Miracle-dz/UniFAR-FTUs huggingface dataset:
Modular Vehicle Control for Transferring Semantic Information to Unseen Weather Conditions using GANs

End-to-end supervised learning has shown promising results for self-driving cars, particularly under conditions for which it was trained. However, it may not necessarily perform well under unseen conditions. In this paper, we demonstrate how knowledge can be transferred from one weather condition for which semantic labels and steering commands are available to a completely new set of conditions for which we have no access to labeled data. The problem is addressed by dividing the task of vehicle control into independent perception and control modules, such that changing one does not affect the other. We train the control module only on the data for the available condition and keep it fixed even under new conditions. The perception module is then used as an interface between the new weather conditions and this control model. The perception module in turn is trained using semantic labels, which we assume are already available for the same weather condition on which the control model was trained. However, obtaining them for other conditions is a tedious and error-prone process. Therefore, we propose to use a generative adversarial network (GAN)-based model to retrieve the semantic information for the new conditions in an unsupervised manner. We introduce a master-servant architecture, where the master model (semantic labels available) trains the servant model (semantic labels not available). The servant model can then be used for steering the vehicle without retraining the control module.
Semi-supervised classification by reaching consensus among modalities

We extend the Consensus Network [1] framework to Transductive Consensus 1 Network (TCN), a semi-supervised multi-modal classification framework, and 2 identify its two mechanisms: consensus and classification. By putting forward 3 three variants as ablation studies, we show both mechanisms should be functioning 4 together. Overall, TCNs outperform or align with the best benchmark algorithms 5 when only 20 to 200 labeled data points are available. 6
N E ] 2 8 O ct 2 01 8 Automatically Evolving CNN Architectures Based on Blocks Yanan Sun and

The performance of Convolutional Neural Networks (CNNs) highly relies on their architectures. In order to design a CNN with promising performance, extended expertise in both CNNs and the investigated problem is required, which is not necessarily held by every user interested in CNNs or the problem domain. In this paper, we propose to automatically evolve CNN architectures by using a genetic algorithm based on ResNet blocks and DenseNet blocks. The proposed algorithm is completely automatic in designing CNN architectures, particularly, neither pre-processing before it starts nor post-processing on the designed CNN is needed. Furthermore, the proposed algorithm does not require users with domain knowledge on CNNs, the investigated problem or even genetic algorithms. The proposed algorithm is evaluated on CIFAR10 and CIFAR100 against 18 state-of-the-art peer competitors. Experimental results show that it outperforms state-of-the-art CNNs hand-crafted and CNNs designed by automatic peer competitors in terms of the classification accuracy, and achieves the competitive classification accuracy against semi-automatic peer competitors. In addition, the proposed algorithm consumes much less time than most peer competitors in finding the best CNN architectures.
Deriving Proved Equality Tests in Coq-Elpi: Stronger Induction Principles for Containers in Coq

We describe a procedure to derive equality tests and their correctness proofs from inductive type declarations in Coq. Programs and proofs are derived compositionally, reusing code and proofs derived previously. The key steps are two. First, we design appropriate induction principles for data types defined using parametric containers. Second, we develop a technique to work around the modularity limitations imposed by the purely syntactic termination check Coq performs on recursive proofs. The unary parametricity translation of inductive data types turns out to be the key to both steps. Last but not least, we provide an implementation of the procedure for the Coq proof assistant based on the Elpi [6] extension language. 2012 ACM Subject Classification Software and its engineering → General programming languages
Graph Optimizer Tensor Optimizer VTA JIT Runtime VTA ISA VTA MicroArchitecture

Hardware acceleration is an enabler for ubiquitous and efficient deep learning. With hardware accelerators being introduced in datacenter and edge devices, it is time to acknowledge that hardware specialization is central to the deep learning system stack. This technical report presents the Versatile Tensor Accelerator (VTA, pronounced vita), an open, generic, and customizable deep learning accelerator design. VTA is a programmable accelerator that exposes a RISC-like programming abstraction to describe operations at the tensor level. We designed VTA to expose the most salient and common characteristics of mainstream deep learning accelerators, such as tensor operations, DMA load/stores, and explicit compute/memory arbitration. VTA is more than a standalone accelerator design: it’s an end-to-end solution that includes drivers, a JIT runtime, and an optimizing compiler stack based on TVM. The current release of VTA includes a behavioral hardware simulator, as well as the infrastructure to deploy VTA on low-cost FPGA development boards for fast prototyping. By extending the TVM stack with a customizable, and open source deep learning hardware accelerator design, we are exposing a transparent end-to-end deep learning stack from the high-level deep learning framework, down to the actual hardware design and implementation. This forms a truly end-to-end, from software-to-hardware open source stack for deep learning systems.
Autoencoder for Synthetic to Real Generalization: From Simple to More Complex Scenes Supplementary

