Interaction-aware Factorization Machines for Recommender Systems

Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless feature may introduce noises; the importance of a feature may also differ when interacting with different features. In this work, we propose a novel model named \emph{Interaction-aware Factorization Machine} (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the \emph{feature aspect} and the \emph{field aspect}, to learn flexible interactions on two levels. The feature aspect learns feature interaction importance via an attention network while the field aspect learns the feature interaction effect as a parametric similarity of the feature interaction vector and the corresponding field interaction prototype. IFM introduces more structured control and learns feature interaction importance in a stratified manner, which allows for more leverage in tweaking the interactions on both feature-wise and field-wise levels. Besides, we give a more generalized architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to capture higher-order interactions. To further improve both the performance and efficiency of IFM, a sampling scheme is developed to select interactions based on the field aspect importance. The experimental results from two well-known datasets show the superiority of the proposed models over the state-of-the-art methods

Impact of Lockdown on Air Pollution: Evidence from the “2+26” Cities in the Beijing-Tianjin-Hebei Region

To prevent the spread of COVID-19 in China, many cities were locked down after January 23, 2020. Based on the panel data of the “2+26” cities from 10 January to 15 March 2020, this paper took the lockdown as a quasi-natural experiment and established a multi-phase DID model to investigate whether the lockdown measures significantly reduced air pollution in locked-down cities in the Beijing-Tianjin-Hebei (BTH) region. The core innovation of this paper is that we considered the urban immigration scale index as a mediating variable , which is rarely adopted in the existing literature, and we identified the relationships between the lockdown, the intracity migration index, the urban immigration scale index and air pollution. The results showed that compared with the non-locked-down cities, the lockdown significantly reduced air pollution. Furthermore, it was found that the lockdown reduced air pollution by reducing intracity migration and the urban scale of immigration. Moreover, compared with the corresponding period in 2019, air pollution was significantly reduced in the locked-down cities of the “2+26” cities. Air pollution is closely related to human activity, and green production and technological innovations are critical for reducing air pollution in the BTH region

Distribution of polycyclic aromatic hydrocarbons in subcellular root tissues of ryegrass (Lolium multiflorum Lam.)

BACKGROUND: Because of the increasing quantity and high toxicity to humans of polycyclic aromatic hydrocarbons (PAHs) in the environment, several bioremediation mechanisms and protocols have been investigated to restore PAH-contaminated sites. The transport of organic contaminants among plant cells via tissues and their partition in roots, stalks, and leaves resulting from transpiration and lipid content have been extensively investigated. However, information about PAH distributions in intracellular tissues is lacking, thus limiting the further development of a mechanism-based phytoremediation strategy to improve treatment efficiency. RESULTS: Pyrene exhibited higher uptake and was more recalcitrant to metabolism in ryegrass roots than was phenanthrene. The kinetic processes of uptake from ryegrass culture medium revealed that these two PAHs were first adsorbed onto root cell walls, and they then penetrated cell membranes and were distributed in intracellular organelle fractions. At the beginning of uptake (< 50 h), adsorption to cell walls dominated the subcellular partitioning of the PAHs. After 96 h of uptake, the subcellular partition of PAHs approached a stable state in the plant water system, with the proportion of PAH distributed in subcellular fractions being controlled by the lipid contents of each component. Phenanthrene and pyrene primarily accumulated in plant root cell walls and organelles, with about 45% of PAHs in each of these two fractions, and the remainder was retained in the dissolved fraction of the cells. Because of its higher lipophilicity, pyrene displayed greater accumulation factors in subcellular walls and organelle fractions than did phenanthrene. CONCLUSIONS: Transpiration and the lipid content of root cell fractions are the main drivers of the subcellular partition of PAHs in roots. Initially, PAHs adsorb to plant cell walls, and they then gradually diffuse into subcellular fractions of tissues. The lipid content of intracellular components determines the accumulation of lipophilic compounds, and the diffusion rate is related to the concentration gradient established between cell walls and cell organelles. Our results offer insights into the transport mechanisms of PAHs in ryegrass roots and their diffusion in root cells

