Miracle-dz/UniFAR-FTUs huggingface dataset:
Topographic Evolution Involving Co-Seismic Landslide, Deformation, Long-Term Folding and Isostatic Rebound: A Case Study on the 2004 Chuetsu Earthquake

Co-seismic landslide volume information is critical to understanding the role of strong earthquakes in topographic and geological evolution. The availability of both preand post-earthquake high-resolution digital elevation models (DEMs) provides us with the opportunity to develop a new approach to obtain robust landslide volume information. Here, we propose a method for landslide volume estimation and test it in the Chuetsu region, where a Mw 6.6 earthquake occurred in 2004. First, we align the DEMs by reconstructing the horizontal difference. Then, we quantitatively obtain the landslide volume in the epicentral area by differencing the preand post-earthquake DEMs. We convert the landslide volume into the distribution of average catchment-scale denudation and the resulting long-term crustal rebound. Our findings reveal that the Chuetsu earthquake mainly roughens the topography in the low-elevation Chuetsu region. Our results indicate that the preserved topography not only is due to the uplift caused by fault-related folding on the hanging wall of the Muikamachi fault but also undergoes erosion caused by seismically induced landslides and crustal rebound also modifies the topography in the long term. This study confirms that the differential DEM method is a valuable approach for quantitative analysis of topographic and geological evolution.
Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction

Most of the existing approaches to the extraction of buildings from high-resolution orthoimages consider the problem as semantic segmentation, which extracts a pixel-wise mask for buildings and trains end-to-end with manually labeled building maps. However, as buildings are highly structured, such a strategy suffers several problems, such as blurred boundaries and the adhesion to close objects. To alleviate the above problems, we proposed a new strategy that also considers the contours of the buildings. Both the contours and structures of the buildings are jointly learned in the same network. The contours are learnable because the boundary of the mask labels of buildings implicitly represents the contours of buildings. We utilized the building contour information embedded in the labels to optimize the representation of building boundaries, then combined the contour information with multi-scale semantic features to enhance the robustness to image spatial resolution. The experimental results showed that the proposed method achieved 91.64%, 81.34%, and 74.51% intersection over union (IoU) on the WHU, Aerial, and Massachusetts building datasets, and outperformed the state-of-the-art (SOTA) methods. It significantly improved the accuracy of building boundaries, especially for the edges of adjacent buildings. The code is made publicly available.
Increasing Spatio-Temporal Resolution for Monitoring Alpine Solifluction Using Terrestrial Laser Scanners and 3D Vector Fields

This article investigates the usage of terrestrial laser scanner (TLS) point clouds for monitoring the gradual movements of soil masses due to freeze–thaw activity and water saturation, commonly referred to as solifluction. Solifluction is a geomorphic process which is characteristic for hillslopes in (high-)mountain areas, primarily alpine periglacial areas and the arctic. The movement can reach millimetre-to-centimetre per year velocities, remaining well below the typical displacement mangitudes of other frequently monitored natural objects, such as landslides and glaciers. Hence, a better understanding of solifluction processes requires increased spatial and temporal resolution with relatively high measurement accuracy. To that end, we developed a workflow for TLS point cloud processing, providing a 3D vector field that can capture soil mass displacement due to solifluction with high fidelity. This is based on the common image-processing techniques of feature detection and tracking. The developed workflow is tested on a study area placed in Hohe Tauern range of the Austrian Alps with a prominent assemblage of solifluction lobes. The derived displacements were compared with the established geomonitoring approach with total station and signalized markers and point cloud deformation monitoring approaches. The comparison indicated that the achieved results were in the same accuracy range as the established methods, with an advantage of notably higher spatial resolution. This improvement allowed for new insights considering the solifluction processes.
High-Resolution SMAP-Derived Root-Zone Soil Moisture Using an Exponential Filter Model Calibrated per Land Cover Type

