Miracle-dz/UniFAR-FTUs huggingface dataset:
Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery

Social media data have emerged as a new source for detecting and monitoring disaster events. A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. However, no effort has been made to classify social media data into stages of disaster management (mitigation, preparedness, emergency response, and recovery), which has been used as a common reference for disaster researchers and emergency managers for decades to organize information and streamline priorities and activities during the course of a disaster. This paper makes an initial effort in coding social media messages into different themes within different disaster phases during a time-critical crisis by manually examining more than 10,000 tweets generated during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages. Moreover, a classifier based on logistic regression is trained and used for automatically mining and classifying the social media messages into various topic categories during various disaster phases. The classification results are necessary and useful for emergency managers to identify the transition between phases of disaster management, the timing of which is usually unknown and varies across disaster events, so that they can take action quickly and efficiently in the impacted communities. Information generated from the classification can also be used by the social science research communities to study various aspects of preparedness, response, impact and recovery.
Large-scale Analysis of Spoken Free-verse Poetry

Most modern and post-modern poems have developed a post-metrical idea of lyrical prosody that employs rhythmical features of everyday language and prose instead of a strict adherence to rhyme and metrical schemes. This development is subsumed under the term free verse prosody. We present our methodology for the large-scale analysis of modern and post-modern poetry in both their written form and as spoken aloud by the author. We employ language processing tools to align text and speech, to generate a null-model of how the poem would be spoken by a naïve reader, and to extract contrastive prosodic features used by the poet. On these, we intend to build our model of free verse prosody, which will help to understand, differentiate and relate the different styles of free verse poetry. We plan to use our processing scheme on large amounts of data to iteratively build models of styles, to validate and guide manual style annotation, to identify further rhythmical categories, and ultimately to broaden our understanding of free verse poetry. In this paper, we report on a proof-of-concept of our methodology using smaller amounts of poems and a limited set of features. We find that our methodology helps to extract differentiating features in the authors’ speech that can be explained by philological insight. Thus, our automatic method helps to guide the literary analysis and this in turn helps to improve our computational models.
Organic Phosphorus Composition and Potential Bioavailability in Semi‐Arid Arable Soils of the Western United States

The organic P composition of semi-arid arable soils is largely unknown, but such information is fundamental to understanding P dynamics in irrigated agriculture. We used solution "P nuclear magnetic resonance (NMR) spectroscopy and phosphatase hydrolysis to characterize organic P in semi-arid arable soils from the western USA (organic C 2.0-30.7 g C kg' soil, clay 2-48%, pH 5.2-8.2, CaCO 3 <1-480 g kg -' soil). Total P concentrations ranged from 220 to 1210 mg P kg-1 soil, of which between 12 and 45% was extracted with NaOH-EDTA. Inorganic orthophosphate was the dominant P compound, but concentrations determined by solution 31P NMR spectroscopy were consistently greater than those determined by molybdate colorimetry. Concentrations of organic P were relatively small, and were dominated by orthophosphate monoesters (11-130 mg P kg-1 soil), with smaller concentrations of orthophosphate diesters (0-7 mg P kg-1 soil). Pyrophosphate was present in almost all soils at concentrations up to 14 mg P kg-1 soil. Bicarbonate-extractable organic P ranged from 1.7 to 22.8 mg P kg-1 soil, of which between 37 and 87% was hydrolyzed by phosphatase enzymes, suggesting its bioavailability. Soil organic P concentrations were positively correlated with mean annual precipitation, organic C, clay, and oxalate-extractable metals (Al, Fe, Mn), and negatively correlated with mean annual temperature and soil pH. However, CaCO3 concentrations were not significantly correlated with any soil property. These results indicate that equilibrium levels of organic P in semi-arid arable soils are controlled by a balance between the physical protection offered by the soil matrix and the suitability of the environment for biological productivity
Mechanisms of VE-cadherin Processing and Degradation in Microvascular Endothelial Cells*

VE-cadherin is an endothelial-specific cadherin that plays important roles in vascular morphogenesis and growth control. To investigate the mechanisms by which endothelial cells regulate cadherin cell surface levels, a VE-cadherin mutant containing the non-adhesive interleukin-2 (IL-2) receptor extracellular domain and the VE-cadherin cytoplasmic tail (IL-2R-VE-cadcyto) was expressed in microvascular endothelial cells. Expression of the IL-2R-VE-cadcyto mutant resulted in the internalization of endogenous VE-cadherin and in a dramatic decrease in endogenous VE-cadherin levels. The internalized VE-cadherin co-localized with early endosomes, and the lysosomal inhibitor chloroquine dramatically inhibited the down-regulation of VE-cadherin in cells expressing the IL-2R-VE-cadcyto mutant. Chloroquine treatment also resulted in the accumulation of a VE-cadherin fragment lacking the β-catenin binding domain of the VE-cadherin cytoplasmic tail. The formation of the VE-cadherin fragment could be prevented by treating endothelial cells with proteasome inhibitors. Furthermore, inhibition of the proteasome prevented VE-cadherin internalization and inhibited the disruption of endothelial intercellular junctions by the IL-2RVE-cadcyto mutant. These results provide new insights into the mechanisms of VE-cadherin processing and degradation in microvascular endothelial cells.
Genome Analysis of Food Grade Lactic Acid-Producing Bacteria: From Basics to Applications

