Secure location-aware communications in energy-constrained wireless networks

Wireless ad hoc network has enabled a variety of exciting civilian, industrial and military applications over the past few years. Among the many types of wireless ad hoc networks, Wireless Sensor Networks (WSNs) has gained popularity because of the technology development for manufacturing low-cost, low-power, multi-functional motes. Compared with traditional wireless network, location-aware communication is a very common communication pattern and is required by many applications in WSNs. For instance, in the geographical routing protocol, a sensor needs to know its own and its neighbors\u27 locations to forward a packet properly to the next hop. The application-aware communications are vulnerable to many malicious attacks, ranging from passive eavesdropping to active spoofing, jamming, replaying, etc. Although research efforts have been devoted to secure communications in general, the properties of energy-constrained networks pose new technical challenges: First, the communicating nodes in the network are always unattended for long periods without physical maintenance, which makes their energy a premier resource. Second, the wireless devices usually have very limited hardware resources such as memory, computation capacity and communication range. Third, the number of nodes can be potentially of very high magnitude. Therefore, it is infeasible to utilize existing secure algorithms designed for conventional wireless networks, and innovative mechanisms should be designed in a way that can conserve power consumption, use inexpensive hardware and lightweight protocols, and accommodate with the scalability of the network. In this research, we aim at constructing a secure location-aware communication system for energy-constrained wireless network, and we take wireless sensor network as a concrete research scenario. Particularly, we identify three important problems as our research targets: (1) providing correct location estimations for sensors in presence of wormhole attacks and pollution attacks, (2) detecting location anomalies according to the application-specific requirements of the verification accuracy, and (3) preventing information leakage to eavesdroppers when using network coding for multicasting location information. Our contributions of the research are as follows: First, we propose two schemes to improve the availability and accuracy of location information of nodes. Then, we study monitoring and detection techniques and propose three lightweight schemes to detect location anomalies. Finally, we propose two network coding schemes which can effectively prevent information leakage to eavesdroppers. Simulation results demonstrate the effectiveness of our schemes in enhancing security of the system. Compared to previous works, our schemes are more lightweight in terms of hardware cost, computation overhead and communication consumptions, and thus are suitable for energy-constrained wireless networks

Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views

Background: View planning for the acquisition of cardiac magnetic resonance (CMR) imaging remains a demanding task in clinical practice. Purpose: Existing approaches to its automation relied either on an additional volumetric image not typically acquired in clinic routine, or on laborious manual annotations of cardiac structural landmarks. This work presents a clinic-compatible, annotation-free system for automatic CMR view planning. Methods: The system mines the spatial relationship, more specifically, locates the intersecting lines, between the target planes and source views, and trains deep networks to regress heatmaps defined by distances from the intersecting lines. The intersection lines are the prescription lines prescribed by the technologists at the time of image acquisition using cardiac landmarks, and retrospectively identified from the spatial relationship. As the spatial relationship is self-contained in properly stored data, the need for additional manual annotation is eliminated. In addition, the interplay of multiple target planes predicted in a source view is utilized in a stacked hourglass architecture to gradually improve the regression. Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers. Results: The experiments include 181 CMR exams. Our system yields the mean angular difference and point-to-plane distance of 5.68 degrees and 3.12 mm, respectively. It not only achieves superior accuracy to existing approaches including conventional atlas-based and newer deep-learning-based in prescribing the four standard CMR planes but also demonstrates prescription of the first cardiac-anatomy-oriented plane(s) from the body-oriented scout.Comment: Medical Physics. arXiv admin note: text overlap with arXiv:2109.1171

Dirac-Surface-State Modulated Spin Dynamics in a Ferrimagnetic Insulator at Room Temperature

This work demonstrates dramatically modified spin dynamics of magnetic insulator (MI) by the spin-momentum locked Dirac surface states of the adjacent topological insulator (TI) which can be harnessed for spintronic applications. As the Bi-concentration x is systematically tuned in 5 nm thick (BixSb1-x)2Te3 TI film, the weight of the surface relative to bulk states peaks at x = 0.32 when the chemical potential approaches the Dirac point. At this concentration, the Gilbert damping constant of the precessing magnetization in 10 nm thick Y3Fe5O12 MI film in the MI/TI heterostructures is enhanced by an order of magnitude, the largest among all concentrations. In addition, the MI acquires additional strong magnetic anisotropy that favors the in-plane orientation with similar Bi-concentration dependence. These extraordinary effects of the Dirac surface states distinguish TI from other materials such as heavy metals in modulating spin dynamics of the neighboring magnetic layer

Global Pattern and Change of Cropland Soil Organic Carbon during 1901-2010: Roles of Climate, Atmospheric Chemistry, Land Use and Management

