Using online linear classifiers to filter spam Emails

The performance of two online linear classifiers - the Perceptron and Littlestone’s Winnow – is explored for two anti-spam filtering benchmark corpora - PU1 and Ling-Spam. We study the performance for varying numbers of features, along with three different feature selection methods: Information Gain (IG), Document Frequency (DF) and Odds Ratio. The size of the training set and the number of training iterations are also investigated for both classifiers. The experimental results show that both the Perceptron and Winnow perform much better when using IG or DF than using Odds Ratio. It is further demonstrated that when using IG or DF, the classifiers are insensitive to the number of features and the number of training iterations, and not greatly sensitive to the size of training set. Winnow is shown to slightly outperform the Perceptron. It is also demonstrated that both of these online classifiers perform much better than a standard Naïve Bayes method. The theoretical and implementation computational complexity of these two classifiers are very low, and they are very easily adaptively updated. They outperform most of the published results, while being significantly easier to train and adapt. The analysis and promising experimental results indicate that the Perceptron and Winnow are two very competitive classifiers for anti-spam filtering

Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia

Vegetation changes play a vital role in modifying local temperatures although, until now, the climate feedback effects of vegetation changes are still poorly known and large uncertainties exist, especially over Central Asia. In this study, using remote sensing and re-analysis of existing data, we evaluated the impact of vegetation changes on local temperatures. Our results indicate that vegetation changes have a significant unidirectional causality relationship with regard to local temperature changes. We found that vegetation greening over Central Asia as a whole induced a cooling effect on the local temperatures. We also found that evapotranspiration (ET) exhibits greater sensitivity to the increases of the Normalized Difference Vegetation Index (NDVI) as compared to albedo in arid/semi-arid/semi-humid regions, potentially leading to a cooling effect. However, in humid regions, albedo warming completely surpasses ET cooling, causing a pronounced warming. Our findings suggest that using appropriate strategies to protect vulnerable dryland ecosystems from degradation, should lead to future benefits related to greening ecosystems and mitigation for rising temperatures

Investigations of pressurized Lu-N-H materials by using the hybrid functional

Recently, Lu-N-H materials were reported to have room-temperature superconductivity, and the Hubbard U correction on the Lu's $\textit{f}$-electrons is necessary, and a constant U = 5.5 eV was applied to different Lu-N-H configurations (Nature 615, 244 (2023)). Following simulations indicate that the superconducting transition temperature (T$_{c}$) of LuH$_{3}$ with U = 0 eV is 50 ~ 60 K, while the N-doped LuH$_{3}$ is below 30 K. Quite recently, calculations with U = 5 eV shows that the T$_{c}$ of N-doped LuH$_{3}$ exceeds 100 K. The properties of Lu-N-H are sensitive to the applied U values. Here, the structural and electronic Lu-N-H properties at high-pressure (0 ~ 10 GPa) are systematically investigated based on the hybrid functional. We show that different Lu-N-H configurations should possess different U values varying from 6.4 eV to 7.4 eV. Furthermore, at pressure ranging from 0 GPa to 1 GPa, the $\textit{f}$ and $\textit{d}$ band centers of N-doped LuH$_{3}$ show oscillation or even plateau, and the band gap of insulators also shows a platform near this pressure, this is consistent with the pressure range where room-temperature superconductivity appeared in Lu-N-H. Our work provides insights into the understanding of Lu-N-H materials and other hydrogen-rich superconductors based on the rare-earth elements

Magnetic properties of pressurized CsV3_{3}Sb5_{5} calculated by using a hybrid functional

Based on the hybrid functional, we find that at 0 GPa, the pristine CsV$_{3}$Sb$_{5}$ has local magnetic moment of 0.85 $\mu_B$ /unit cell, which is suppressed at pressure of 2.5 GPa resulting in a spin-crossover. Since the ground sate of CsV$_{3}$Sb$_{5}$ with charge density wave (CDW) distortion is non-magnetic state, the local magnetic moment of pristine CsV$_{3}$Sb$_{5}$ will be suppressed by temperature-induced CDW transition at 94 K. The schematic evolution of magnetic moments as functions of pressure and temperature are presented. At low temperature, CsV$_{3}$Sb$_{5}$ is a rare example of materials hosting pressureinduced local magnetic moment, and we suggeste that the effects of local magnetic moments should be considered for understanding its properties