Whole-genome sequencing has revolutionized and accelerated scientific research that aims to study the genetics, biochemistry and molecular biology of bacteria. Lactic acid-producing bacteria, which include lactic acid bacteria (LAB) and bifidobacteria, are typically Gram-positive, catalase-negative organisms, which occupy a wide range of natural plant- and animal-associated environments. LAB species are frequently involved in the transformation of perishable raw materials into more stable, pleasant, palatable and safe fermented food products. LAB and bifidobacteria are also found among the resident microbiota of the gastrointestinal and/or genitourinary tracts of vertebrates, where they are believed to exert health-promoting effects. At present, the genomes of more than 20 LAB and bifidobacterial species have been completely sequenced. Their genome content reflects its specific metabolism, physiology, biosynthetic capabilities, and adaptability to varying conditions and environments. The typical LAB/bifidobacterial genome is relatively small (from 1.7 to 3.3 Mb) and thus harbors a limited assortment of genes (from around 1,600 to over 3,000). These small genomes code for a broad array of transporters for efficient carbon and nitrogen assimilation from the nutritionally-rich niches they usually inhabit, and specify a rather limited range of biosynthetic and degrading capabilities. The variation in the number of genes suggests that the genome evolution of each of these bacterial groups involved the processes of extensive gene loss from their particular ancestor, diversification of certain common biological activities through gene duplication, and acquisition of key functions via horizontal gene transfer. The availability of genome sequences is expected to revolutionize the exploitation of the metabolic potential of LAB and bifidobacteria, improving their use in bioprocessing and their utilization in biotechnological and health-related applications.
Sparging and agitation‐induced injury of cultured animals cells: Do cell‐to‐bubble interactions in the bulk liquid injure cells?

It has been established that the forces resulting from bubbles rupturing at the free air (gas)/liquid surface injure animal cells in agitated and/or sparged bioreactors. Although it has been suggested that bubble coalescence and breakup within agitated and sparged bioreactors (i.e., away from the free liquid surface) can be a source of cell injury as well, the evidence has been indirect. We have carried out experiments to examine this issue. The free air/liquid surface in a sparged and agitated bioractor was eliminated by completely filling the 2‐L reactor and allowing sparged bubbles to escape through an outlet tube. Two identical bioreactors were run in parallel to make comparisons between cultures that were oxygenated via direct air sparging and the control culture in which silicone tubing was used for bubble‐free oxygenation. Thus, cell damage from cell‐to‐bubble interactions due to processes (bubble coalescence and breakup) occurring in the bulk liquid could be isolated by eliminating damage due to bubbles rupturing at the free air/liquid surface of the bioreactor. We found that Chinese hamster ovary (CHO) cells grown in medium that does not contain shear‐protecting additives can be agitated at rates up to 600 rpm without being damaged extensively by cell‐to bubble interactions in the bulk of the bioreactor. We verified this using both batch and high‐density perfusion cultures. We tested two impeller designs (pitched blade and Rushton) and found them not to affect cell damage under similar operational conditions. Sparger location (above vs. below the impeller) had no effect on cell damage at higher agitation rates but may affect the injury process at lower agitation intensities (here, below 250 rpm). In the absence of a headspace, we found less cell damage at higher agitation intensities (400 and 600 rpm), and we suggest that this nonintuitive finding derives from the important effect of bubble size and foam stability on the cell damage process. © 1996 John Wiley & Sons, Inc.
The influence of haematocrit on the cerebral circulation

There is currently considerable interest in the effect of haematocrit and blood viscosity on the cerebral circulation and on the development of stroke. This is in part because of the very high incidence of stroke in patients with polycythaemia rubra Vera (1) and the low incidence of stroke in patients with anaemia (2). It has been estimated that there is a 20-fold increased risk of stroke in patients with polycythaemia. It is also widely accepted that patients with pseudoor relative polycythaemia (where there is no absolute increase in the red cell mass, but where the plasma volume is low) also have increased risk of occlusive vascular events (3). There is also epidemiological data from the Framingham Study to indicate that high normal haemoglobin is associated with an increased incidence of stroke, particularly in patients who are also hypertensive
A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs

This paper studies an approach to gait based human identification via similarity learning by deep convolutional neural networks (CNNs). With a pretty small group of labeled multi-view human walking videos, we can train deep networks to recognize the most discriminative changes of gait patterns which suggest the change of human identity. To the best of our knowledge, this is the first work based on deep CNNs for gait recognition in the literature. Here, we provide an extensive empirical evaluation in terms of various scenarios, namely, cross-view and cross-walking-condition, with different preprocessing approaches and network architectures. The method is first evaluated on the challenging CASIA-B dataset in terms of cross-view gait recognition. Experimental results show that it outperforms the previous state-of-the-art methods by a significant margin. In particular, our method shows advantages when the cross-view angle is large, i.e., no less than 36 degree. And the average recognition rate can reach 94 percent, much better than the previous best result (less than 65 percent). The method is further evaluated on the OU-ISIR gait dataset to test its generalization ability to larger data. OU-ISIR is currently the largest dataset available in the literature for gait recognition, with 4,007 subjects. On this dataset, the average accuracy of our method under identical view conditions is above 98 percent, and the one for cross-view scenarios is above 91 percent. Finally, the method also performs the best on the USF gait dataset, whose gait sequences are imaged in a real outdoor scene. These results show great potential of this method for practical applications.
Logic Design using Memristors : An Emerging Technology ( Embedded Tutorial )

This paper provides an introduction to memristor, which is considered as the fourth circuit element along with resistor, inductor and capacitor. Memristors possess some unique properties, i.e. it can change the resistance under voltage control and can retain its value even after the voltage is withdrawn. Another property of memristors is their small feature size which makes them useful for design of ultra-compact memory systems. In addition, the resistive switching property of memristors allows to execute logic primitives and thus can also be used for implementing logic functions using various logic design styles studied in this paper. The paper also discusses memristor fabrication, circuit models, methods for implementing logic functions, and the various computing methodologies that can be used viz. nearmemory computing and in-memory computing.
Peer influence on event reports among adolescents and young adults

When two or more people witness an event together, the event report from one person can influence others’ reports. In the current study we examined the role of age and motivational factors on peer influence regarding event reports in adolescents and young adults. Participants (N=249) watched a short video of a robbery then answered questions with no co-witness information or with information believed to be from a co-witness. Public and private response conditions were included to explore motivations for peer influence. Co-witness information influenced participants’ responses, although the effect was equally strong in the private and the public co-witness conditions. Peer influence on event reports was steady across a large age range (11- to 25-year-olds).
Hand Segmentation from Depth Image using Anthropometric Approach in Natural Interface Development

Hand gestures are often used as natural interface between human and robot. To acquire hand gesture from a captured image, hand segmentation procedure is performed. In this manuscript, a method for hand image segmentation from depth image is proposed. This method uses image thresholding technique to obtain human image part from a depth image. The threshold level is obtained by analyzing human posture dimension (anthropometry). By finding the centroid of the human image, left and right regions of human body can be separated. Assuming that each region has a hand image and the hand is positioned in front of its body, both hand images can be located. Hand segmentation is started by using an anthropometric data of hand pose. The data is used to compute the color values that represent the depth of hand in each image region. Thus, the acquired values are used as threshold for each image region. The thresholding operation resulted in completely segmented hand images. This proposed method has low computation time and works well when the basic assumptions are fulfilled.
Enhancing the capacity of DS-CDMA system using hybrid spreading sequences

A new direct-sequence code-division multiple-access (DS-CDMA) system using hybrid spreading sequences in order to increase system capacity is proposed. This scheme permits accommodating more than N users, where N is the processing gain of the spreading sequences. Users are divided into two classes. The centerpiece idea is to synchronously assign N mutually orthogonal codes to class-1 users, and asynchronously assign quasi-orthogonal codes to class-2 users. The proposed architecture is realized through the DS-CDMA and multicode CDMA (MC-CDMA) techniques for class-1 and class-2 users, respectively, and the system capacity is considerably enhanced as compared with Sari et al. The new approach still excels over Vanhaverbeke et al. when the number of class-1 users K/sub 1/ is large; but our desire is to expand system capacity. Compared with Sari et al. and Vanhaverbeke et al., the new design is able to support multirate applications, and is especially suitable for high-speed multimedia transmissions. Furthermore, the performance of the MC-CDMA system over an additive white Gaussian noise channel is evaluated for various primary codes and mutually orthogonal subcodes. It is shown that orthogonal Gold codes concatenated with Hadamard sequences yield the lowest bit-error rate when the signal-to-noise ratio lies between 0 and 30 dB.
A Novel Approach for Multilevel Fixed Outline Floorplanning

Multilevel strategy is one of the most popular methods for fixed outline floor planning. It partitions an original circuit into some sub-circuits and then merges them into relatively big ones. In this paper, we propose a novel evaluation approach and integrate it into the simulated annealing framework in the merging stage. Moreover, a novel search technique, which can almost keep away from the local optimal, is proposed to obtain better floor plans. Experimental results show that our approach can obtain better performance than the state-of-the-art in terms of runtime, wire length and success rate.
An experimental power pick-up mechanism for an electrically driven UAV.

