3 datasets shows that FairNeuron
can effectively improve all models' fairness while maintaining a stable
utility
Multiobjective Optimization Design of a Fractional Order PID Controller for a Gun Control System
Motion control of gun barrels is an ongoing topic for the development of gun control equipments possessing excellent performances. In this paper, a typical fractional order PID control strategy is employed for the gun control system. To obtain optimal parameters of the controller, a multiobjective optimization scheme is developed from the loop-shaping perspective. To solve the specified nonlinear optimization problem, a novel Pareto optimal solution based multiobjective differential evolution algorithm is proposed. To enhance the convergent rate of the optimization process, an opposition based learning method is embedded in the chaotic population initialization process. To enhance the robustness of the algorithm for different problems, an adapting scheme of the mutation operation is further employed. With assistance of the evolutionary algorithm, the optimal solution for the specified problem is selected. The numerical simulation results show that the control system can rapidly follow the demand signal with high accuracy and high robustness, demonstrating the efficiency of the proposed controller parameter tuning method
Neural-IMLS: Self-supervised Implicit Moving Least-Squares Network for Surface Reconstruction
Surface reconstruction is very challenging when the input point clouds,
particularly real scans, are noisy and lack normals. Observing that the
Multilayer Perceptron (MLP) and the implicit moving least-square function
(IMLS) provide a dual representation of the underlying surface, we introduce
Neural-IMLS, a novel approach that directly learns the noise-resistant signed
distance function (SDF) from unoriented raw point clouds in a self-supervised
fashion. We use the IMLS to regularize the distance values reported by the MLP
while using the MLP to regularize the normals of the data points for running
the IMLS. We also prove that at the convergence, our neural network, benefiting
from the mutual learning mechanism between the MLP and the IMLS, produces a
faithful SDF whose zero-level set approximates the underlying surface. We
conducted extensive experiments on various benchmarks, including synthetic
scans and real scans. The experimental results show that {\em Neural-IMLS} can
reconstruct faithful shapes on various benchmarks with noise and missing parts.
The source code can be found at~\url{https://github.com/bearprin/Neural-IMLS}
Optimization of volume fracturing technology for shallow bow horizontal well in a tight sandstone oil reservoir
The physical property of Chang 6 reservoir in Yanchang oilfield is poor, and the heterogeneity is strong. Multistage fracturing of horizontal wells is easy to form only one large horizontal fracture, but it is difficult to control the fracture height and length. The new mining method of âbow horizontal well + multistage horizontal jointâ can effectively increase the multistage horizontal jointâs spatial position, which improves the drainage area and stimulation efficiency of oil wells. Due to the reservoirâs low permeability and strong heterogeneity, the single well mode of âbow horizontal well + multistage horizontal fractureâ cannot effectively produce Chang 6 reservoir. To improve the production degree of the g 6 reservoir, the fracture model is established using equivalent conductivity and the multigrid method. The pressure response functions of horizontal wells and volume fracturing horizontal wells are established by using the source function, and the relationship between reservoir permeability and starting pressure gradient in the block is calculated. On this basis, the reservoir productivity equation of the block is established, which provides a basis for optimizing the fracturing design parameters of horizontal wells. It is proposed that the flow unit should be considered in the design of fracturing parameters of horizontal fractures, the number of fractures should comprehensively consider whether the fractures can make each flow unit be used, and have large controlled reserves, and the scale of fracturing should comprehensively consider the output and cost. The fracture network model is established by using equivalent conductivity and multi-gridthod, and the volume fracturing design parameters of horizontal wells are optimized, considering the seepage characteristics of the flow unit. The fracturing design parameters of the horizontal section are further defined, which provides a theoretical basis for the efficient development of shallow tight reservoirs
POIROT: Aligning Attack Behavior with Kernel Audit Records for Cyber Threat Hunting
Cyber threat intelligence (CTI) is being used to search for indicators of
attacks that might have compromised an enterprise network for a long time
without being discovered. To have a more effective analysis, CTI open standards
have incorporated descriptive relationships showing how the indicators or
observables are related to each other. However, these relationships are either
completely overlooked in information gathering or not used for threat hunting.
In this paper, we propose a system, called POIROT, which uses these
correlations to uncover the steps of a successful attack campaign. We use
kernel audits as a reliable source that covers all causal relations and
information flows among system entities and model threat hunting as an inexact
graph pattern matching problem. Our technical approach is based on a novel
similarity metric which assesses an alignment between a query graph constructed
out of CTI correlations and a provenance graph constructed out of kernel audit
log records. We evaluate POIROT on publicly released real-world incident
reports as well as reports of an adversarial engagement designed by DARPA,
including ten distinct attack campaigns against different OS platforms such as
Linux, FreeBSD, and Windows. Our evaluation results show that POIROT is capable
of searching inside graphs containing millions of nodes and pinpoint the
attacks in a few minutes, and the results serve to illustrate that CTI
correlations could be used as robust and reliable artifacts for threat hunting.Comment: The final version of this paper is going to appear in the ACM SIGSAC
Conference on Computer and Communications Security (CCS'19), November 11-15,
2019, London, United Kingdo
Extracellular nanomatrix-induced self-organization of neural stem cells into miniature substantia nigra-like structures with therapeutic effects on Parkinsonian rats
Substantia nigra (SN) is a complex and critical region of the brain wherein Parkinson's disease (PD) arises from the degeneration of dopaminergic neurons. Miniature SNâlike structures (miniâSNLSs) constructed from novel combination of nanomaterials and cell technologies exhibit promise as potentially curative cell therapies for PD. In this work, a rapid selfâorganization of miniâSNLS, with an organizational structure and neuronal identities similar to those of the SN in vivo, is achieved by differentiating neural stem cells in vitro on biocompatible silica nanozigzags (NZs) sculptured by glancing angle deposition, without traditional chemical growth factors. The differentiated neurons exhibit electrophysiological activity in vitro. Diverse physical cues and signaling pathways that are determined by the nanomatrices and lead to the selfâorganization of the miniâSNLSs are clarified and elucidated. In vivo, transplantation of the neurons from a miniâSNLS results in an early and progressive amelioration of PD in rats. The sculptured medical device reported here enables the rapid and specific selfâorganization of regionâspecific and functional brainâlike structures without an undesirable prognosis. This development provides promising and significant insights into the screening of potentially curative drugs and cell therapies for PD
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