6,073 research outputs found
Sufficient Conditions of 6-Cycles Make Planar Graphs DP-4-Colorable
No full textIn simple graphs, DP-coloring is a generalization of list coloring and thus many results of DP-coloring generalize those of list coloring. Xu and Wu proved that every planar graph without 5-cycles adjacent simultaneously to 3-cycles and 4-cycles is 4-choosable. Later, Sittitrai and Nakprasit showed that if a planar graph has no pairwise adjacent 3-, 4-, and 5-cycles, then it is DP-4-colorable, which is a generalization of the result of Xu and Wu. In this paper, we extend the results on 3-, 4-, 5-, and 6-cycles by showing that every planar graph without 6-cycles simultaneously adjacent to 3-cycles, 4-cycles, and 5-cycles is DP-4-colorable, which is also a generalization of previous studies as follows: every planar graph G is DP-4-colorable if G has no 6-cycles adjacent to i-cycles where i∈{3,4,5}
DP-YOLO: A Lightweight Traffic Sign Detection Model for small object detection
No full textAutonomous driving is a critical area in artificial intelligence, with vast potential for development. While current object detection algorithms have shown strong performance in traffic sign detection, they still face difficulties with small object recognition, often resulting in missed or false detections. To address this, we propose DP-YOLO, a traffic sign detection algorithm based on YOLOv8s. To enhance detection accuracy for small objects and reduce the model's parameter count, we first removed the large object detection layer from the baseline model and added a small object detection layer. In the feature extraction stage, we design the DBBNCSPELAN4 module to boost the network's feature extraction capability. Additionally, we propose the PTCSP module, incorporating Transformer technology into the model's feature processing network and reducing both parameters and computational cost. Finally, we introduce the W3F_MPDIoU loss to mitigate the impact of low-quality samples on the model and enhance its robustness. Experiments demonstrate that, compared to YOLOv8s, DP-YOLO reduces the model's parameter count by 77.0%, while achieving improvements in mAP0.5 by 5.8% on the TT100K dataset, 2.7% on the GTSDB dataset, and 1.3% on the CCTSDB dataset. Experimental results demonstrate that the proposed method effectively enhances the detection capability for small-sized traffic signs and exhibits high potential for edge deployment.</p
SEARCH FOR CRITICAL-POINTS IN THE SU(2) HIGGS-MODEL
No full textEVERTZ HG, JERSAK J, LANDAU DP, Neuhaus T, XU JL. SEARCH FOR CRITICAL-POINTS IN THE SU(2) HIGGS-MODEL. PHYSICAL REVIEW D. 1990;41(8):2573-2580
A common HLA-DPA1 variant is associated with hepatitis B virus infection but fails to distinguish active from inactive Caucasian carriers
No full textBackground and Aims: Chronic infection with the hepatitis B virus (HBV) is a major health issue worldwide. Recently, single nucleotide polymorphisms (SNPs) within the human leukocyte antigen (HLA)-DP locus were identified to be associated with HBV infection in Asian populations. Most significant associations were observed for the A alleles of HLA-DPA1 rs3077 and HLA-DPB1 rs9277535, which conferred a decreased risk for HBV infection. We assessed the implications of these variants for HBV infection in Caucasians.
Methods: Two HLA-DP gene variants (rs3077 and rs9277535) were analyzed for associations with persistent HBV infection and with different clinical outcomes, i.e., inactive HBsAg carrier status versus progressive chronic HBV (CHB) infection in Caucasian patients (n = 201) and HBsAg negative controls (n = 235).
Results: The HLA-DPA1 rs3077 C allele was significantly associated with HBV infection (odds ratio, OR = 5.1, 95% confidence interval, CI: 1.9–13.7; p = 0.00093). However, no significant association was seen for rs3077 with progressive CHB infection versus inactive HBsAg carrier status (OR = 2.7, 95% CI: 0.6–11.1; p = 0.31). In contrast, HLA-DPB1 rs9277535 was not associated with HBV infection in Caucasians (OR = 0.8, 95% CI: 0.4–1.9; p = 1).