A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification

Traffic classification, i.e. the identification of the type of applications flowing in a network, is a strategic task for numerous activities (e.g., intrusion detection, routing). This task faces some critical challenges that current deep learning approaches do not address. The design of current approaches do not take into consideration the fact that networking hardware (e.g., routers) often runs with limited computational resources. Further, they do not meet the need for faithful explainability highlighted by regulatory bodies. Finally, these traffic classifiers are evaluated on small datasets which fail to reflect the diversity of applications in real-world settings. Therefore, this paper introduces a Lightweight, Efficient and eXplainable-by-design convolutional neural network (LEXNet) for Internet traffic classification, which relies on a new residual block (for lightweight and efficiency purposes) and prototype layer (for explainability). Based on a commercial-grade dataset, our evaluation shows that LEXNet succeeds to maintain the same accuracy as the best performing state-of-the-art neural network, while providing the additional features previously mentioned. Moreover, we illustrate the explainability feature of our approach, which stems from the communication of detected application prototypes to the end-user, and we highlight the faithfulness of LEXNet explanations through a comparison with post hoc methods

Cross-network transferable neural models for WLAN interference estimation

Airtime interference is a key performance indicator for WLANs, measuring, for a given time period, the percentage of time during which a node is forced to wait for other transmissions before to transmitting or receiving. Being able to accurately estimate interference resulting from a given state change (e.g., channel, bandwidth, power) would allow a better control of WLAN resources, assessing the impact of a given configuration before actually implementing it. In this paper, we adopt a principled approach to interference estimation in WLANs. We first use real data to characterize the factors that impact it, and derive a set of relevant synthetic workloads for a controlled comparison of various deep learning architectures in terms of accuracy, generalization and robustness to outlier data. We find, unsurprisingly, that Graph Convolutional Networks (GCNs) yield the best performance overall, leveraging the graph structure inherent to campus WLANs. We notice that, unlike e.g. LSTMs, they struggle to learn the behavior of specific nodes, unless given the node indexes in addition. We finally verify GCN model generalization capabilities, by applying trained models on operational deployments unseen at training time

Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation

Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL). Yet, the vast majority of TC literature lacks code artifacts, performance assessments across datasets and reference comparisons against Machine Learning (ML) methods. Among those works, a recent study from IMC22 [16] is worth of attention since it adopts recent DL methodologies (namely, few-shot learning, self-supervision via contrastive learning and data augmentation) appealing for networking as they enable to learn from a few samples and transfer across datasets. The main result of [16] on the UCDAVIS19, ISCX-VPN and ISCX-Tor datasets is that, with such DL methodologies, 100 input samples are enough to achieve very high accuracy using an input representation called "flowpic" (i.e., a per-flow 2d histograms of the packets size evolution over time). In this paper (i) we reproduce [16] on the same datasets and (ii) we replicate its most salient aspect (the importance of data augmentation) on three additional public datasets (MIRAGE19, MIRAGE22 and UTMOBILENET21). While we confirm most of the original results, we also found a 20% accuracy drop on some of the investigated scenarios due to a data shift in the original dataset that we uncovered. Additionally, our study validates that the data augmentation strategies studied in [16] perform well on other datasets too. In the spirit of reproducibility and replicability we make all artifacts (code and data) available to the research community at https://tcbenchstack.github.io/tcbench/Comment: to appear at ACM Internet Traffic Measurement (IMC) 2023, replication trac

Global Existence and Blow-Up for a Chemotaxis System

In this paper we consider a Keller-Segel-type chemotaxis model with reaction term under no-flux boundary conditions, where the kinetics term of the system is power function. Assuming some growth conditions, the existence of bounded global strong solution to the parabolic-parabolic system is given. We also give the numerical test and find out that there exists a threshold. When the power frequency greater than the threshold, both global solution and blow-up solution exist

The Sirtuin-2 Inhibitor AK7 Is Neuroprotective in Models of Parkinson’s Disease but Not Amyotrophic Lateral Sclerosis and Cerebral Ischemia

Sirtuin deacetylases regulate diverse cellular pathways and influence disease processes. Our previous studies identified the brain-enriched sirtuin-2 (SIRT2) deacetylase as a potential drug target to counteract neurodegeneration. In the present study, we characterize SIRT2 inhibition activity of the brain-permeable compound AK7 and examine the efficacy of this small molecule in models of Parkinson’s disease, amyotrophic lateral sclerosis and cerebral ischemia. Our results demonstrate that AK7 is neuroprotective in models of Parkinson’s disease; it ameliorates alpha-synuclein toxicity in vitro and prevents 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced dopamine depletion and dopaminergic neuron loss in vivo. The compound does not show beneficial effects in mouse models of amyotrophic lateral sclerosis and cerebral ischemia. These findings underscore the specificity of protective effects observed here in models of Parkinson’s disease, and previously in Huntington’s disease, and support the development of SIRT2 inhibitors as potential therapeutics for the two neurodegenerative diseases

