Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization

Semantic parsing aims to transform natural language (NL) utterances into formal meaning representations (MRs), whereas an NL generator achieves the reverse: producing a NL description for some given MRs. Despite this intrinsic connection, the two tasks are often studied separately in prior work. In this paper, we model the duality of these two tasks via a joint learning framework, and demonstrate its effectiveness of boosting the performance on both tasks. Concretely, we propose a novel method of dual information maximization (DIM) to regularize the learning process, where DIM empirically maximizes the variational lower bounds of expected joint distributions of NL and MRs. We further extend DIM to a semi-supervision setup (SemiDIM), which leverages unlabeled data of both tasks. Experiments on three datasets of dialogue management and code generation (and summarization) show that performance on both semantic parsing and NL generation can be consistently improved by DIM, in both supervised and semi-supervised setups.Comment: Accepted to ACL 201

Oxidative damage to guanine in DNA caused by reactive oxygen species

Oxidative damage to DNA, a factor in cancer, mutation, and aging, is attributed to reactive oxygen species (ROS). The less well characterized ROS, organic peroxyl radicals and peracid are present during lipid peroxidation and also produced by peroxidases from organic hydroperoxides. Peracetic acid is also formed in mitochondria. Guanine (Gua) is the nucleobases most susceptible to oxidation due to its lowest electron potential. The study described here focuses on Gua oxidation by epoxidizing reagents including peroxyl radicals and organic peracids. Dimethyldioxirane (DMDO), peracetic acid and m-chloroperbenzoic acid selectively oxidizes the guanine moiety of dGuo, dGMP and dGTP to 5-carboxamido-5-formamido-2-iminohydantoin (2-Ih). Structures were established on mass spectrometry and NMR studies. Labeling studies support a mechanism involving initial epoxidation of the guanine 4-5 bond and contraction of the pyrimidine ring by a 1,2-migration of the guanine carbonyl C6 to form a transient dehydrodeoxyspiroiminodihydantoin followed by hydrolytic ring opening of the imidazolone ring. The 2-Ih is shown to be a major transformation in the oxidation of the single-stranded DNA 5-mer d(TTGTT) and the 5-base pair duplex d[(TTGTT)·(AACAA)]. 2-Ih has not previously been reported as an oxidative lesion in DNA. Consistent with the proposed mechanism, no 8-oxoguanine was detected as a product of the oxidations of the oligonucleotides or monomeric species mediated by the monooxygen donors. The 2-Ih base thus appears to be a pathway-specific lesion and holds promise as a potential biomarker. N9-(β-D-2-deoxyribofuranosyl)-N2,3-ethenoguanine is a highly mutagenic DNA adduct arising from exposure to known occupational and environmental carcinogens and lipid peroxidation products in vivo. Chemical synthesis has proven to be challenging because of the reported lability of the glycosidic bond under conditions generally applicable to chemical synthesis. Enzymatic and chemical glycosylations of N2,3-ethenoguanine were attempted as approaches to obtain this nucleoside under mild conditions. Both glycosylations led to nucleosides with ribosylation at positions corresponding to N7- and N2 of the Gua framework. A minor product of the enzymatic ribosylation has tentatively been assigned as the α-anomer of the desired N3 riboside, and rigorous confirmation of this structure would demonstrate an unusual stereochemistry for the trans ribosylation

Adaptive Model Predictive Control for Engine-Driven Ducted Fan Lift Systems using an Associated Linear Parameter Varying Model

Ducted fan lift systems (DFLSs) powered by two-stroke aviation piston engines present a challenging control problem due to their complex multivariable dynamics. Current controllers for these systems typically rely on proportional-integral algorithms combined with data tables, which rely on accurate models and are not adaptive to handle time-varying dynamics or system uncertainties. This paper proposes a novel adaptive model predictive control (AMPC) strategy with an associated linear parameter varying (LPV) model for controlling the engine-driven DFLS. This LPV model is derived from a global network model, which is trained off-line with data obtained from a general mean value engine model for two-stroke aviation engines. Different network models, including multi-layer perceptron, Elman, and radial basis function (RBF), are evaluated and compared in this study. The results demonstrate that the RBF model exhibits higher prediction accuracy and robustness in the DFLS application. Based on the trained RBF model, the proposed AMPC approach constructs an associated network that directly outputs the LPV model parameters as an adaptive, robust, and efficient prediction model. The efficiency of the proposed approach is demonstrated through numerical simulations of a vertical take-off thrust preparation process for the DFLS. The simulation results indicate that the proposed AMPC method can effectively control the DFLS thrust with a relative error below 3.5%

