Drift control is significant to the safety of autonomous vehicles when there is a sudden loss of traction due to external conditions such as rain or snow. It is a challenging control problem due to the presence of significant sideslip and nearly full
saturation of the tires. In this paper, we focus on the control of drift maneuvers following circular paths with either fixed or moving centers, subject to change in the tire-ground interaction, which are common training tasks for drift enthusiasts and can therefore be used as benchmarks of the performance of drift control. In order to achieve the above tasks, we propose a novel hierarchical control architecture which decouples the curvature and center control of the trajectory. In particular, an outer loop stabilizes the center by tuning the target curvature, and an inner loop tracks the curvature using a feedforward/feedback controller enhanced by an $mathcal{L}_1$ adaptive component. The hierarchical architecture is flexible because the inner loop is task-agnostic and adaptive to changes in tire-road interaction, which allows the outer loop to be designed independent of low-level dynamics, opening up the possibility of incorporating sophisticated planning algorithms. We implement our control strategy on a simulation platform as well as on a 1/10 scale Radio-Control~(RC) car, and both the simulation and experiment results illustrate the effectiveness of our strategy in achieving the above described set of drift maneuvering tasks.
We propose an Auto-Parsing Network (APN) to discover and exploit the input datas hidden tree structures for improving the effectiveness of the Transformer-based vision-language systems. Specifically, we impose a Probabilistic Graphical Model (PGM) pa
rameterized by the attention operations on each self-attention layer to incorporate sparse assumption. We use this PGM to softly segment an input sequence into a few clusters where each cluster can be treated as the parent of the inside entities. By stacking these PGM constrained self-attention layers, the clusters in a lower layer compose into a new sequence, and the PGM in a higher layer will further segment this sequence. Iteratively, a sparse tree can be implicitly parsed, and this trees hierarchical knowledge is incorporated into the transformed embeddings, which can be used for solving the target vision-language tasks. Specifically, we showcase that our APN can strengthen Transformer based networks in two major vision-language tasks: Captioning and Visual Question Answering. Also, a PGM probability-based parsing algorithm is developed by which we can discover what the hidden structure of input is during the inference.
Medical instrument segmentation in 3D ultrasound is essential for image-guided intervention. However, to train a successful deep neural network for instrument segmentation, a large number of labeled images are required, which is expensive and time-co
nsuming to obtain. In this article, we propose a semi-supervised learning (SSL) framework for instrument segmentation in 3D US, which requires much less annotation effort than the existing methods. To achieve the SSL learning, a Dual-UNet is proposed to segment the instrument. The Dual-UNet leverages unlabeled data using a novel hybrid loss function, consisting of uncertainty and contextual constraints. Specifically, the uncertainty constraints leverage the uncertainty estimation of the predictions of the UNet, and therefore improve the unlabeled information for SSL training. In addition, contextual constraints exploit the contextual information of the training images, which are used as the complementary information for voxel-wise uncertainty estimation. Extensive experiments on multiple ex-vivo and in-vivo datasets show that our proposed method achieves Dice score of about 68.6%-69.1% and the inference time of about 1 sec. per volume. These results are better than the state-of-the-art SSL methods and the inference time is comparable to the supervised approaches.
Although much progress has been made in visual emotion recognition, researchers have realized that modern deep networks tend to exploit dataset characteristics to learn spurious statistical associations between the input and the target. Such dataset
characteristics are usually treated as dataset bias, which damages the robustness and generalization performance of these recognition systems. In this work, we scrutinize this problem from the perspective of causal inference, where such dataset characteristic is termed as a confounder which misleads the system to learn the spurious correlation. To alleviate the negative effects brought by the dataset bias, we propose a novel Interventional Emotion Recognition Network (IERN) to achieve the backdoor adjustment, which is one fundamental deconfounding technique in causal inference. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
In this paper, we propose a deep unfitted Nitsche method for computing elliptic interface problems with high contrasts in high dimensions. To capture discontinuities of the solution caused by interfaces, we reformulate the problem as an energy minimi
zation involving two weakly coupled components. This enables us to train two deep neural networks to represent two components of the solution in high-dimensional. The curse of dimensionality is alleviated by using the Monte-Carlo method to discretize the unfitted Nitsche energy function. We present several numerical examples to show the efficiency and accuracy of the proposed method.
