Fake News Detection via NLP is Vulnerable to Adversarial Attacks

News plays a significant role in shaping people's beliefs and opinions. Fake news has always been a problem, which wasn't exposed to the mass public until the past election cycle for the 45th President of the United States. While quite a few detection methods have been proposed to combat fake news since 2015, they focus mainly on linguistic aspects of an article without any fact checking. In this paper, we argue that these models have the potential to misclassify fact-tampering fake news as well as under-written real news. Through experiments on Fakebox, a state-of-the-art fake news detector, we show that fact tampering attacks can be effective. To address these weaknesses, we argue that fact checking should be adopted in conjunction with linguistic characteristics analysis, so as to truly separate fake news from real news. A crowdsourced knowledge graph is proposed as a straw man solution to collecting timely facts about news events.Comment: 11th International Conference on Agents and Artificial Intelligence (ICAART 2019

Terrestrial hydrological controls on land surface phenology of African savannas and woodlands

This paper presents a continental-scale phenological analysis of African savannas and woodlands. We apply an array of synergistic vegetation and hydrological data records from satellite remote sensing and model simulations to explore the influence of rainy season timing and duration on regional land surface phenology and ecosystem structure. We find that (i) the rainy season onset precedes and is an effective predictor of the growing season onset in African grasslands. (ii) African woodlands generally have early green-up before rainy season onset and have a variable delayed senescence period after the rainy season, with this delay correlated nonlinearly with tree fraction. These woodland responses suggest their complex water use mechanisms (either from potential groundwater use by relatively deep roots or stem-water reserve) to maintain dry season activity. (iii) We empirically find that the rainy season length has strong nonlinear impacts on tree fractional cover in the annual rainfall range from 600 to 1800 mm/yr, which may lend some support to the previous modeling study that given the same amount of total rainfall to the tree fraction may first increase with the lengthening of rainy season until reaching an “optimal rainy season length,” after which tree fraction decreases with the further lengthening of rainy season. This nonlinear response is resulted from compound mechanisms of hydrological cycle, fire, and other factors. We conclude that African savannas and deciduous woodlands have distinctive responses in their phenology and ecosystem functioning to rainy season. Further research is needed to address interaction between groundwater and tropical woodland as well as to explicitly consider the ecological significance of rainy season length under climate change

Mitochondrial Stress Engages E2F1 Apoptotic Signaling to Cause Deafness

SummaryMitochondrial dysfunction causes poorly understood tissue-specific pathology stemming from primary defects in respiration, coupled with altered reactive oxygen species (ROS), metabolic signaling, and apoptosis. The A1555G mtDNA mutation that causes maternally inherited deafness disrupts mitochondrial ribosome function, in part, via increased methylation of the mitochondrial 12S rRNA by the methyltransferase mtTFB1. In patient-derived A1555G cells, we show that 12S rRNA hypermethylation causes ROS-dependent activation of AMP kinase and the proapoptotic nuclear transcription factor E2F1. This retrograde mitochondrial-stress relay is operative in vivo, as transgenic-mtTFB1 mice exhibit enhanced 12S rRNA methylation in multiple tissues, increased E2F1 and apoptosis in the stria vascularis and spiral ganglion neurons of the inner ear, and progressive E2F1-dependent hearing loss. This mouse mitochondrial disease model provides a robust platform for deciphering the complex tissue specificity of human mitochondrial-based disorders, as well as the precise pathogenic mechanism of maternally inherited deafness and its exacerbation by environmental factors.PaperFlic

Track Performance in Tunnels and Rail Transition Areas with Under Tie Pads and Under Ballast Mats

Railroads have begun to use under tie pads (UTP) and under ballast mats (UBM) in rail track construction to reduce maintenance costs by better distributing loads, reducing the track modulus, and increasing ballast contact areas with ties. Locations such as tunnels, bridges, and bridge approaches are especially strong candidates for UTP and UBM use due to the high support stiffness they provide to the ballast. In this study, the University of Florida (UF) instrumented the Virginia Avenue Tunnel in Washington D.C., which uses UTP and UBM, during construction to monitor track pressure distribution, tie movement, and tunnel floor vibration during the first 20 months of use (July 2018 – February 2020). Track pressure distributions across ties were measured for hundreds of trains at the tunnel floor transition area and inside the tunnel. Measurements showed that the track settlement occurred over the first 6 months of measurement after track was opened, after which it stabilized to less than 0.157 in. (4 mm)

Avian Influenza A Virus (H5N1) Outbreaks, Kuwait, 2007

Phylogenetic analysis of influenza A viruses (H5N1) isolated from Kuwait in 2007 show that (H5N1) sublineage clade 2.2 viruses continue to spread across Europe, Africa, and the Middle East. Virus isolates were most closely related to isolates from central Asia and were likely vectored by migratory birds

Characterization of Avian Influenza Viruses A (H5N1) from Wild Birds, Hong Kong, 2004–2008

Repeated detection of subclade 2.3.2 viruses in nonpasserine birds from different regions suggests possible establishment of this lineage in wild bird species

The role of topography, soil, and remotely sensed vegetation condition towards predicting crop yield

Foreknowledge of the spatiotemporal drivers of crop yield would provide a valuable source of information to optimize on-farm inputs and maximize profitability. In recent years, an abundance of spatial data providing information on soils, topography, and vegetation condition have become available from both proximal and remote sensing platforms. Given the wide range of data costs (between USD $0−50/ha), it is important to understand where often limited financial resources should be directed to optimize field production. Two key questions arise. First, will these data actually aid in better fine-resolution yield prediction to help optimize crop management and farm economics? Second, what level of priority should stakeholders commit to in order to obtain these data? Before fully addressing these questions a remaining challenge is the complex nature of spatiotemporal yield variation. Here, a methodological framework is presented to separate the spatial and temporal components of crop yield variation at the subfield level. The framework can also be used to quantify the benefits of different data types on the predicted crop yield as well to better understand the connection of that data to underlying mechanisms controlling yield. Here, fine-resolution (10 m) datasets were assembled for eight 64 ha field sites, spanning a range of climatic, topographic, and soil conditions across Nebraska. Using Empirical Orthogonal Function (EOF) analysis, we found the first axis of variation contained 60–85 % of the explained variance from any particular field, thus greatly reducing the dimensionality of the problem. Using Multiple Linear Regression (MLR) and Random Forest (RF) approaches, we quantified that location within the field had the largest relative importance for modeling crop yield patterns. Secondary factors included a combination of vegetation condition, soil water content, and topography. With respect to predicting spatiotemporal crop yield patterns, we found the RF approach (prediction RMSE of 0.2−0.4 Mg/ha for maize) was superior to MLR (0.3−0.8 Mg/ha). While not directly comparable to MLR and RF the EOF approach had relatively low error (0.5–1.7 Mg/ha) and is intriguing as it requires few calibration parameters (2–6 used here) and utilizes the climate-based aridity index, allowing for pragmatic long-term predictions of subfield crop yield