Modeling Rett Syndrome Using TALEN-Edited MECP2 Mutant Cynomolgus Monkeys

Gene-editing technologies have made it feasible to create nonhuman primate models for human genetic disorders. Here, we report detailed genotypes and phenotypes of TALEN-edited MECP2 mutant cynomolgus monkeys serving as a model for a neurodevelopmental disorder, Rett syndrome (RTT), which is caused by loss-of-function mutations in the human MECP2 gene. Male mutant monkeys were embryonic lethal, reiterating that RTT is a disease of females. Through a battery of behavioral analyses, including primate-unique eye-tracking tests, in combination with brain imaging via MRI, we found a series of physiological, behavioral, and structural abnormalities resembling clinical manifestations of RTT. Moreover, blood transcriptome profiling revealed that mutant monkeys resembled RTT patients in immune gene dysregulation. Taken together, the stark similarity in phenotype and/or endophenotype between monkeys and patients suggested that gene-edited RTT founder monkeys would be of value for disease mechanistic studies as well as development of potential therapeutic interventions for RTT

The impact of different benefit packages of Medical Financial Assistance Scheme on health service utilization of poor population in Rural China

<p>Abstract</p> <p>Background</p> <p>Since 2003 and 2005, National Pilot Medical Financial Assistance Scheme (MFA) has been implemented in rural and urban areas of China to improve the poorest families' accessibility to health services. Local governments of the pilot areas formulated various benefit packages. Comparative evaluation research on the effect of different benefit packages is urgently needed to provide evidence for improving policy-making of MFA. This study was based on a MFA pilot project, which was one component of Health VIII Project conducted in rural China. This article aimed to compare difference in health services utilization of poor families between two benefit package project areas: H8 towns (package covering inpatient service, some designated preventive and curative health services but without out-patient service reimbursement in Health VIII Project,) and H8SP towns (package extending coverage of target population, covering out- patient services and reducing co-payment rate in Health VIII Supportive Project), and to find out major influencing factors on their services utilization.</p> <p>Methods</p> <p>A cross-sectional survey was conducted in 2004, which used stratified cluster sampling method to select poor families who have been enrolled in MFA scheme in rural areas of ChongQing. All family members of the enrolled households were interviewed. 748 and 1129 respondents from two kinds of project towns participated in the survey. Among them, 625 and 869 respondents were included (age≥15) in the analysis of this study. Two-level linear multilevel model and binomial regressions with a log link were used to assess influencing factors on different response variables measuring service utilization.</p> <p>Results</p> <p>In general, there was no statistical significance in physician visits and hospitalizations among all the respondents between the two kinds of benefit package towns. After adjusting for major confounding factors, poor families in H8SP towns had much higher frequency of MFA use (β = 1.17) and less use of hospitalization service (OR = 0.7 (H8SP/H8), 95%CI (0.5, 1.0)) among all the respondents. While calculating use of hospital services among those who needed, there was significant difference (p = 0.032) in percentage of hospitalization use between H8SP towns (46%) and H8 towns (33%). Meanwhile, the non-use but ought-to-use hospitalization ratio of H8SP (54%) was lower than that of H8 (67 %) towns. This indicated that hospitalization utilizations had improved in H8SP towns among those who needed. Awareness of MFA detailed benefit package and presence of physician diagnosed chronic disease had significant association with frequency of MFA use and hospitalizations. There was no significant difference in rate of borrowing money for illness treatment between the two project areas. Large amount of medical debt had strong association with hospitalization utilization.</p> <p>Conclusions</p> <p>The new extended benefit package implemented in pilot towns significantly increased the poor families' accessibility to MFA package in H8SP than that of H8 towns, which reduced poor families' demand of hospitalization services for their chronic diseases, and improved the poor population's utilization of out-patient services to some degree. It can encourage poor people to use more outpatient services thus reduce their hospitalization need. Presence of chronic disease and hospitalization had strong association with the presence of large amount of medical debt, which indicated that: although establishment of MFA had facilitated accessibility of poor families to this new system, and improved service utilization of poor families to some degree, but its role in reducing poor families' medical debt resulted from chronic disease and hospitalization was still very limited. Besides, the following requirements of MFA: co-payment for in-patient services, ceiling and deductibles for reimbursement, limitations on eligibility for diseases reimbursement, also served as most important obstacles for poor families' access to health care.</p> <p>Therefore, there is great need to improve MFA benefit package design in the future, including extending to cover out-patient services, raising ceiling for reimbursement, removing deductibles of MFA, reducing co-payment rate, and integrating MFA with New Rural Cooperative Medical Scheme more closely so as to provide more protection to the poor families.</p