What Happened 3 Seconds Ago? Inferring the Past with Thermal Imaging

Inferring past human motion from RGB images is challenging due to the inherent uncertainty of the prediction problem. Thermal images, on the other hand, encode traces of past human-object interactions left in the environment via thermal radiation measurement. Based on this observation, we collect the first RGB-Thermal dataset for human motion analysis, dubbed Thermal-IM. Then we develop a three-stage neural network model for accurate past human pose estimation. Comprehensive experiments show that thermal cues significantly reduce the ambiguities of this task, and the proposed model achieves remarkable performance. The dataset is available at https://github.com/ZitianTang/Thermal-IM

An Optimization Framework For Anomaly Detection Scores Refinement With Side Information

This paper considers an anomaly detection problem in which a detection algorithm assigns anomaly scores to multi-dimensional data points, such as cellular networks' Key Performance Indicators (KPIs). We propose an optimization framework to refine these anomaly scores by leveraging side information in the form of a causality graph between the various features of the data points. The refinement block builds on causality theory and a proposed notion of confidence scores. After motivating our framework, smoothness properties are proved for the ensuing mathematical expressions. Next, equipped with these results, a gradient descent algorithm is proposed, and a proof of its convergence to a stationary point is provided. Our results hold (i) for any causal anomaly detection algorithm and (ii) for any side information in the form of a directed acyclic graph. Numerical results are provided to illustrate the advantage of our proposed framework in dealing with False Positives (FPs) and False Negatives (FNs). Additionally, the effect of the graph's structure on the expected performance advantage and the various trade-offs that take place are analyzed

RecAD: Towards A Unified Library for Recommender Attack and Defense

In recent years, recommender systems have become a ubiquitous part of our daily lives, while they suffer from a high risk of being attacked due to the growing commercial and social values. Despite significant research progress in recommender attack and defense, there is a lack of a widely-recognized benchmarking standard in the field, leading to unfair performance comparison and limited credibility of experiments. To address this, we propose RecAD, a unified library aiming at establishing an open benchmark for recommender attack and defense. RecAD takes an initial step to set up a unified benchmarking pipeline for reproducible research by integrating diverse datasets, standard source codes, hyper-parameter settings, running logs, attack knowledge, attack budget, and evaluation results. The benchmark is designed to be comprehensive and sustainable, covering both attack, defense, and evaluation tasks, enabling more researchers to easily follow and contribute to this promising field. RecAD will drive more solid and reproducible research on recommender systems attack and defense, reduce the redundant efforts of researchers, and ultimately increase the credibility and practical value of recommender attack and defense. The project is released at https://github.com/gusye1234/recad

VNI-Net: Vector Neurons-based Rotation-Invariant Descriptor for LiDAR Place Recognition

LiDAR-based place recognition plays a crucial role in Simultaneous Localization and Mapping (SLAM) and LiDAR localization. Despite the emergence of various deep learning-based and hand-crafting-based methods, rotation-induced place recognition failure remains a critical challenge. Existing studies address this limitation through specific training strategies or network structures. However, the former does not produce satisfactory results, while the latter focuses mainly on the reduced problem of SO(2) rotation invariance. Methods targeting SO(3) rotation invariance suffer from limitations in discrimination capability. In this paper, we propose a new method that employs Vector Neurons Network (VNN) to achieve SO(3) rotation invariance. We first extract rotation-equivariant features from neighboring points and map low-dimensional features to a high-dimensional space through VNN. Afterwards, we calculate the Euclidean and Cosine distance in the rotation-equivariant feature space as rotation-invariant feature descriptors. Finally, we aggregate the features using GeM pooling to obtain global descriptors. To address the significant information loss when formulating rotation-invariant descriptors, we propose computing distances between features at different layers within the Euclidean space neighborhood. This greatly improves the discriminability of the point cloud descriptors while ensuring computational efficiency. Experimental results on public datasets show that our approach significantly outperforms other baseline methods implementing rotation invariance, while achieving comparable results with current state-of-the-art place recognition methods that do not consider rotation issues