Conclusions: A highly significant association of HLA-DPA1 rs3077 with HBV infection was observed in Caucasians. However, as a differentiation between different clinical courses of HBV infection was not possible, knowledge of the HLA-DPA1 genotype cannot be translated into personalized anti-HBV therapy approaches
On the effects of non-linearities in DP systems
No full textIn offshore operations a trend is forming where vessels are more often required to do multiple short operations within a small-time frame. Traditional mooring systems require execution time far beyond the operation time. Dynamic positioning systems offer great advantages for short time span operations such as crew transfer or lift operations. Currently operations are planned based on DP capability plots and experience of captain and DPO. DP capability plots have little operational value as this is a static calculation and only provide information for average station keeping capability. During operations, the displacements made by the vessel around the DP set-point, also referred to as DP offset, are of great importance to determine the operability of an operation. Currently, the only way of calculating the DP offset is by conducting extensive time domain simulations, which are hard to integrate in the operational workflow of a DP vessel involved in walk-to-work operations. Therefore, a new approach is developed which predicts the vessel’s DP offset in the frequency domain, which enables a quick and robust calculation of the DP offset which is suited to merge into the on-board workflow. A frequency domain model is per definition a linear model. This leads to the main challenge of this research. A vessel operating on DP is non-linear. Currently there is no insight in what the effect is of non-linear components present in a DP system, on the linear approximation of a frequency domain model. To investigate the effect of non-linear components onto the DP frequency domain model, a time domain model is developed that is capable of systematically enabling/disabling different non-linear components. The time domain model will serve as the ’truth’ in this research as no actual vessel data is available. Furthermore, this helps identify the effects more easily, as the input for both models are identical. From the time domain model transfer functions can be derived that serve as the basis for the frequency domain model. The transfer function is a linear relation between two variables. In this case, between second order wave drift forces and displacement of the vessel in surge, sway and yaw direction. The following non-linear components are investigated in this research: Thruster ramp up, thruster turning rate, forbidden zones, saturation and thruster allocation. Thruster allocation is present in each model that will be tested, as this is an essential part of a DP system. Using two methods of determining transfer functions the model and the effects of all non-linear components are tested. The model is subjected to a variety sea-state, with different wave directions. Both methods offer similar results even though different approaches to determine the transfer functions are used. The selected method is capable of accurately predicting vessel offsets, although some extreme offsets are not captured. It is concluded that the presence of non-linear components have little to no effect on the DP offset as calculated by the time domain model. Because natural frequencies characteristic to these non-linear components are expected to exist at much higher frequencies that naturally present in second order wave drift forces. Thus, making a linear frequency domain model suitable for DP offset forecasting. It is advised to investigate the effect of including 2D input spectra as this is expected to improve the current model.<br/
Levels, profile and distribution of Dechloran Plus (DP) and Polybrominated Diphenyl Ethers (PBDEs) in the environment of Pakistan
No full textNo scientific data is available on emerging contaminants including Polybrominated Diphenyl Ethers (PBDEs) and Dechloran Plus (DP) levels in the environment in Pakistan. Levels of PBDEs and DP were determined in the soil, sediment and atmospheric samples along the stretch of River Ravi in Punjab Province. Average concentrations of ΣPBDEs in atmosphere, soils and sediments were 36 pg m(-3), 40 ng g(-1) and 640 ng g(-1). BDE-209 was the most abundant PBDE congener, showing that deca-BDE accounts for most of the total PBDE emitted in the environment of Pakistan. Total DP levels were calculated as 88 pg m(-3), 0.8 ng g(-1) and 1.9 ng g(-1) in air, soil and sediment samples, respectively. The lower average fractions of anti-DP showed significant differences to those of the technical mixtures, indicating the lack of DP production source in Pakistan
Differentially Private GAN for Time Series
No full textGenerative Adversarial Networks (GANs) are a modern solution aiming to encourage public sharing of data, even if the data contains inherently private information, by generating synthetic data that looks like, but is not equal to, the data the GAN was trained on. However, GANs are prone to remembering samples from the training data, therefore additional care is needed to guarantee privacy. Differentially Private (DP) GANs offer a solution to this problem by protecting user privacy through a mathematical guarantee, achieved by adding carefully constructed noise at specific points in the training process. A state-of-the-art example of such a GAN is Gradient Sanitized Wasserstein GAN, (GS-WGAN), \cite{chen2021gswgan}. This model is shown to create higher quality synthetic images than other DP GANs. To extend the applicability of GS-WGAN we first reproduce and extend the evaluation, verifying that the model outperforms DP-CGAN by an average of 40\% when assessed across three qualitative metrics and two datasets. Secondly we propose improvements to the architecture and training procedure to make GS-WGAN applicable on timeseries data. The experimental results show that GS-WGAN is fit for generating synthetic timeseries through promising experimental results.[1] D. Chen, T. Orekondy, and M. Fritz, “Gs-wgan: A gradient-sanitized approach for learning differentially private generators,” 2021CSE3000 Research ProjectComputer Science and Engineerin
Fast Approximate Dynamic Programming for Input-Affine Dynamics
No full textWe propose two novel numerical schemes for the approximate implementation of the dynamic programming (DP) operation concerned with finite-horizon optimal control of discrete-time systems with input-affine dynamics. The proposed algorithms involve discretization of the state and input spaces and are based on an alternative path that solves the dual problem corresponding to the DP operation. We provide error bounds for the proposed algorithms, along with a detailed analysis of their computational complexity. In particular, for a specific class of problems with separable data in the state and input variables, the proposed approach can reduce the typical time complexity of the DP operation from O(XU) to O(X+U) , where X and U denote the size of the discrete state and input spaces, respectively. This reduction in complexity is achieved by an algorithmic transformation of the minimization in DP operation to an addition via discrete conjugation.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Peyman Mohajerin Esfahan
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming
No full textIn this paper, we introduce BNN-DP, an efficient algorithmic framework for analysis of adversarial robustness of Bayesian Neural Networks (BNNs). Given a compact set of input points T ⊂ Rn, BNN-DP computes lower and upper bounds on the BNN's predictions for all the points in T. The framework is based on an interpretation of BNNs as stochastic dynamical systems, which enables the use of Dynamic Programming (DP) algorithms to bound the prediction range along the layers of the network. Specifically, the method uses bound propagation techniques and convex relaxations to derive a backward recursion procedure to over-approximate the prediction range of the BNN with piecewise affine functions. The algorithm is general and can handle both regression and classification tasks. On a set of experiments on various regression and classification tasks and BNN architectures, we show that BNN-DP outperforms state-of-the-art methods by up to four orders of magnitude in both tightness of the bounds and computational efficiency.Team Luca Laurent
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