Soil organic carbon (SOC) in croplands is a key property of soil quality for ensuring food security and agricultural sustainability, and also plays a central role in the global carbon (C) budget. When managed sustainably, soils may play a critical role in mitigating climate change by sequestering C and decreasing greenhouse gas emissions into the atmosphere. However, the magnitude and spatio-temporal patterns of global cropland SOC are far from well constrained due to high land surface heterogeneity, complicated mechanisms, and multiple influencing factors. Here, we use a process-based agroecosystem model (DLEM-Ag) in combination with diverse spatially-explicit gridded environmental data to quantify the long-term trend of SOC storage in global cropland area during 1901-2010 and identify the relative impacts of climate change, elevated CO2, nitrogen deposition, land cover change, and land management practices such as nitrogen fertilizer use and irrigation. Model results show that the total SOC and SOC density in the 2000s increased by 125% and 48.8%, respectively, compared to the early 20th century. This SOC increase was primarily attributed to cropland expansion and nitrogen fertilizer use. Factorial analysis suggests that climate change reduced approximately 3.2% (or 2,166 Tg C) of the total SOC over the past 110 years. Our results indicate that croplands have a large potential to sequester C through implementing better land use management practices, which may partially offset SOC loss caused by climate change

Explaining Pulsar Timing Array Observations with Primordial Gravitational Waves in Parity-Violating Gravity

The pulsar timing array (PTA) collaborations have recently suggested the presence of a gravitational wave background at nano-Hertz frequencies. In this paper, we explore potential inflationary interpretation of this signal within the context of a simple and health parity-violating gravity model termed the Nieh-Yan modified Teleparallel Gravity. Through this model, two inflationary scenarios are evaluated, both yielding significant polarized primordial gravitational waves (PGWs) that align well with the results from PTA observations. Furthermore, the resulting PGWs can display strong circular polarization and significant anisotropies in the PTA frequency band, which are distinct features to be verified by observations of both PTA and the cosmic microwave background.The detection of such a distinctive background of PGWs is expected to provide strong evidence supporting our scenarios and insights into inflationary dynamics and gravity theory.Comment: 9 pages, 8 figure

Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing

In the context-dependent Text-to-SQL task, the generated SQL statements are refined iteratively based on the user input utterance from each interaction. The input text from each interaction can be viewed as component modifications to the previous SQL statements, which could be further extracted as the modification patterns. Since these modification patterns could also be combined with other SQL statements, the models are supposed to have the compositional generalization to these novel combinations. This work is the first exploration of compositional generalization in context-dependent Text-to-SQL scenarios. To facilitate related studies, we constructed two challenging benchmarks named \textsc{CoSQL-CG} and \textsc{SParC-CG} by recombining the modification patterns and existing SQL statements. The following experiments show that all current models struggle on our proposed benchmarks. Furthermore, we found that better aligning the previous SQL statements with the input utterance could give models better compositional generalization ability. Based on these observations, we propose a method named \texttt{p-align} to improve the compositional generalization of Text-to-SQL models. Further experiments validate the effectiveness of our method. Source code and data are available.Comment: Accepted to ACL 2023 (Findings), Long Paper, 11 page

mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to joint learning of multimodal images. However, in clinical practice, it is not always possible to acquire a complete set of MRIs, and the problem of missing modalities causes severe performance degradation in existing multimodal segmentation methods. In this work, we present the first attempt to exploit the Transformer for multimodal brain tumor segmentation that is robust to any combinatorial subset of available modalities. Concretely, we propose a novel multimodal Medical Transformer (mmFormer) for incomplete multimodal learning with three main components: the hybrid modality-specific encoders that bridge a convolutional encoder and an intra-modal Transformer for both local and global context modeling within each modality; an inter-modal Transformer to build and align the long-range correlations across modalities for modality-invariant features with global semantics corresponding to tumor region; a decoder that performs a progressive up-sampling and fusion with the modality-invariant features to generate robust segmentation. Besides, auxiliary regularizers are introduced in both encoder and decoder to further enhance the model's robustness to incomplete modalities. We conduct extensive experiments on the public BraTS $2018$ dataset for brain tumor segmentation. The results demonstrate that the proposed mmFormer outperforms the state-of-the-art methods for incomplete multimodal brain tumor segmentation on almost all subsets of incomplete modalities, especially by an average 19.07% improvement of Dice on tumor segmentation with only one available modality. The code is available at https://github.com/YaoZhang93/mmFormer.Comment: Accepted to MICCAI 202

Responses of soil carbon sequestration to climate-smart agriculture practices: A meta-analysis

Climate-smart agriculture (CSA) management practices (e.g., conservation tillage, cover crops, and biochar applications) have been widely adopted to enhance soil organic carbon (SOC) sequestration and to reduce greenhouse gas emissions while ensuring crop productivity. However, current measurements regarding the influences of CSA management practices on SOC sequestration diverge widely, making it difficult to derive conclusions about individual and combined CSA management effects and bringing large uncertainties in quantifying the potential of the agricultural sector to mitigate climate change. We conducted a meta-analysis of 3,049 paired measurements from 417 peer-reviewed articles to examine the effects of three common CSA management practices on SOC sequestration as well as the environmental controlling factors. We found that, on average, biochar applications represented the most effective approach for increasing SOC content (39%), followed by cover crops (6%) and conservation tillage (5%). Further analysis suggested that the effects of CSA management practices were more pronounced in areas with relatively warmer climates or lower nitrogen fertilizer inputs. Our meta-analysis demonstrated that, through adopting CSA practices, cropland could be an improved carbon sink. We also highlight the importance of considering local environmental factors (e.g., climate and soil conditions and their combination with other management practices) in identifying appropriate CSA practices for mitigating greenhouse gas emissions while ensuring crop productivity