EMaP: Explainable AI with Manifold-based Perturbations

In the last few years, many explanation methods based on the perturbations of input data have been introduced to improve our understanding of decisions made by black-box models. The goal of this work is to introduce a novel perturbation scheme so that more faithful and robust explanations can be obtained. Our study focuses on the impact of perturbing directions on the data topology. We show that perturbing along the orthogonal directions of the input manifold better preserves the data topology, both in the worst-case analysis of the discrete Gromov-Hausdorff distance and in the average-case analysis via persistent homology. From those results, we introduce EMaP algorithm, realizing the orthogonal perturbation scheme. Our experiments show that EMaP not only improves the explainers' performance but also helps them overcome a recently-developed attack against perturbation-based methods.Comment: 29 page

Wood-Burning Device Changeout: Modeling the Impact on PM 2.5

The effects of exchanging noncertified with certified wood-burning devices on the 24h-average PM2.5 concentrations in the nonattainment area of Fairbanks, Alaska, in a cold season (October to March) were investigated using the Weather Research and Forecasting model inline coupled with a chemistry package. Even changing out only 2930 uncertified woodstoves and 90 outdoor wood boilers reduced the 24 h-average PM2.5 concentrations on average by 0.6 μg.m−3 (6%) and avoided seven out of 55 simulated exceedance days during this half-a-year. The highest reductions on any exceedance day ranged between 1.7 and 2.8 μg.m−3. The relative response factors obtained were consistently relatively low (~0.95) for all PM2.5 species and all months. Sensitivity studies suggest that the assessment of the benefits of a wood-burning device changeout program in avoiding exceedances heavily relies on the accuracy of the estimates on how many wood-burning devices exist that can be exchanged

FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning

Federated learning (FL) enables a decentralized machine learning paradigm for multiple clients to collaboratively train a generalized global model without sharing their private data. Most existing works simply propose typical FL systems for single-modal data, thus limiting its potential on exploiting valuable multimodal data for future personalized applications. Furthermore, the majority of FL approaches still rely on the labeled data at the client side, which is limited in real-world applications due to the inability of self-annotation from users. In light of these limitations, we propose a novel multimodal FL framework that employs a semi-supervised learning approach to leverage the representations from different modalities. Bringing this concept into a system, we develop a distillation-based multimodal embedding knowledge transfer mechanism, namely FedMEKT, which allows the server and clients to exchange the joint knowledge of their learning models extracted from a small multimodal proxy dataset. Our FedMEKT iteratively updates the generalized global encoders with the joint embedding knowledge from the participating clients. Thereby, to address the modality discrepancy and labeled data constraint in existing FL systems, our proposed FedMEKT comprises local multimodal autoencoder learning, generalized multimodal autoencoder construction, and generalized classifier learning. Through extensive experiments on three multimodal human activity recognition datasets, we demonstrate that FedMEKT achieves superior global encoder performance on linear evaluation and guarantees user privacy for personal data and model parameters while demanding less communication cost than other baselines

Emissions of organic compounds from produced water ponds I: Characteristics and speciation

We measured fluxes of methane, a suite of non-methane hydrocarbons (C2–C11), light alcohols, and carbon dioxide from oil and gas produced water storage and disposal ponds in Utah (Uinta Basin) and Wyoming (Upper Green River Basin) United States during 2013–2016. In this paper, we discuss the characteristics of produced water composition and air-water fluxes, with a focus on flux chamber measurements. In companion papers, we will (1) report on inverse modeling methods used to estimate emissions from produced water ponds, including comparisons with flux chamber measurements, and (2) discuss the development of mass transfer coefficients to estimate emissions and place emissions from produced water ponds in the context of all regional oil and gas-related emissions. Alcohols (made up mostly of methanol) were the most abundant organic compound group in produced water (91% of total volatile organic concentration, with upper and lower 95% confidence levels of 89 and 93%) but accounted for only 34% (28 to 41%) of total organic compound fluxes from produced water ponds. Non-methane hydrocarbons, which are much less water-soluble than methanol and less abundant in produced water, accounted for the majority of emitted organics. C6–C9 alkanes and aromatics dominated hydrocarbon fluxes, perhaps because lighter hydrocarbons had already volatilized from produced water prior to its arrival in storage or disposal ponds, while heavier hydrocarbons are less water soluble and less volatile. Fluxes of formaldehyde and other carbonyls were low (1% (1 to 2%) of total organic compound flux). The speciation and magnitude of fluxes varied strongly across the facilities measured and with the amount of time water had been exposed to the atmosphere. The presence or absence of ice also impacted fluxes

Long-term outcomes of primary cardiac malignant tumors: Difference between African American and Caucasian population

