Miracle-dz/UniFAR-FTUs huggingface dataset:
Hemodynamic Surveillance of Unilateral Carotid Artery Stenting in Patients With or Without Contralateral Carotid Occlusion by TCD/TCCD in the Early Stage Following Procedure

Objective: To evaluate the cerebral hemodynamic variations in patients with unilateral carotid artery stenosis and contralateral carotid occlusion (CCO) in hours following carotid artery stenting (CAS) by transcranial Doppler (TCD) or transcranial color-code Doppler (TCCD). Methods: Sixty-five consecutive patients who underwent unilateral CAS were enrolled. Among them, 14 patients had ipsilateral severe stenosis and CCO (CCO group) while the other 51 patients had only unilateral severe carotid stenosis without CCO (UCS group). All patients underwent TCD or TCCD monitoring before, at 1 and 3 h after CAS. We monitored bilateral middle cerebral artery (MCA) peak systolic velocity (PSV), pulsatility index (PI), and blood pressure (BP), and compared that data between two groups. Results: In UCS group, ipsilateral MCA PSV increased relative to baseline at 1 h (96 ± 30 vs. 85 ± 26 cm/s, 15%, P < 0.001) and 3 h (97 ± 29 vs. 85 ± 26 cm/s, 17%, P < 0.001) following CAS. Significant PI increases were observed at 1 and 3 h following CAS on the ipsilateral side. In CCO group, ipsilateral MCA PSV increased relative to baseline at 1 h (111 ± 30 vs. 83 ± 26 cm/s, 35%, P < 0.001) and 3 h (107 ± 28 vs. 83 ± 26 cm/s, 32%, P <0.001) following CAS. The magnitude of ipsilateral MCA PSV increase was significantly higher in CCO group compared with UCS group at 1 h (P = 0.002) and 3 h (P = 0.024) following CAS, while BP similarly decreased between the two groups. On the contralateral side, significant MCA PSV increases were observed following CAS in CCO group but not in UCS group. Bilateral MCA PSV increases were higher in patients with a stenosis degree of ≥90% than in patients with stenosis degree of 70–89% only in CCO group. Conclusion: The ipsilateral MCA PSV and PI increase moderately in the initial hours after unilateral CAS in patients without CCO. In patients with CCO, the ipsilateral, and contralateral MCA PSV increase significantly in the early stage following CAS. CCO is a factor of the increased blood flow velocity in ipsilateral MCA after unilateral CAS.
Phylogeography and Historical Demography of Two Sympatric Atlantic Snappers: Lutjanus analis and L. jocu

Lutjanus analis (mutton snapper) and L. jocu (dog snapper) are mesopredator species with extensive geographic distribution in the Atlantic Ocean. Although historically overfished, their genetic diversity, population structuring, and historical demography along the Brazilian coast are unknown. Here we present genetic data for the hypervariable region 1 (HVR1) of the mtDNA control region of both L. jocu and L. analis, and for cytB of L. analis from distinct geographic regions. Phylogeographic analyses based on HVR1 sequences revealed no geographic structuring of mtDNA lineages for either species. The bimodal mismatch distribution plots of mutton and dog snapper populations implied that they might have experienced secondary contact. Historical demographic parameters estimated that population expansions ranged from 13,631 to 61,171 years before present (ybp) for L. analis and 36,783-55,577 ybp for L. jocu, associated with events that occurred at the end of the last glaciation period. Estimates of the average effective population size for L. jocu were higher than those for L. analis, with the largest population occupying the Brazilian northeastern region coast. High migration rates are maintained among the three northernmost locations, with a substantial decrease to the further southern region. Our study suggests that a tripartite interaction of larval dispersion and interregional adult movement (aggregate spawning), allied to historical contingencies, contributed to contemporary population genetic patterns of these species, and adds relevant information for conservation management of such vulnerable - and valuable - marine resources.
The pH Signaling Transcription Factor PAC-3 Regulates Metabolic and Developmental Processes in Pathogenic Fungi