Root-zone soil moisture (RZSM) plays a key role for most water and energy budgets, as it is particularly relevant in controlling plant transpiration and hydraulic redistribution. RZSM data is needed for a variety of different applications, such as forecasting crop yields, improving flood predictions and monitoring agricultural drought, among others. Remote sensing provides surface soil moisture (SSM) retrievals, whose key advantage is the large spatial coverage on a systematic basis. This study tests a simple method to retrieve RZSM estimates from high-resolution SSM derived from SMAP (Soil Moisture Active Passive). A recursive exponential filter using a time constant τ is calibrated per land cover type, which uses as an intermediate step a long-term ISBA-DIF (Interaction Soil Biosphere Atmosphere—Diffusion scheme) dataset over an area located in Catalonia, NE of Spain. The τ values thus obtained are then used as an input to the same recursive exponential filter, to derive 1 km resolution RZSM estimates from 1 km SMAP SSM, which are obtained from the original data by downscaling to a 1 km resolution, through the DISPATCH (DISaggregation based on a Physical and Theoretical scale CHange) methodology. The results are then validated with scaled in situ observations at different depths, over two different areas, one representative of rainfed crops, and the other of irrigated crops. In general, the estimates agree well with the observations over the rainfed crops, especially at a 10 cm and 25 cm depth. Nash–Sutcliffe (NS) scores ranging between 0.33 and 0.58, and between 0.37 and 0.56 have been found, respectively. Correlation coefficients for these depths are high, between 0.76 and 0.91 (10 cm), and between 0.71 and 0.90 (25 cm). For the irrigated sites, results are poorer (partly due to the extremely high heterogeneity present), with NS scores ranging between −2.57 and 0.16, and correlations ranging between −0.56 and 0.48 at 25 cm. Given the strong correlations and NS scores found in the surface, the sensitivity of the filter to different τ values was investigated. For the rainfed site, it was found, as expected, with increasing τ, increasing NS and correlations with the deeper layers, suggesting a better coupling. Nevertheless, a strong correlation with the surface (5 cm) or shallower depths (10 cm) observed over certain sites indicates a certain lack of skill of the filter to represent processes which occur at lower levels in the SM column. All in all, a calibration accounting for the vegetation was shown to be an adequate methodology in applying the recursive exponential filter to derive the RZSM estimates over large areas. Nevertheless, the relative shallow surface at which the estimates correlate in some cases seem to indicate that an effect of evapotranspiration in the profile is not well captured by the filter.
Regulation of the Vacuolar ATPase activity

By Paulina Konarzewska Mentor Dr. Chang-Hui Shen In eukaryotes, V-ATPases play an essential role in cellular pH homeostasis as they transport hydrogen ions into the vacuoles. This assures an acidic vacuolar interior and normal physiological processes associated with this organelle. Studies show that mutations in any of the VMA genes encoding for subunits of the V-ATPase pump result in growth inhibition in the absence of inositol, suggesting that VATPases may play an important role in phospholipid biosynthesis or vice versa. It is not clear how VMA genes affect phospholipid biosynthesis and how the regulators of phospholipid biosynthetic genes may affect ATPase activity. Here, we employ biochemical and genetic analyses to understand the connection. In the first part of the study, we evaluated how VMA3 an indispensable gene required for proper VATPase function affects phospholipid biosynthesis. Using growth analysis, we demonstrated that vma3Δ cells were sensitive to pH conditions in the absence of inositol. This result strongly suggests that VMA3 can influence phospholipid gene expression. Subsequently, we observed that HXK2 gene was downregulated in vma3Δ cells using real-time PCR analysis. Acetic acid sensitivity assay further confirmed reduced expression of HXK2 gene in vma3Δ cells. As such, we have demonstrated that the VMA3 plays an important role in phospholipid biosynthesis through regulation of HXK2 gene expression. Recent studies show that some phospholipid genes play important roles in vacuolar morphology and acidification. Among these genes are transcription factors, INO2 and OPI1. To determine their role on vacuolar function, we first performed CaCl2 sensitivity test which screens for genes important in vacuolar ATPase activity. Our results showed that WT, opi1Δ and ino2Δ cells exhibited similar growth sensitivity at 10 mM CaCl2, whereas at 60 mM CaCl2 both opi1Δ and ino2Δ cells exhibited moderate increase in sensitivity compared to WT. As microscopic analyses did not show any significant differences in vacuolar morphology of opi1Δ or ino2Δ compared to WT, vacuolar pH analyses showed significant increases in vacuolar acidification of opi1Δ but not ino2Δ cells compared to WT. Moreover, we observed that some of the genes that encode for V-ATPases were down-regulated significantly in both opi1Δ and ino2Δ. iv 1 Furthermore, both opi1Δ and ino2Δ cells showed lower ATPase activity than WT cells, suggesting that the transcription factors affecting expression of phospholipid genes play an important role in V-ATPase pump activity and vacuolar homeostasis. It is possible that down-regulation of ATPase activity is to maintain normal H+ concentration in the vacuole and to compensate a pH imbalance caused by the lack Opi1p or Ino2p. Together, our results have provided new insights into the connection between phospholipid biosynthesis and cellular pH homeostasis.
Impact of IMPACT: Longitudinal Analysis of an Integrated Participant Scheduling System in a Clinical Research Setting