BACKGROUND: The survival outcome for primary cardiac malignant tumors (PMCTs) based on race has yet to be fully elucidated in previously published literature. This study aimed to address the general long-term outcome and survival rate differences in PMCTs among African Americans and Caucasian populations. METHODS: The 18 cancer registries database from the Surveillance, Epidemiology, and End Results (SEER) Program from 1975 to 2016 were utilized. Ninety-four African American (AA) and 647 Caucasian (CAU) patients from the SEER registry were available for survival analysis. The log-rank test was used to compare the difference in mortality between two populations and presented by the Kaplan-Meier curves. A multivariate Cox proportional hazards regression was used to determine the independent predictors of all-cause mortality. RESULTS: The overall 30-day, 1-year, and 5-year survival rates were 74%, 44.3%, and 16.6%, respectively, with a median survival of 10 months. There was no significant difference in survival rate between the two races (p-value = 0.55). The 1-year survival rate improved significantly during the study timeline in the AA population (13.3% during 1975-1998, 40.9% during 1999-2004, 50% during 2005-2010, and 59.7% during 2011-2016, p-value = 0.0064). Age of diagnosis, type of tumor, disease stage, and chemotherapy administration are the main factors that predict survival outcomes of PMCT patients. Interactive nomogram was developed based on significant predictors. CONCLUSIONS: PMCTs have remained one of the most lethal diseases with poor survival outcome. Survival rate improved during the timeline in AA patients, but in general, racial differences in survival outcome were not observed

The impact of cataract surgey on vision-related quality of life for bilateral cataract patients in Ho Chi Minh City, Vietnam: a prospective study

BACKGROUND: To determine the impact of cataract surgery on vision-related quality of life (VRQOL) and examine the association between objective visual measures and change in VRQOL after surgery among bilateral cataract patients in Ho Chi Minh City, Vietnam. METHODS: A cohort of older patients with bilateral cataract was assessed one week before and one to three months after first eye or both eye cataract surgery. Visual measures including visual acuity, contrast sensitivity and stereopsis were obtained. Vision-related quality of life was assessed using the NEI VFQ-25. Descriptive analyses and a generalized linear estimating equation (GEE) analysis were undertaken to measure change in VRQOL after surgery. RESULTS: Four hundred and thirteen patients were assessed before cataract surgery and 247 completed the follow-up assessment one to three months after first or both eye cataract surgery. Overall, VRQOL significantly improved after cataract surgery (p < 0.001) particularly after both eye surgeries. Binocular contrast sensitivity (p < 0.001) and stereopsis (p < 0.001) were also associated with change in VRQOL after cataract surgery. Visual acuity was not associated with VRQOL. CONCLUSIONS: Cataract surgery significantly improved VRQOL among bilateral cataract patients in Vietnam. Contrast sensitivity as well as stereopsis, rather than visual acuity significantly affected VRQOL after cataract surgery

Behaviour-aware Malware Classification: Dynamic Feature Selection

Despite the continued advancements in security research, malware persists as being a major threat in this digital age. Malware detection is a primary defence strategy for most networks but the identification of malware strains is becoming increasingly difficult. Reliable identification is based upon characteristic features being detectable within an object. However, the limitations and expense of current malware feature extraction methods is significantly hindering this process. In this paper, we present a new method for identifying malware based on behavioural feature extraction. Our proposed method has been evaluated using seven classification methods whilst analysing 2,068 malware samples from eight different families. The results achieved thus far have demonstrated promising improvements over existing approaches

Quantum Criticality in Heavy Fermion Metals

Quantum criticality describes the collective fluctuations of matter undergoing a second-order phase transition at zero temperature. Heavy fermion metals have in recent years emerged as prototypical systems to study quantum critical points. There have been considerable efforts, both experimental and theoretical, which use these magnetic systems to address problems that are central to the broad understanding of strongly correlated quantum matter. Here, we summarize some of the basic issues, including i) the extent to which the quantum criticality in heavy fermion metals goes beyond the standard theory of order-parameter fluctuations, ii) the nature of the Kondo effect in the quantum critical regime, iii) the non-Fermi liquid phenomena that accompany quantum criticality, and iv) the interplay between quantum criticality and unconventional superconductivity.Comment: (v2) 39 pages, 8 figures; shortened per the editorial mandate; to appear in Nature Physics. (v1) 43 pages, 8 figures; Non-technical review article, intended for general readers; the discussion part contains more specialized topic

Motivation gaps and implementation traps:the paradoxical and time-varying effects of family ownership on firm absorptive capacity

We present a theoretical framework of family ownership as a driver of the heterogeneity (between‐firm differences) and variability (within‐firm differences over time) of absorptive capacity (AC). Building on our analysis of the multiple dimensions of family owner influence on firm behavior and the mechanisms that can shape the firm willingness and ability to acquire, assimilate, transform, and exploit external knowledge, we introduce the concepts of motivation gap and implementation gap to explain why, paradoxically, family ownership can cause both upward and downward divergences in AC. Our contingency framework identifies conditions under which the positive and negative effects of family ownership on AC are likely to prevail and adds a temporal perspective suggesting that AC varies depending on the duration of family ownership and ownership succession