Changes of AM Fungal Abundance along Environmental Gradients in the Arid and Semi-Arid Grasslands of Northern China

Arbuscular mycorrhizal (AM) fungi are ubiquitous symbionts of higher plants in terrestrial ecosystems, while the occurrence of the AM symbiosis is influenced by a complex set of abiotic and biotic factors. To reveal the regional distribution pattern of AM fungi as driven by multiple environmental factors, and to understand the ecological importance of AM fungi in natural ecosystems, we conducted a field investigation on AM fungal abundance along environmental gradients in the arid and semi-arid grasslands of northern China. In addition to plant parameters recorded in situ, soil samples were collected, and soil chemo-physical and biological parameters were measured in the lab. Statistical analyses were performed to reveal the relative contribution of climatic, edaphic and vegetation factors to AM fungal abundance, especially for extraradical hyphal length density (HLD) in the soil. The results indicated that HLD were positively correlated with mean annual temperature (MAT), soil clay content and soil pH, but negatively correlated with both soil organic carbon (SOC) and soil available N. The multiple regressions and structural equation model showed that MAT was the key positive contributor and soil fertility was the key negative contributor to HLD. Furthermore, both the intraradical AM colonization (IMC) and relative abundance of AM fungi, which was quantified by real-time PCR assay, tended to decrease along the increasing SOC content. With regard to the obvious negative correlation between MAT and SOC in the research area, the positive correlation between MAT and HLD implied that AM fungi could potentially mitigate soil carbon losses especially in infertile soils under global warming. However, direct evidence from long-term experiments is still expected to support the AM fungal contribution to soil carbon pools

Integrity, Confidentiality, and Equity: Using Inquiry-Based Labs to help students understand AI and Cybersecurity

Recent advances in Artificial Intelligence (AI) have brought society closer to the long-held dream of creating machines to help with both common and complex tasks and functions. From recommending movies to detecting disease in its earliest stages, AI has become an aspect of daily life many people accept without scrutiny. Despite its functionality and promise, AI has inherent security risks that users should understand and programmers must be trained to address. The ICE (integrity, confidentiality, and equity) cybersecurity labs developed by a team of cybersecurity researchers addresses these vulnerabilities to AI models through a series of hands-on, inquiry-based labs. Through experimenting with and manipulating data models, students can experience firsthand how adversarial samples and bias can degrade the integrity, confidentiality, and equity of deep learning neural networks, as well as implement security measures to mitigate these vulnerabilities. This article addresses the pedagogical approach underpinning the ICE labs, and discusses both sample activities and technological considerations for teachers who want to implement these labs with their students

Dust-acoustic waves and stability in the permeating dusty plasma: I. Maxwellian distribution

The dust-acoustic waves and their stability in the permeating dusty plasma with the Maxwellian velocity distribution are investigated. We derive the dust-acoustic wave frequency and instability growth rate in two limiting physical cases that the thermal velocity of the flowing dusty plasma is (a) much larger than, and (b) much smaller than the phase velocity of the waves. We find that the stability of the waves depend strongly on the velocity of the flowing dusty plasma in the permeating dusty plasma. The numerical analyses are made based on the example that a cometary plasma tail is passing through the interplanetary space plasma. We show that, in case (a), the waves are generally unstable for any flowing velocity, but in case (b), the waves become unstable only when the wave number is small and the flowing velocity is large. When the physical conditions are between these two limiting cases, we gain a strong insight into the dependence of the stability criterions on the physical conditions in the permeating dusty plasma.Comment: 16 pages, 4 figures, 35 reference

Automated estimation of vector error correction models

Model selection and associated issues of post-model selection inference present well known challenges in empirical econometric research. These modeling issues are manifest in all applied work but they are particularly acute in multivariate time series settings such as cointegrated systems where multiple interconnected decisions can materially affect the form of the model and its interpretation. In cointegrated system modeling, empirical estimation typically proceeds in a stepwise manner that involves the determination of cointegrating rank and autoregressive lag order in a reduced rank vector autoregression followed by estimation and inference. This paper proposes an automated approach to cointegrated system modeling that uses adaptive shrinkage techniques to estimate vector error correction models with unknown cointegrating rank structure and unknown transient lag dynamic order. These methods enable simultaneous order estimation of the cointegrating rank and autoregressive order in conjunction with oracle-like efficient estimation of the cointegrating matrix and transient dynamics. As such they offer considerable advantages to the practitioner as an automated approach to the estimation of cointegrated systems. The paper develops the new methods, derives their limit theory, discusses implementation, reports simulations, and presents an empirical illustration with macroeconomic aggregates.</jats:p

SmartEmbed: A tool for clone and bug detection in smart contracts through structural code embedding

Ministry of Education, Singapore under its Academic Research Funding Tier 1authors' own version</p

Coupling and stacking order of ReS2 atomic layers revealed by ultralow-frequency Raman spectroscopy

We investigate the ultralow-frequency Raman response of atomically thin ReS2, a special type of two-dimensional (2D) semiconductors with unique distorted 1T structure. Bilayer and few-layer ReS2 exhibit rich Raman spectra at frequencies below 50 cm-1, where a panoply of interlayer shear and breathing modes are observed. The emergence of these interlayer phonon modes indicate that the ReS2 layers are coupled and stacked orderly, in contrast to the general belief that the ReS2 layers are decoupled from one another. While the interlayer breathing modes can be described by a linear chain model as in other 2D layered crystals, the shear modes exhibit distinctive behavior due to the in-plane lattice distortion. In particular, the two shear modes in bilayer ReS2 are non-degenerate and well separated in the Raman spectrum, in contrast to the doubly degenerate shear modes in other 2D materials. By carrying out comprehensive first-principles calculations, we can account for the frequency and Raman intensity of the interlayer modes, and determine the stacking order in bilayer ReS2

Revisiting Test Time Adaptation under Online Evaluation

This paper proposes a novel online evaluation protocol for Test Time Adaptation (TTA) methods, which penalizes slower methods by providing them with fewer samples for adaptation. TTA methods leverage unlabeled data at test time to adapt to distribution shifts. Though many effective methods have been proposed, their impressive performance usually comes at the cost of significantly increased computation budgets. Current evaluation protocols overlook the effect of this extra computation cost, affecting their real-world applicability. To address this issue, we propose a more realistic evaluation protocol for TTA methods, where data is received in an online fashion from a constant-speed data stream, thereby accounting for the method's adaptation speed. We apply our proposed protocol to benchmark several TTA methods on multiple datasets and scenarios. Extensive experiments shows that, when accounting for inference speed, simple and fast approaches can outperform more sophisticated but slower methods. For example, SHOT from 2020 outperforms the state-of-the-art method SAR from 2023 under our online setting. Our online evaluation protocol emphasizes the need for developing TTA methods that are efficient and applicable in realistic settings.Comment: 14 pages, 8 figures, 7 table