High-dimensional, low sample-size (HDLSS) data problems have been a topic of immense importance for the last couple of decades. There is a vast literature that proposed a wide variety of approaches to deal with this situation, among which variable se
lection was a compelling idea. On the other hand, a deep neural network has been used to model complicated relationships and interactions among responses and features, which is hard to capture using a linear or an additive model. In this paper, we discuss the current status of variable selection techniques with the neural network models. We show that the stage-wise algorithm with neural network suffers from disadvantages such as the variables entering into the model later may not be consistent. We then propose an ensemble method to achieve better variable selection and prove that it has probability tending to zero that a false variable is selected. Then, we discuss additional regularization to deal with over-fitting and make better regression and classification. We study various statistical properties of our proposed method. Extensive simulations and real data examples are provided to support the theory and methodology.
The chirality-induced spin selectivity (CISS) effect enables the detection of chirality as electrical charge signals. It is often studied in a spin-valve device where a ferromagnet is connected to a chiral component between two electrodes, and magnet
oresistance (MR) is reported upon magnetization reversal. This however is not expected in the linear response regime because of compensating reciprocal processes, thereby limiting the interpretation of experimental results. Here we show that MR effects can indeed appear in the linear response regime, but not by complete magnetization or magnetic field reversal. We illustrate this in a spin-valve device and in a chiral thin film as the CISS-induced Hanle magnetoresistance (CHMR) effect.
We report the results of a multi-year spectroscopic and photometric monitoring campaign of two luminous quasars, PG~0923+201 and PG~1001+291, both located at the high-luminosity end of the broad-line region (BLR) size-luminosity relation with optical
luminosities above $10^{45}~{rm erg~s^{-1}}$. PG~0923+201 is for the first time monitored, and PG~1001+291 was previously monitored but our campaign has a much longer temporal baseline. We detect time lags of variations of the broad H$beta$, H$gamma$, Fe {sc ii} lines with respect to those of the 5100~{AA} continuum. The velocity-resolved delay map of H$beta$ in PG~0923+201 indicates a complicated structure with a mix of Keplerian disk-like motion and outflow, and the map of H$beta$ in PG~1001+291 shows a signature of Keplerian disk-like motion. Assuming a virial factor of $f_{rm BLR}=1$ and FWHM line widths, we measure the black hole mass to be $118_{-16}^{+11}times 10^7 M_{odot}$ for PG~0923+201 and $3.33_{-0.54}^{+0.62}times 10^7 M_{odot}$ for PG~1001+291. Their respective accretion rates are estimated to be $0.21_{-0.07}^{+0.06} times L_{rm Edd},c^{-2}$ and $679_{-227}^{+259}times L_{rm Edd},c^{-2}$, indicating that PG~0923+201 is a sub-Eddington accretor and PG~1001+291 is a super-Eddington accretor. While the H$beta$ time lag of PG~0923+201 agrees with the size-luminosity relation, the time lag of PG~1001+291 shows a significant deviation, confirming that in high-luminosity AGN the BLR size depends on both luminosity and Eddington ratio. Black hole mass estimates from single AGN spectra will be over-estimated at high luminosities and redshifts if this effect is not taken into account.
This paper discusses desirable properties of forecasting models in production systems. It then develops a family of models which are designed to satisfy these properties: highly customizable to capture complex patterns; accommodates a large variety o
f objectives; has interpretable components; produces robust results; has automatic changepoint detection for trend and seasonality; and runs fast -- making it a good choice for reliable and scalable production systems. The model allows for seasonality at various time scales, events/holidays, and change points in trend and seasonality. The volatility is fitted separately to maintain flexibility and speed and is allowed to be a function of specified features.
For a graph $H$ consisting of finitely many internally disjoint paths connecting two vertices, with possibly distinct lengths, we estimate the corresponding extremal number $text{ex}(n,H)$. When the lengths of all paths have the same parity, $text{ex
}(n,H)$ is $O(n^{1+1/k^ast})$, where $2k^ast$ is the size of the smallest cycle which is included in $H$ as a subgraph. We also establish the matching lower bound in the particular case of $text{ex}(n,Theta_{3,5,5})$, where $Theta_{3,5,5}$ is the graph consisting of three disjoint paths of lengths $3,5$ and $5$ connecting two vertices.