Rapidly increasing costs have been a major threat to our clinical research enterprise. Improvement in appointment scheduling is a crucial means to boost efficiency and save cost in clinical research and has been well studied in the outpatient setting. This study reviews nearly 5 years of usage data of an integrated scheduling system implemented at Columbia University/New York Presbyterian (CUIMC/NYP) called IMPACT and provides original insights into the challenges faced by a clinical research facility. Briefly, the IMPACT data shows that high rates of room and resource changes correlate with rescheduled appointments and that rescheduled visits are more likely to be attended than non-rescheduled visits. We highlight the differing roles of schedulers, coordinators, and investigators, and propose a highly accurate predictive model of participant no-shows in a research setting. This study sheds light on ways to reduce overall cost and improve the care we offer to clinical research participants.
A Hybrid Radar System With a Phased Transmitting Array and a Digital Beamforming Receiving Array

In this article, a hybrid radar configuration for automotive radar applications is proposed, with the system architecture, design, and measured results reported. The hybrid radar system consists of a vertically distributed phased transmitting array (PTA) and a horizontally aligned digital beamforming (DBF) receiving array, thereby enabling 2-D beam scanning with a reduced channel number. In contrast to time-division multiplexing (TDM) multiple-input multiple-output (MIMO) radars, in which only a single transmitter is active at a time in the TDM MIMO radar configuration due to the working principle of switching between different transmitting elements, here, all the phased array transmitters of the proposed radar work simultaneously. The demonstrated system realizes a higher transmitted power and signal-to-noise ratio, provided that the equivalent isotropically radiated power (EIRP) is not limited and the radiated power of each transmitter is equal in these two radar configurations. In addition, only 1-D DBF is required for the proposed hybrid radar because beamforming in another dimension is accomplished by the PTA, thus alleviating the baseband complexity and signal processing burden. As a proof-of-concept example for system validation, a hybrid radar prototype is fabricated and measured at around 24 GHz, without considering any specific EIRP limitations in this band, thus confirming the validity of the proposed concept.
Frequency Continuously Tunable Patch-Slot Antenna With Wide-Angle Discrete Scanning and Optional Backward Radiation

A novel frequency continuously tunable patch-slot antenna with wide-angle discrete scanning performance and one optional backward radiation state is presented. This antenna originates from combining one patch antenna with two endfire radiation slot antennas. By loading varactors on the patch, the working frequency is continuously adjusted, and simultaneously the maximum beam direction could be reconfigured by controlling four PIN diodes located on two slots. The proposed antenna is capable of reconfiguring its maximum beam direction to six discrete angles at 0°, −36°, −78°, +36°, +78°, and 180°. A prototype is fabricated and measured to validate the design, and experimental results demonstrate that the antenna has the merits of both wide-angle beam discrete scanning performance of ±78° in the H-plane and switchable backward radiation feature within frequency tuning range of 2.24–2.58 GHz.
A Method for Performance Degradation Recognition of RF Electronic System on CHMM

As the critical part of radio frequency circuit, radio frequency amplifier has gotten wide attention with the continuous improvement of safety and reliability requirements. For the sake of ensuring the safety of RF electronic system and realizing condition-based maintenance, the degradation state of it should be accurately identified. Based on the S-parameters of RF amplifier, the working performance of RF low noise amplifier is analyzed. In this paper, a continuous hidden Markov model (CHMM) is developed to identify different degradation states of RF circuit. After research, it is found that CHMM can be modeled by the strict data structure and reliable statistical information with dynamic characteristics. Compared with some traditional state recognition methods that stay in static analysis, CHMM can better retain the statistical information of sample data and has higher recognition rate.
Anti Money Laundering: Rule-Based Methods to Identify Funnel Accounts

Money Laundering is a controversial topic. In some geographical areas (such as the United States especially after September 11th), the topic of Money Laundering is conflated with the topic of financing of terrorism and the two expressions are often used interchangeably. In other geographical areas (such as the Netherlands), some researchers wonder what all the fuss is about with Money Laundering. Such researchers even go so far as to question whether or not Anti Money Laundering (AML) activities infringe human rights. And, if AML does not infringe human rights, some researchers will argue that AML does, at the very least, infringe some civil rights especially regarding personal privacy. Be that as it may, in many, if not most, countries, Money Laundering is an illegal activity. The US Immigration and Customs Enforcement (ICE) agency publish guidelines to assist in recognizing Money Laundering. One such guideline is the presence of funnel accounts where many deposits are input into an account and then dispersed to other accounts within a short period of time. In addition ICE publishes 12 Red Flags on how to recognize such funnel accounts. These 12 Red Flags can be the basis of a rule-based algorithm to programmatically identify funnel accounts.
Sequential Transfer Learning via Segment After Cue Enhances the Motor Imagery-based Brain-Computer Interface