Backward magnetostatic surface spin waves in exchange coupled Co/FeNi bilayers

Propagation of backward magnetostatic surface spin waves (SWs) in exchange coupled Co/FeNi bilayers are studied by using Brillouin light scattering (BLS) technique. Two types of SWs modes were identified in our BLS measurements. They are magnetostatic surface waves (MSSWs) mode and perpendicular standing spin waves (PSSWs) mode. The dispersion relations of MSSWs obtained from the Stokes and Anti-Stokes measurements display respectively positive and negative group velocities. The Anti-Stokes branch with positive phase velocities and negative group velocities, known as backward magnetostatic surface mode originates from the magnetostatic interaction of the bilayer. The experimental data are in good agreement with the theoretical calculations. Our results are useful for understanding the SWs propagation and miniaturizing SWs storage devices

Characterization, sub-cellular localization and expression profiling of the isoprenylcysteine methylesterase gene family in Arabidopsis thaliana

Background: Isoprenylcysteine methylesterases (ICME) demethylate prenylated protein in eukaryotic cell. Until now, knowledge about their molecular information, localization and expression pattern is largely unavailable in plant species. One ICME in Arabidopsis, encoded by At5g15860, has been identified recently. Over-expression of At5g15860 caused an ABA hypersensitive phenotype in transgenic Arabidopsis plants, indicating that it functions as a positive regulator of ABA signaling. Moreover, ABA induced the expression of this gene in Arabidopsis seedlings. The current study extends these findings by examining the sub-cellular localization, expression profiling, and physiological functions of ICME and two other ICME-like proteins, ICME-LIKE1 and ICME-LIKE2, which were encoded by two related genes At1g26120 and At3g02410, respectively. Results: Bioinformatics investigations showed that the ICME and other two ICME-like homologs comprise a small subfamily of carboxylesterase (EC 3.1.1.1) in Arabidopsis. Sub-cellular localization of GFP tagged ICME and its homologs showed that the ICME and ICME-like proteins are intramembrane proteins predominantly localizing in the endoplasmic reticulum (ER) and Golgi apparatus. Semi-quantitative and real-time quantitative PCR revealed that the ICME and ICME-like genes are expressed in all examined tissues, including roots, rosette leaves, cauline leaves, stems, flowers, and siliques, with differential expression levels. Within the gene family, the base transcript abundance of ICME-LIKE2 gene is very low with higher expression in reproductive organs (flowers and siliques). Time-course analysis uncovered that both ICME and ICME-like genes are up-regulated by mannitol, NaCl and ABA treatment, with ICME showing the highest level of up-regulation by these treatments. Heat stress resulted in up-regulation of the ICME gene significantly but down-regulation of the ICME-LIKE1 and ICME-LIKE2 genes. Cold and dehydration stimuli led to no significant change of both ICME and ICME-like gene expression. Mutant icme-like2-1 showed increased sensitivity to ABA but slightly decreased sensitivity to salt and osmotic stresses during seed germination. Conclusions: It is concluded that the ICME family is involved in stress and ABA signaling in Arabidopsis, probably through mediating the process of demethylating prenylated proteins. Identification of these prenylated proteins will help to better understand the significance of protein prenylation in Planta

Skill Demands in the Audit Labor Market: Evidence from Job Postings

This study examines how the demand for auditor skill sets has changed over the past decade as well as how these changes relate to audit quality and audit fees. Using a novel dataset that contains the near-universe of online job postings by accounting firms from 2007 to 2017, we find that audit firms have decreased their demand for auditors and increased their demand for IT-related personnel. We also find that audit firms are increasingly demanding expanded skill sets from their auditors—the portion of cognitive, social, and IT-related skills has increased over our sample period whereas financial skills have remained relatively flat. Further, we find substantial variation in the demand for skills not only across audit firms, but also across offices within an audit firm. More importantly, these differences in skill requirements have a significant effect on audit quality—specifically, audit offices that demand more social and cognitive skills are less likely to have clients experience subsequent restatements. Taken together, our findings provide new insights on the changing dynamics of the auditor labor market and their relations to audit quality

A Modified Meta-Learner for Few-Shot Learning

Meta-Learning, or so-called Learning to learn, has become another important research branch in Machine Learning. Different from traditional deep learning, meta-learning can be used to solve one-to-many problems and has a better performance in few-shot learning which only few samples are available in each class. In these tasks, meta-learning is designed to quickly form a relatively reliable model through very limited samples. In this paper, we propose a modified LSTM-based meta-learning model, which can initialize and update the parameters of classifier (learner) considering both short-term knowledge of one task and long-term knowledge across multiple tasks. We reconstruct a Compound loss function to make up for the deficiency caused by the separate one in original model aiming for a quick start and better stability, without taking expensive operation. Our modification enables meta-learner to perform better under few-updates. Experiments conducted on the Mini-ImageNet demonstrate the improved accuracies