The zinc finger transcription factor PAC-3/RIM101/PacC has a defined role in the secretion of enzymes and proteins in response to ambient pH, and also contributes to the virulence of species. Herein we evaluated the role of PAC-3 in the regulation of Neurospora crassa genes, in a model that examined the plant-fungi interactions. N. crassa is a model fungal species capable of exhibiting dynamic responses to its environment by employing endophytic or phytopathogenic behavior according to a given circumstance. Since plant growth and productivity are highly affected by pH and phosphorus (P) acquisition, we sought to verify the impact that induction of a Δpac-3 mutation would have under limited and sufficient Pi availability, while ensuring that the targeted physiological adjustments mimicked ambient pH and nutritional conditions required for efficient fungal growth and development. Our results suggest direct regulatory functions for PAC-3 in cell wall biosynthesis, homeostasis, oxidation-reduction processes, hydrolase activity, transmembrane transport, and modulation of genes associated with fungal virulence. Pi-dependent modulation was observed mainly in genes encoding for transporter proteins or related to cell wall development, thereby advancing the current understanding regarding colonization and adaptation processes in response to challenging environments. We have also provided comprehensive evidence that suggests a role for PAC-3 as a global regulator in plant pathogenic fungi, thus presenting results that have the potential to be applied to various types of microbes, with diverse survival mechanisms.
Increases in Theta Oscillatory Activity During Episodic Memory Retrieval Following Mindfulness Meditation Training

Mindfulness meditation has been shown to improve episodic memory and increase theta oscillations which are known to play a role in episodic memory retrieval. The present study examined the effect of mindfulness meditation on episodic memory retrieval and theta oscillations. Using a longitudinal design, subjects in the mindfulness meditation experimental group who underwent 4 weeks of mindfulness meditation training and practice were compared to a waitlist control group. During the pre-training and post-training experimental sessions, subjects completed the Five Facet Mindfulness Questionnaire (FFMQ) and studied adjectives and either imagined a scene (Place Task) or judged its pleasantness (Pleasant Task). During the recognition test, subjects decided which task was performed with each word (“Old Place Task” or “Old Pleasant Task”) or “New. ” FFMQ scores and source discrimination were greater post-training than pre-training in the mindfulness meditation experimental group. Electroencephalography (EEG) results revealed that for the mindfulness meditation experimental group theta power was greater post-training than pre-training in right frontal and left parietal channels and changes in FFMQ scores correlated with changes in theta oscillations in right frontal channels (n = 20). The present results suggest that mindfulness meditation increases source memory retrieval and theta oscillations in a fronto-parietal network.
The Fog of Warnings: How Non-essential Notifications Blur with Security Warnings

Adherence to security warnings continues to be an important problem in information security. Although users may fail to heed a security warning for a variety of reasons, a major contributor is habituation, which is decreased response to repeated stimulation. However, the scope of this problem may actually be much broader than previously thought because of the neurobiological phenomenon of generalization. Whereas habituation describes a diminished response with repetitions of the same stimulus, generalization occurs when habituation to one stimulus carries over to other novel stimuli that are similar in appearance. Generalization has important implications for the domains of usable security and human–computer interaction. Because a basic principle of user interface design is visual consistency, generalization suggests that through exposure to frequent non-security-related notifications (e.g., dialogs, alerts, confirmations, etc.) that share a similar look and feel, users may become deeply habituated to critical security warnings that they have never seen before. Further, with the increasing number of notifications in our lives across a range of mobile, Internet of Things, and computing devices, the accumulated effect of generalization may be substantial. However, this problem has not been empirically examined before. This paper contributes by measuring the impacts of generalization in terms of (1) diminished attention via mouse cursor tracking and (2) users’ ability to behaviorally adhere to security warnings. Through an online experiment, we find that:
Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features

Mobile Mapping is an efficient technology to acquire spatial data of the environment. The spatial data is fundamental for applications in crisis management, civil engineering or autonomous driving. The extrinsic calibration of the Mobile Mapping System is a decisive factor that affects the quality of the spatial data. Many existing extrinsic calibration approaches require the use of artificial targets in a time-consuming calibration procedure. Moreover, they are usually designed for a specific combination of sensors and are, thus, not universally applicable. We introduce a novel extrinsic self-calibration algorithm, which is fully automatic and completely data-driven. The fundamental assumption of the self-calibration is that the calibration parameters are estimated the best when the derived point cloud represents the real physical circumstances the best. The cost function we use to evaluate this is based on geometric features which rely on the 3D structure tensor derived from the local neighborhood of each point. We compare different cost functions based on geometric features and a cost function based on the Rényi quadratic entropy to evaluate the suitability for the self-calibration. Furthermore, we perform tests of the self-calibration on synthetic and two different real datasets. The real datasets differ in terms of the environment, the scale and the utilized sensors. We show that the self-calibration is able to extrinsically calibrate Mobile Mapping Systems with different combinations of mapping and pose estimation sensors such as a 2D laser scanner to a Motion Capture System and a 3D laser scanner to a stereo camera and ORB-SLAM2. For the first dataset, the parameters estimated by our self-calibration lead to a more accurate point cloud than two comparative approaches. For the second dataset, which has been acquired via a vehicle-based mobile mapping, our self-calibration achieves comparable results to a manually refined reference calibration, while it is universally applicable and fully automated.
Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data

