A Framework Design for Integrating Knowledge Graphs into Recommendation Systems

Online recommendation is a significant research domain in artificial intelligence. A recommendation system recommends different items to users, and has applications in varied domains, including news, music, movies, etc. Initially, recommendation systems were based on demographic, content-based filtering and collaborative filtering. But collaborative filtering often suffers from sparsity and cold start problems, therefore, side information is often used to address these issues and improve recommendation performance. Currently, incorporating knowledge into the recommendation algorithm has attracted increasing attention, as it can help improve recommendation system performance. Knowledge graph representation and construction, and recommendation system development are independent but related; the triples of knowledge graph form the input to the recommendation system. While, there are a number of independent solutions for each of these two tasks, currently, there is no existing solution that can combine the construction of knowledge graph and input it to the recommendation system to provide an integrated work pipeline. Our major contribution is a modular, easy to use framework solution that fills this gap, essentially enabling integration of a structured knowledge graph and a recommendation system. Our framework provides multiple functionalities, including cross-language invocation and pipeline execution mechanism, and also knowledge graph query, modification and visualization. We instantiate our implementation of the proposed framework and evaluate its performance to show that we achieve higher accuracy in recommendations by using side information extracted from knowledge graphs. Our framework addresses the complete pipeline from constructing structured data knowledge graph to training recommendation model to incorporating the recommendation system into application domains

TAPS: Connecting Certified and Adversarial Training

Training certifiably robust neural networks remains a notoriously hard problem. On one side, adversarial training optimizes under-approximations of the worst-case loss, which leads to insufficient regularization for certification, while on the other, sound certified training methods optimize loose over-approximations, leading to over-regularization and poor (standard) accuracy. In this work we propose TAPS, an (unsound) certified training method that combines IBP and PGD training to yield precise, although not necessarily sound, worst-case loss approximations, reducing over-regularization and increasing certified and standard accuracies. Empirically, TAPS achieves a new state-of-the-art in many settings, e.g., reaching a certified accuracy of $22\%$ on TinyImageNet for $\ell_\infty$-perturbations with radius $\epsilon=1/255$. We make our implementation and networks public at https://github.com/eth-sri/taps.Comment: NeuIPS'2

SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment

The increasing maturity of big data applications has led to a proliferation of models targeting the same objectives within the same scenarios and datasets. However, selecting the most suitable model that considers model's features while taking specific requirements and constraints into account still poses a significant challenge. Existing methods have focused on worker-task assignments based on crowdsourcing, they neglect the scenario-dataset-model assignment problem. To address this challenge, a new problem named the Scenario-based Optimal Model Assignment (SOMA) problem is introduced and a novel framework entitled Scenario and Model Associative percepts (SMAP) is developed. SMAP is a heterogeneous information framework that can integrate various types of information to intelligently select a suitable dataset and allocate the optimal model for a specific scenario. To comprehensively evaluate models, a new score function that utilizes multi-head attention mechanisms is proposed. Moreover, a novel memory mechanism named the mnemonic center is developed to store the matched heterogeneous information and prevent duplicate matching. Six popular traffic scenarios are selected as study cases and extensive experiments are conducted on a dataset to verify the effectiveness and efficiency of SMAP and the score function

Constraining Black Carbon Aerosol over Asia using OMI Aerosol Absorption Optical Depth and the Adjoint of GEOS-Chem

Accurate estimates of the emissions and distribution of black carbon (BC) in the region referred to here as Southeastern Asia (70degE-l50degE, 11degS-55degN) are critical to studies of the atmospheric environment and climate change. Analysis of modeled BC concentrations compared to in situ observations indicates levels are underestimated over most of Southeast Asia when using any of four different emission inventories. We thus attempt to reduce uncertainties in BC emissions and improve BC model simulations by developing top-down, spatially resolved, estimates of BC emissions through assimilation of OMI observations of aerosol absorption optical depth (AAOD) with the GEOS-Chem model and its adjoint for April and October of 2006. Overwhelming enhancements, up to 500%, in anthropogenic BC emissions are shown after optimization over broad areas of Southeast Asia in April. In October, the optimization of anthropogenic emissions yields a slight reduction (1-5%) over India and parts of southern China, while emissions increase by 10-50% over eastern China. Observational data from in situ measurements and AERONET observations are used to evaluate the BC inversions and assess the bias between OMI and AERONET AAOD. Low biases in BC concentrations are improved or corrected in most eastern and central sites over China after optimization, while the constrained model still underestimates concentrations in Indian sites in both April and October, possibly as a. consequence of low prior emissions. Model resolution errors may contribute up to a factor of 2.5 to the underestimate of surface BC concentrations over northern India. We also compare the optimized results using different anthropogenic emission inventories and discuss the sensitivity of top-down constraints on anthropogenic emissions with respect to biomass burning emissions. In addition, the impacts of brown carbon, the formulation of the observation operator, and different a priori constraints on the optimization are investigated. Overall, despite these limitations and uncertainties, using OMI AAOD to constrain BC sources improves model representation of BC distributions, particularly over China