Brain-computer interface (BCI) based on electroencephalogram (EEG) is a promising technology, allowing computers to estimate human intentions. Intention recognition tool such as motor imagery (MI) with high reliability is one of the major challenges in the BCI field. Recently, researchers have attempted to use transfer learning for various BCI datasets, but the studies showed low classification accuracy. This study aimed to increase the classification accuracy of the MI through sequential transfer learning for a single dataset. EEG-MI data with 9 subjects from the dataset 2a of BCI competition IV were used. EEGNet was used for MI classification. The pre-trained model was constructed by first learning whether the data were MI or not. The model was then sequentially fine-tuned through transfer learning for four MI tasks (i.e., left hand, right hand, both feet and tongue). The model was able to classify MI with 91.34% accuracy. In the meantime, the baseline model without transfer learning showed an accuracy of 61.62%, whereas the fine-tuned model presented an improved accuracy of 63.82%. Consequently, the sequential transfer learning was able to improve the performance of MI-BCI.
Design of a Sustainable Electric Boat with Renewable Energy Based Charging System

Design of an Electric boat with solar power charging is proposed here for the water transport of Bangladesh. The design includes the installation of a solar charging station with a net metering system; where all the boats can be charged when needed instead of placing solar panels in each boat. The speed and dimensions of the boat have been selected based on the requirements and availability of boats in different regions of Bangladesh. The simulation results show that the boat can have a reasonable range of operation after each charging. The cost analysis is also performed and the payback period is found to be low. The system can pave the way for using green energy in the river transport of Bangladesh.
NoPTPeer:Protecting Android Devices from Stealthy Spoofing and Stealing in WLANs without Privilege

Android devices are prone to spoofing attacks in Wireless Local Area Networks (WLANs), and many of them access numerous unknown networks in daily use. Moreover, because of the weak authentication between Android smartphones, attackers can steal data in a stealthier way based on Address Resolution Protocol (ARP) spoofing. These facts bring a gap in the study of device-side spoofing defense for Android devices. So in this paper a framework is proposed which requires No Privilege but can guarantee the True identity of Peers (NoPTPeer), to protect Android’s device-to-device communication in WLANs. Its main features include realizing strong authentication between Android devices, controlling dangerous outgoing connections, and monitoring suspicious incoming connections. These features are realized by an Identity-Based-Signature (IBS) scheme, an Android base class VpnService, and information read from Android system files, which all require no root privilege and are independent of network infrastructures. We implement this framework as an Android smartphone application. The experiments show its effectiveness in detecting spoofing and monitoring stealing, as well as acceptable overhead in memory, Central Processing Unit (CPU) usage and communication latency.
A Secure IoT Transmission Scheme Based on Edge Computing

IoT security is one of the most important challenges for the development of IoT. A large number of resource-constrained nodes in IoT have insufficient storage space, limited computing power, unstable communication links, and cannot adopt internet standard security protocols to guarantee the end-to-end security of communication, which becomes the weakness of IoT. Combining the characteristics and development trend of IoT, this paper proposes an edge computing based IoT security architecture and proposes a novel security transmission scheme. In this scheme, an end-to-end security approach based on proxy datagram transport layer security is adopted. Analysis and experiments show that the scheme can enable resource-constrained devices in the IoT to communicate securely end-to-end using internet-standard security protocols, and has good scale, scalability, and practical feasibility
Digital predistortion technique based on improved LMS algorithm

Digital predistortion is an effective method to improve the nonlinear distortion of power amplifier. As the traditional LMS algorithm is slow in convergence and easy to be disturbed by noise and there is convergence instability in the optimal solution, it cannot converge quickly and accurately to identify the inverse model of power amplifier behavior, which affects its predistortion effect. In this paper, an improved NVSLMS algorithm is proposed to improve the anti-noise capability of the predistortion system by changing the iterative function to improve the convergence speed and introducing the autocorrelation matrix of the adjacent moment error. The simulation results show that the predistortion system based on the improved New variable step LMS (NVSLMS) algorithm is significantly superior to the traditional adaptive algorithm in anti-noise performance, convergence speed and out-of-band suppression. EVM index is optimized by 4.3%, and ACPR index is optimized by -8.02dB.
Static Detection of Malicious Code in Programs Using Semantic Techniques

In this paper, we propose a method for static detection of malicious intent of programs by expressing the properties of its source code through ontology. Our study concentrates on unraveling the traits of malware written in Java programming language by using semantic techniques. We have studied the source code of a known malware and identified the characteristics that make it a malicious one. We have expressed the said characteristics through an ontology that relates elements of programming language with each other. We have made the ontology expressive enough to detect the malware while providing a human-interpretable sketch. Code obfuscation enables the continued arms race between malware and malware detection systems. The work presented here also successfully found a solution to this problem.