We only used the grayscale training images from the SVIRO dataset. We considered everyday objects as background and removed all images containing empty child and infant seats. For the classification evaluation we used all the images from all the different vehicles, but we used training images only. Occupancy classification is performed on the entire image such that all three seats need to be classified simultaneously. Since four classes are available per seat (empty, infant seat, child seat and adult) this results in a total of 4 = 64 classes. The dataset can be downloaded from our website.
Prevention of Atrial Fibrillation After Cardiac Surgery

Atrial fibrillation (AF) is the most common arrhythmia to occur after cardiac surgery. It is associated with postoperative complications, including increased risk of stroke, prolonged hospital stay and increased costs. The purpose of this study was to find a reliable, effective, safe and well tolerated tools for the prevention of postoperative AF after cardiac surgery. The study consisted of three prospective, randomized clinical trials. In study I we randomized 240 patients to receive either oral or intravenous metoprolol for 48 hours after cardiac surgery. The incidence of postoperative AF was significantly lower in the intravenous metoprolol group (16.8%) than in oral group (28.1%), (p=0.036). Intravenous metoprolol administration was feasible and easy and turned out to be welltolerated in postoperative patients. In study II a total of 241 patients were scheduled to receive either intravenous hydrocortisone or placebo postoperatively for 84 hours. The incidence of postoperative AF was 30.0 % in the hydrocortisone group compared with 47.9% in the placebo group and the relative risk reduction was 37%. Intravenous hydrocortisone therapy turned out to be well tolerated. In addition, no serious complications were associated with intravenous corticosteroid therapy. In study III intravenous metoprolol therapy showed to be as effective as intravenous amiodarone in the prevention of AF after cardiac surgery. Three hundred and sixteen patients were randomized to receive either metoprolol or amiodarone intravenously starting on the first postoperative morning after cardiac surgery. The incidence of postoperative AF was 23.9% in the metoprolol group and 24.8% in the amiodarone group with no statistical difference between the groups. However, because of the wide range of the confidence intervals, we can not conclude that intravenous metoprolol and amiodarone are equally effective in the prevention of AF after cardiac surgery. In summary, we suggest that intravenous metoprolol therapy should be a part of routine medication to prevent AF in all patients undergoing cardiac surgery, unless contraindicated. Moderate-dosage corticosteroid (hydrocortisone) should be considered for the prevention of AF in high risk patients undergoing cardiac surgery. Amiodarone should be used for the prevention of postoperative AF only if beta-blocker therapy is contraindicated. National Library of Medical Classification: QV 150, WG 169, WG 330, WO 184 Medical Subject Headings: Amiodarone; Anti-Arrhythmia Agents; Aortic Valve Surgery; Arrhythmias; Atrial Fibrillation; Cardiac Surgical Procedures; Coronary Artery Bypass Surgery; Hydrocortisone; Metoprolol; Postoperative; Prevention
th International Meshing Roundtable ( IMR 24 ) Parallel local remeshing for moving body applications

This paper presents a parallel local remeshing approach on distributed parallel computers based on the message passing interfaces (MPI). In comparison to those existing approaches that require combining a single hole initially spanning multiple processors into one processor so that the sequential results can be reproduced, the proposed approach avoids this limit by decomposing the initially distributed holes into many sub-holes and distributing them evenly on the involved computer cores. The employed domain decomposition approach can ensure the generation of a well-shaped inter-hole boundary always. Therefore, the subsequent remeshing step can fix the inter-hole boundary without compromising the quality of elements around this boundary. In the mean time, because the remeshing procedure involves no communications, its parallel efficiency is very high. An application of the remeshing algorithms in a benchmark store separation simulation demonstrates the capabilities of the approach. © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of organizing committee of the 24th International Meshing Roundtable (IMR24).
Mapping DNN Embedding Manifolds for Network Generalization Prediction