Sources of Black Carbon in the Western United States Mountain Ranges

This dissertation investigates the sources of black carbon (BC) in the Western United States (WUS) mountain ranges using a global 3-dimensional chemical transport model (CTM). We quantify the relative contributions from different sources and source regions to BC in the WUS mountain ranges by analyzing surface BC observations for 2006 from the Interagency Monitoring of PROtected Visual Environment (IMPROVE) network. Major discrepancies between modeled and observed surface BC concentrations are found at elevated mountainous sites during the July-October fire season when simulated BC concentrations are negatively biased by a factor of two. We attribute these low biases largely to the underestimated (by more than a factor of two) biomass burning BC emissions in the model, not only in the absolute magnitudes of fire emissions but also in the timing and location of fires. We improve estimates of biomass burning and anthropogenic BC emissions in the WUS for 2006 by inverting surface BC concentrations from the IMPROVE network using a global CTM and its adjoint. We first use active fire counts from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve the spatiotemporal distributions of the biomass burning BC emissions from the Global Fire Emissions Database (GFEDv2). This adjustment primarily shifts emissions from late to middle and early summer (a 33% decrease for September-October and a 56% increase for June-August) and leads to appreciable increases in modeled surface BC concentrations in early and middle summer, especially at the 1-2 and 2-3 km altitude ranges. We then conduct analytical and adjoint inversions at both 2� � 2.5� and 0.5� � 0.667� (nested over North America) horizontal resolutions. The a posteriori biomass burning and anthropogenic BC emissions in the WUS for July-September are 16.4-31.7 Gg (increased by a factor of 2.4-4.7 relative to the corresponding a priori) and 9.1-33.5 Gg (48-190% of the corresponding a priori), respectively. There are large differences in the a posteriori emissions between the analytical and adjoint inversions, mostly evident in different BC emission sectors. The anthropogenic BC emissions in the WUS increase by about a factor of two from the adjoint inversions, but decrease by ~50% from the analytical inversions. The biomass burning BC emissions increase by about factors of 2 after the adjoint inversions and 3 after analytical inversions. The differences are partially because that the inversion system has trouble effectively distinguishing collocated anthropogenic and biomass burning emissions at the grid-based resolution and tends to falsely impose larger anthropogenic emissions in the regions where biomass burning emissions are severely underestimated. Simulated surface BC concentrations with the a posteriori emissions capture the observed major fire episodes at most sites and the substantial enhancements at the 1-2 and 2-3 km altitude ranges, especially at 0.5� � 0.667�. The a posteriori emissions also lead to large bias reductions in modeled surface BC concentrations (~30% on average) and significantly better agreement with observations (increases in Taylor skill scores of ~40-200%)

Long-term Causal Inference Under Persistent Confounding via Data Combination

We study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Since the long-term outcome is observed only after a long delay, it is not measured in the experimental data, but only recorded in the observational data. However, both types of data include observations of some short-term outcomes. In this paper, we uniquely tackle the challenge of persistent unmeasured confounders, i.e., some unmeasured confounders that can simultaneously affect the treatment, short-term outcomes and the long-term outcome, noting that they invalidate identification strategies in previous literature. To address this challenge, we exploit the sequential structure of multiple short-term outcomes, and develop three novel identification strategies for the average long-term treatment effect. We further propose three corresponding estimators and prove their asymptotic consistency and asymptotic normality. We finally apply our methods to estimate the effect of a job training program on long-term employment using semi-synthetic data. We numerically show that our proposals outperform existing methods that fail to handle persistent confounders

Experimental Investigation of Discharge Phenomena in Electrochemical Discharge Machining Process

Electrochemical discharge machining (ECDM) is a promising non−traditional processing technology used to machine non−conductive materials, such as glass and ceramic, based on the evoked electrochemical discharge phenomena around the tool electrode. The discharge in ECDM is a key factor that affects the removal of material. Moreover, the discharge current is an important indicator reflecting the discharge state. However, the discharge characteristics remain an open topic for debate and require further investigation. There is still confusion regarding the distinction of the discharge current from the electrochemical reaction current in ECDM. In this study, high−speed imaging technology was applied to the investigation of the discharge characteristics. By comparing the captured discharge images with the corresponding discharge current, the discharge can be classified into three types. The observations of the discharge effect on the gas film indicate that a force was exerted on the gas film during the discharge process and the shape of the gas film was changed by the force. In addition, the energies released by different types of discharge were calculated according to the voltage and current waveforms. The discharge frequency was found to increase with the increase in applied voltage and the frequency of the second type of discharge was approximately equal to that of the third type when the applied voltage was higher than 40 V