An actively controlled mechanism for picking up electrical power from the conductors of an overhead line on a distribution network is described. The mechanism is part of a novel concept for inspecting power lines from an unmanned aerial vehicle (UAV). The background to the application is explained and an outline given of a small, ducted-fan rotorcraft which draws its power from the overhead line (OHL). A cranked linkage is proposed that maintains a firm sliding electrical contact with the OHL and compensates for the movement of the rotorcraft. The paper contains details of a kinematic analysis and 3D computer visualization of the mechanism's working range. The design of an experimental pick-up mechanism is described and a computer simulation shows how impedance control can regulate the contact force with the OHL. Experimental results using an air vehicle simulator (AVS) are presented to verify the action of the pick-up mechanism.
Statistical machine translation without long parallel sentences for training data.

In this study, we paid attention to the reliability of phrase table. We have been used the phrase table using Och’s method[2]. And this method sometimes generate completely wrong phrase tables. We found that such phrase table caused by long parallel sentences. Therefore, we removed these long parallel sentences from training data. Also, we utilized general tools for statistical machine translation, such as ”Giza++”[3], ”moses”[4], and ”training-phrasemodel.perl”[5]. We obtained a BLEU score of 0.4047 (TEXT) and 0.3553(1-BEST) of the Challenge-EC task for our proposed method. On the other hand, we obtained a BLEU score of 0.3975(TEXT) and 0.3482(1-BEST) of the Challenge-EC task for a standard method. This means that our proposed method was effective for the Challenge-EC task. However, it was not effective for the BTECT-CE and Challenge-CE tasks. And our system was not good performance. For example, our system was the 7th place among 8 system for Challenge-EC task.
Multilabel classification of UAV images with Convolutional Neural Networks

In this paper, we present a multilabel classification method for images acquired by means of Unmanned Ariel Vehicles (UAV) over urban areas. Due to the fact that UAV-grabbed images are characterized by extremely high spatial resolution, usual recognition schemes (such as traditional satellite or airborne based images) are likely to fail. In this work, tile-based multilabel classification framework is adopted to overcome such issue. In particular, a given UAV-shot image is first subdivided into a grid of equal tiles. Next, deep neural network-induced features are extracted from each tile and then fed into a radial basis function neural network classifier in order to infer the corresponding object list. We apply a refinement step at the top of the complete deep network architecture to boost the classification results. The proposed method was evaluated on a dataset acquired over the city of Trento, Italy with an hexacopter UAV. Superior classification rates have been scored with respect to the state-of-the-art.
NEURAL NETWORK AIDED KALMAN FILTERING FOR INTEGRATED GPS / INS GEO-REFERENCING PLATFORM

Kalman filtering theory plays an important role in integrated GPS/INS georeference system design. A Kalman filter (KF) uses measurement updates to correct system states error and to limit the errors in navigation solutions. However, only when the system dynamic and measurement models are correctly defined, and the noise statistics for the process are completely known, a KF can optimally estimate a system’s states. Without measurement updates, a Kalman filter’s prediction diverges; therefore the performance of an integrated GPS/INS georeference system may degrade rapidly when GPS signals are unavailable. It is a challenge to deal with this problem in real time though it can be handled in post processing by smoothing methods. This paper presents a neural network (NN) aided Kalman filtering method to improve navigation solutions of integrated GPS/INS georeference system. It is known that the errors inherent to strapdown inertial sensors are affected by the platform manoeuvre and environment conditions etc., which are hard to be modelled precisely. On the other hand, NNs have the capability to map inputoutput relationships of a system without apriori knowledge about them. A properly designed NN is able to learn and extract complex relationships given enough training. Furthermore, it is able to adapt to the change of sensors and dynamic platforms. In the proposed loosely coupled GPS/INS georeference system, an extended KF (EKF) estimates the INS measurement errors, plus position, velocity and attitude errors, and provides precise navigation solutions while GPS signals are available. At the same time, a multi-layer NN is trained to map the vehicle manoeuvre with INS prediction errors during each GPS epoch, which is the input of the EKF. During GPS signal blockages, the NN can be used to predict the INS errors for EKF measurement updates, and in this way to improve navigation solutions. The principle of this hybrid method and the NN design are presented. Land vehicle based field test data are processed to evaluate the performance of the proposed method. * Corresponding author.