One of the most challenging problems in precision agriculture is to correctly identify and separate crops from the soil. Current precision farming algorithms based on artificially intelligent networks use multi-spectral or hyper-spectral data to derive radiometric indices that guide the operational management of agricultural complexes. Deep learning applications using these big data require sensitive filtering of raw data to effectively drive their hidden layer neural network architectures. Threshold techniques based on the normalized difference vegetation index (NDVI) or other similar metrics are generally used to simplify the development and training of deep learning neural networks. They have the advantage of being natural transformations of hyper-spectral or multi-spectral images that filter the data stream into a neural network, while reducing training requirements and increasing system classification performance. In this paper, to calculate a detailed crop/soil segmentation based on high resolution Digital Surface Model (DSM) data, we propose the redefinition of the radiometric index using a directional mathematical filter. To further refine the analysis, we feed this new radiometric index image of about 3500 × 4500 pixels into a relatively small Convolution Neural Network (CNN) designed for general image pattern recognition at 28 × 28 pixels to evaluate and resolve the vegetation correctly. We show that the result of applying a DSM filter to the NDVI radiometric index before feeding it into a Convolutional Neural Network can potentially improve crop separation hit rate by 65%.
Proteus: A DLT-Agnostic Emulation and Analysis Framework

This paper presents Proteus, a framework for conducting rapid, emulation-based analysis of distributed ledger technologies (DLTs) using FIREWHEEL, an orchestration tool that assists a user in building, controlling, observing, and analyzing realistic experiments of distributed systems. Proteus is designed to support any DLT that has some form of a “transaction” and which operates on a peer-to-peer network layer. Proteus provides a framework for an investigator to set up a network of nodes, execute rich agent-driven behaviors, and extract run-time observations. Proteus relies on common features of DLTs to define agent-driven scenarios in a DLTagnostic way allowing for those scenarios to be executed against different DLTs. We demonstrate the utility of using Proteus by executing a 51% attack on an emulated Ethereum network containing 2000 nodes.
Multipath Residual Network for Spectral-Spatial Hyperspectral Image Classification

Convolutional neural networks (CNNs) have recently shown outstanding capability for hyperspectral image (HSI) classification. In this work, a novel CNN model is proposed, which is wider than other existing deep learning-based HSI classification models. Based on the fact that very deep residual networks (ResNets) behave like an ensemble of relatively shallow networks, our proposed network, called multipath ResNet (MPRN), employs multiple residual functions in the residual blocks to make the network wider, rather than deeper. The proposed network consists of shorter-medium paths for efficient gradient flow and replaces the stacking of multiple residual blocks in ResNet with fewer residual blocks but more parallel residual functions in each of it. Experimental results on three real hyperspectral data sets demonstrate the superiority of the proposed method over several state-of-the-art classification methods.
Cooperative Privacy and Security: Learning from People with Visual Impairments and Their Allies

To better inform privacy/security designs for people with disabilities, we “shadowed” people with visual impairments and their allies (e.g., friends, family members, and professional helpers) for two days followed by an exit interview. Our study results provide rich and nuanced accounts of how people with visual impairments enact their privacy/security in daily life, inﬂuenced by both their interactions with their allies and multiple (marginalized) dimensions of their identities such as different disabilities. We also found that people with visual impairments often work closely with their allies to protect their privacy and security in a cooperative manner. However, they were also thoughtful about who they would ask for help in part due to privacy reasons, even if they are trustworthy family members. We discuss ideas for future research and design, particularly a need for designing mechanisms or tools that facilitate cooperative privacy management (e.g., between people with visual impairments and their allies).
Artifice: A Deniable Steganographic File System