Understanding Deep Neural Network (DNN) performance in changing conditions is essential for deploying DNNs in safety critical applications with unconstrained environments, e.g., perception for self-driving vehicles or medical image analysis. Recently, the task of Network Generalization Prediction (NGP) has been proposed to predict how a DNN will generalize in a new operating domain. Previous NGP approaches have relied on labeled metadata and known distributions for the new operating domains. In this study, we propose the first NGP approach that predicts DNN performance based solely on how unlabeled images from an external operating domain map in the DNN embedding space. We demonstrate this technique for pedestrian, melanoma, and animal classification tasks and show state of the art NGP in 13 of 15 NGP tasks without requiring domain knowledge. Additionally, we show that our NGP embedding maps can be used to identify misclassified images when the DNN performance is poor.
Adaptive residual based schemes for solving the penalized Navier Stokes equations with moving bodies : application to ice shedding trajectories

The prediction of solid motion evolving in a fluid presents a real interest for engineering application such as ice accretion on aerodynamics bodies.In this context, considering de-icing systems, the ice shedding trajectory is needed to prevent the risk of collision/ingestion of the ice in/with some sensitive part of the aircraft. This application raises many challenges from a numerical point of view, especially concerning mesh generation/adaptation as the solid moves in the computational domain. To handle this issue, in this work the solids are known implicitly on the mesh via a level set function. An immersed boundary method, called penalization, is employed to impose the wall boundary conditions. To improve the resolution of these boundaries, the equations are solved on adaptive unstructured grids. This allows to have are finement close to the solid boundary and thus increases the solid definition,leading to a more accurate imposition of the wall conditions. To save computational time, and avoid costly remeshing/interpolation steps, the strategy chosen for unsteady simulations is to use a constant connectivity mesh adaptation,also known as r-adaptation
Electron density distribution and electronic structure as tools to study the origin of ferroic states in ferroelectric and magnetic materials

ABSTRACT In this paper, three well-known prototypic ferroic materials, BaTiO3, PbZr0.4Ti0. 6O3 and Fe3O4, were experimentally and theoretically investigated to explore the origin of their respective ferroic state. Their structural properties were determined by X-ray powder diffraction and Rietveld refinement. The electron density distribution around cationic and anionic sites were obtained by using the Maximum Entropy Method on analyzing the X-ray diffraction results. Electron density distributions and the electronic band structures were determined by Density Functional Theory simulations using experimental and refined structural parameters as input data. The results show a strong correspondence for all the obtained properties and features, suggesting the important role of structural aspects and chemical bonding in the stabilization of the ferroic states in each studied system.
The market model of CDO spreads

Abstract: In this paper we present a new arbitrage-free bottomup model of correlated defaults, based on a special approach to systematic and idiosyncratic risks for individual obligors. The model admits several attractive features, like consistency with currency and interest rate models, as well as numerical tractability and flexibility, making it capable to fit the market for practically all self-consistent CDO tranche prices. Its background is rather remote from other approaches, like copulas and point processes, so our presentation is detailed.
Reﬂection and Rotation Symmetry Detection via Equivariant Learning — Supplemental Material —

The details of data annotation for the DENDI dataset are described in this section. To identify symmetry, we disregard texture and focus just on the shape of the object. The partial occlusion of boundary of symmetric object is allowed to a fourth of the object boundaries. For both reflection and rotation symmetries, we exhaustively mark all symmetries in an image, including those of parts. This policy ensures that the DENDI contains dense annotations. we present some examples in Fig.1(d) and Fig.2(d).
A reconstruction algorithm based on sparse representation for Raman signal processing under high background noise

Background noise is one of the main interference sources of the Raman spectroscopy measurement and imaging technique. In this paper, a sparse representation based algorithm is presented to process the Raman signals under high background noise. In contrast with the existing de-noising methods, the proposed method reconstructs the pure Raman signals by estimating the Raman peak information. The advantage of the proposed algorithm is its high anti-noise capacity and low pure Raman signal reduction contributed by its reconstruction principle. Meanwhile, the Batch-OMP algorithm is applied to accelerate the training of the sparse representation. Therefore, it is very suitable to be adopted in the Raman measurement or imaging instruments to observe fast dynamic processes where the scanning time has to be shortened and the signal-to-noise ratio (SNR) of the raw tested signal is reduced. In the simulation and experiment, the de-noising result obtained by the proposed algorithm was better than the traditional Savitzky-Golay (S-G) filter and the fixed-threshold wavelet de-noising algorithm.
Observed Human Motion Ground-truth Zero-Velocity Res-GRU LSTM-3 LR Ours

Human motion prediction aims at generating future frames of human motion based on an observed sequence of skeletons. Recent methods employ the latest hidden states of a recurrent neural network (RNN) to encode the historical skeletons, which can only address short-term prediction. In this work, we propose a motion context modeling by summarizing the historical human motion with respect to the current prediction. A modified highway unit (MHU) is proposed for efficiently eliminating motionless joints and estimating next pose given the motion context. Furthermore, we enhance the motion dynamic by minimizing the gram matrix loss for long-term motion prediction. Experimental results show that the proposed model can promisingly forecast the human future movements, which yields superior performances over related state-ofthe-art approaches. Moreover, specifying the motion context with the activity labels enables our model to perform human motion transfer.
Counting Down To 2004: Some Insights And Strategies For Satisfying Tc2 K While There Is Still Time

Next fall (2003) will be the last time a technology program may choose to be evaluated under the old criteria of the Technology Accreditation Commission of the Accreditation Board for Engineering and Technology (TAC/ABET). Starting in 2004 accreditation visits will be governed by the “Technology Criteria 2000” (TC2K) criteria except for interim visits. What does this mean operationally for programs? It means that faculty and administrators must be prepared to prove that their students at the end of the program satisfy the required a through k outcomes of Criterion 1. Program criteria developed by professional societies must be satisfied and continuous improvement processes based on assessment data evaluation must be in place and producing results. Additionally, program objectives that satisfy constituents and are consistent with the institution’s mission, goals, and objectives need to be defined and data gathered on graduates to show the quality of the program in meeting these objectives. The authors are drawing on their TC2K program evaluator experience and on their TC2K evaluator training to prepare their programs at Purdue Calumet for a visit under the new criteria in fall, 2005. This paper will point out sources of information on how to prepare for an accreditation visit and will discuss preparations at Purdue University Calumet to meet the TC2K criteria. Thus far, a continuous improvement culture has been cultivated with several measures that will be mentioned in the paper. Ten new tools for assessment have been developed that fit within a continuous improvement paradigm and meet strategic planning needs at the school, department, and program levels. Course embedded assessment measures to collect student outcomes data are being implemented. And finally, curriculum adjustments to accommodate new TC2K requirements are considered.
Automatic Vector-Based Road Structure Mapping Using Multibeam LiDAR

The high-definition map (HD-map) of road structures is crucial for the safe planning and control of autonomous vehicles. However, generating and updating such maps requires intensive manual work. Simultaneous localization and mapping (SLAM) is able to automatically build and update a map of the environment. Nevertheless, there is still a lack of SLAM method for generating vector-based road structure maps. In this paper, we propose a vector-based SLAM method for the road structure mapping using vehicle-mounted multibeam LiDAR. We propose using polylines as the primary mapping element instead of grid maps or point clouds because the vector-based representation is lightweight and precise. We explored the following: (1) the extraction and vectorization of road structures based on multiframe probabilistic fusion; (2) the efficient vector-based matching between frames of road structures; (3) the loop closure and optimization based on the pose-graph; and (4) the global reconstruction of the vector map. One specific road structure, the road boundary, is taken as an example. We applied the proposed mapping method to three road scenes, ranging from hundreds of meters to over ten kilometers and the results are automatically generated vector-based road boundary maps. The average absolute pose error of the trajectory in the mapping is 1.83 m without the aid of high-precision GPS.
Expert Systems With Applications

Feature selection is used in many application areas relevant to expert and intelligent systems, such as data mining and machine learning, image processing, anomaly detection, bioinformatics and natural language processing. Feature selection based on information theory is a popular approach due its computational ef-ﬁciency, scalability in terms of the dataset dimensionality, and independence from the classiﬁer. Common drawbacks of this approach are the lack of information about the interaction between the features and the classiﬁer, and the selection of redundant and irrelevant features. The latter is due to the limitations of the employed goal functions leading to overestimation of the feature signiﬁcance. To address this problem, this article introduces two new nonlinear feature selection methods, namely Joint Mutual Information Maximisation (JMIM) and Normalised Joint Mutual Information Maximisation (NJMIM); both these methods use mutual information and the ‘maximum of the minimum’ criterion, which alleviates the problem of overestimation of the feature signiﬁcance as demonstrated both theoretically and experimentally. The proposed methods are compared using eleven publically available datasets with ﬁve competing methods. The results demonstrate that the JMIM method outperforms the other methods on most tested public datasets, reducing the relative average classiﬁcation error by almost 6% in comparison to the nextbestperformingmethod. ThestatisticalsigniﬁcanceoftheresultsisconﬁrmedbytheANOVAtest. More-over, this method produces the best trade-off between accuracy and stability.