The challenge of deniability for sensitive data can be a life or death issue depending on location. Plausible deniability directly impacts groups such as democracy advocates relaying information in repressive regimes, journalists covering human rights stories in a war zone, and NGO workers hiding food shipment schedules from violent militias. All of these users would benefit from a plausibly deniable data storage system. Previous deniable storage solutions only offer pieces of an implementable solution. Artifice is the first tunable, operationally secure, self repairing, and fully deniable steganographic file system. Artifice operates through the use of a virtual block device driver stored separately from the hidden data. It uses external entropy sources and erasure codes to deniably and reliably store data within the unallocated space of an existing file system. A set of data blocks to be hidden are combined with entropy blocks through erasure codes to produce a set of obfuscated carrier blocks that are indistinguishable from other pseudorandom blocks on the disk. A subset of these blocks may then be used to reconstruct the data. Artifice presents a truly deniable storage solution through its use of external entropy and erasure codes, while providing better durability than other deniable storage systems.
Scalable Scanning and Automatic Classification of TLS Padding Oracle Vulnerabilities

The TLS protocol provides encryption, data integrity, and authentication on the modern Internet. Despite the protocol’s importance, currently-deployed TLS versions use obsolete cryptographic algorithms which have been broken using various attacks. One prominent class of such attacks is CBC padding oracle attacks. These attacks allow an adversary to decrypt TLS trafﬁc by observing different server behaviors which depend on the validity of CBC padding. We present the ﬁrst large-scale scan for CBC padding oracle vulnerabilities in TLS implementations on the modern Internet. Our scan revealed vulnerabilities in 1.83% of the Alexa Top Million websites, detecting nearly 100 different vulnerabilities. Our scanner observes subtle differences in server behavior, such as responding with different TLS alerts, or with different TCP header ﬂags. We used a novel scanning methodology consisting of three steps. First, we created a large set of probes that detect vulnerabilities at a considerable scanning cost. We then reduced the number of probes using a preliminary scan, such that a smaller set of probes has the same detection rate but is small enough to be used in large-scale scans. Finally, we used the reduced set to scan at scale, and clustered our ﬁndings with a novel approach using graph drawing algorithms. Contrary to common wisdom, exploiting CBC padding or-acles does not necessarily require performing precise timing measurements. We detected vulnerabilities that can be exploited simply by observing the content of different server responses. These vulnerabilities pose a signiﬁcantly larger threat in practice than previously assumed.
Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35–45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days.
Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

The Advanced Satellite with New system ARchitecture for Observation-2 (ASNARO-2), which carries the X-band Synthetic Aperture Radar (XSAR), was launched on 17 January 2018 and is expected to be used to supplement data provided by larger satellites. Land cover classification is one of the most common applications of remote sensing, and the results provide a reliable resource for agricultural field management and estimating potential harvests. This paper describes the results of the first experiments in which ASNARO-2 XSAR data were applied for agricultural crop classification. In previous studies, Sentinel-1 C-SAR data have been widely utilized to identify crop types. Comparisons between ASNARO-2 XSAR and Sentinel-1 C-SAR using data obtained in June and August 2018 were conducted to identify five crop types (beans, beetroot, maize, potato, and winter wheat), and the combination of these data was also tested. To assess the potential for accurate crop classification, some radar vegetation indices were calculated from the backscattering coefficients for two dates. In addition, the potential of each type of SAR data was evaluated using four popular supervised learning models: Support vector machine (SVM), random forest (RF), multilayer feedforward neural network (FNN), and kernel-based extreme learning machine (KELM). The combination of ASNARO-2 XSAR and Sentinel-1 C-SAR data was effective, and overall classification accuracies of 85.4 ± 1.8% were achieved using SVM.
pClay: A Precise Parallel Algorithm for Comparing Molecular Surfaces

5 Comparing binding sites as geometric solids can reveal conserved features of protein structure that 6 bind similar molecular fragments and varying features that select different partners. Due to the 7 subtlety of these features, algorithmic efficiency and geometric precision are essential for comparison 8 accuracy. For these reasons, this paper presents pClay, the first structure comparison algorithm to 9 employ fine-grained parallelism to enhance both throughput and efficiency. We evaluated the parallel 10 performance of pClay on both multicore workstation CPUs and a 61-core Xeon Phi, observing 11 scaleable speedup in many thread configurations. Parallelism unlocked levels of precision that 12 were not practical with existing methods. This precision has important applications, which we 13 demonstrate: A statistical model of steric variations in binding cavities, trained with data at the level 14 of precision typical of existing work, can overlook 46% of authentic steric influences on specificity 15 (p ≤ .02). The same model, trained with more precise data from pClay, overlooked 0% using the 16 same standard of statistical significance. These results demonstrate how enhanced efficiency and 17 precision can advance the detection of binding mechanisms that influence specificity. 18 2012 ACM Subject Classification Applied Computing → Molecular Structural Biology; Computing 19 Methodologies → Volumetric Models; Parallel algorithms 20