Miracle-dz/UniFAR-FTUs huggingface dataset:
Supporting large-area, sample-based forest inventories with very high spatial resolution satellite imagery

Information needs associated with forest management and reporting requires data with a steadily increasing level of detail and temporal frequency. Remote sensing satellites commonly used for forest monitoring (eg, Landsat, SPOT) typically collect imagery with sufficient temporal frequency, but lack the requisite spatial and categorical detail for some forest inventory information needs. Aerial photography remains a principal data source for forest inventory; however, information extraction is primarily accomplished through manual processes. The spatial, categorical, and temporal information requirements of large-area forest inventories can be met through sample-based data collection. Opportunities exist for very high spatial resolution (VHSR; ie, <1 m) remotely sensed imagery to augment traditional data sources for large-area, sample-based forest inventories, especially for inventory update. In this paper, we synthesize the state-of-the-art in the use of VHSR remotely sensed imagery for forest inventory and monitoring. Based upon this review, we develop a framework for updating a sample-based, large-area forest inventory that incorporates VHSR imagery. Using the information needs of the Canadian National Forest Inventory (NFI) for context, we demonstrate the potential capabilities of VHSR imagery in four phases of the forest inventory update process: stand delineation, automated attribution, manual interpretation, and indirect attribute modelling. Although designed to support the information needs of the Canadian NFI, the framework presented herein could be adapted to support other sample-based, large-area forest monitoring initiatives.
Towards Multilingual Conversations in the Medical Domain: Development of Multilingual Medical Data and A Network-based ASR System

This paper outlines the recent development on multilingual medical data and multilingual speech recognition system for network-based speech-to-speech translation in the medical domain. The overall speech-to-speech translation (S2ST) system was designed to translate spoken utterances from a given source language into a target language in order to facilitate multilingual conversations and reduce the problems caused by language barriers in medical situations. Our final system utilizes a weighted finite-state transducers with n-gram language models. Currently, the system successfully covers three languages: Japanese, English, and Chinese. The difficulties involved in connecting Japanese, English and Chinese speech recognition systems through Web servers will be discussed, and the experimental results in simulated medical conversation will also be presented.
Molecular Epidemiology of Measles Virus before and after the 2003 Mass Vaccination Campaign for Measles/Rubella in Iran

Background: Molecular epidemiology of measles virus (MV) is important, not only to measure the success of measles vaccination programs but also to monitor the circulation and elimination of the virus worldwide. In this study, we compared MV obtained from patients before the 2003 mass vaccination MR campaign and viruses detected after 2003 until 2008 in Iran. Methods: The nucleoprotein (N) gene of 29 MV strains circulating in Iran between 2002 and 2008 were amplified by RT-PCR and subjected to sequence and phylogenetic analysis. Results: Molecular characterization of MV studied here revealed that although the outbreaks in Iran were associated with MV genotype D4, the isolated viruses clearly belonged to several different lineages. Maximum and minimum homology within the 29 Iranian strains in our study was100% and 94.9% within the carboxyl terminus of the N gene, respectively. Using ClustalX program, the alignment of Iranian MV sequences showed nine lineages. Conclusion: This study provides the usefulness of MV sequence analysis for the demonstration of local interruption of indigenous strain transmission as well as providing a valuable means for monitoring the elimination processes of MV control.
Theranostic Self-Assembly Structure of Gold Nanoparticles for NIR Photothermal Therapy and X-Ray Computed Tomography Imaging

The controllable self-assembly of amphiphilic mixed polymers grafted gold nanoparitcles (AuNPs) leads to strong interparticle plasmonic coupling, which can be tuned to the near-infrared (NIR) region for enhanced photothermal therapy (PTT). In this study, an improved thiolation method was adopted for ATRP and ROP polymer to obtain amphiphilic brushes of PMEO2MA-SH and PCL-SH. By anchoring PCL-SH and PMEO2MA-SH onto the 14 nm AuNPs, a smart hybrid building block for self-assembly was obtained. Increasing the PCL/PMEO2MA chain ratio from 0.8:1, 2:1 and 3:1 to 7:1, the structure of gold assemblies (GAs) was observed to transfer from vesicle to large compound micelle (LCM). Contributed to the special dense packed structure of gold nanoparticles in LCM, the absorption spectrometry of gold nanoparticles drastically red-shifted from 520 nm to 830 nm, which endowed the GAs remarkable NIR photothermal conversion ability. In addition, gold has high X-ray absorption coefficient which qualifies gold nanomaterial a potential CT contrast agent Herein, we obtain a novel gold assembly structure which can be utilized as potential photothermal therapeutic and CT contrast agents. In vitro and In vivo studies testified the excellent treatment efficacy of optimum GAs as a PTT and CT contrast agent. In vitro degradation test, MTT assay and histology study indicated that GAs was a safe, low toxic reagent with good biodegradability. Therefore, the optimum GAs with strong NIR absorption and high X-ray absorption coefficient could be used as a theranostic agent and the formation of novel gold large compound micelle might offers a new theory foundation for engineering design and synthesis of polymer grafted AuNPs for biomedical applications.
A membrane penetrating peptide aptamer inhibits STAT3 function and suppresses the growth of STAT3 addicted tumor cells

Cancer cells are characterized by the aberrant activation of signaling pathways governing proliferation, survival, angiogenesis, migration and immune evasion. These processes are partially regulated by the transcription factor STAT3. This factor is inappropriately activated in diverse tumor types. Since tumor cells can become dependent on its persistent activation, STAT3 is a favorable drug target. Here, we describe the functional characterization of the recombinant STAT3 inhibitor, rS3-PA. This inhibitor is based on a 20 amino acid peptide which specifically interacts with the dimerization domain of STAT3. It is integrated into a thioredoxin scaffold and fused to a protein transduction domain. Protein gel blot and immunofluorescence analyses showed that rS3-PA is efficiently taken up by cells via an endocytosis independent mechanism. Intracellularly, it reduces the phosphorylation of STAT3 and enhances its degradation. This leads to the downregulation of STAT3 target gene expression on the mRNA and protein levels. Subsequently, tumor cell proliferation, survival and migration and the induction of angiogenesis are inhibited. In contrast, normal cells remain unaffected. Systemic administration of rS3-PA at doses of 7.5 mg/kg reduced P-STAT3 levels and significantly inhibited tumor growth up to 35% in a glioblastoma xenograft mouse model.
Replica Placement in Cloud through Simple Stochastic Model Predictive Control

This paper presents a model and an algorithm for optimal service placement (OSP) of a set of N-tier software systems, subject to dynamic changes in the workload, Service Level Agreements (SLA), and administrator preferences. The objective function models the resources' cost, the service level agreements and the trashing cost. The optimization algorithm is predictive: its allocation or reallocation decisions are based not only on the current metrics but also on predicted evolution of the system. The solution of the optimization, in each step, is a set some service replicas to be added or removed from the available hosts. These deployment changes are optimal with regards to overall objectives defined over time. In addition, the optimization considers the restrictions imposed on the number of possible service migrations at each time interval. We present experimental results that show the effectiveness of our approach.
Differential Regulation of Active Zone Density during Long-Term Strengthening of Drosophila Neuromuscular Junctions

In this study we established a transgenic Ca2+imaging technique in Drosophila that enabled us to target the Ca2+ sensor protein yellow Cameleon-2 specifically to larval neurons. This noninvasive method allowed us to measure evoked Ca2+ signals in presynaptic terminals of larval neuromuscular junctions (NMJs). We combined transgenic Ca2+ imaging with electrophysiological recordings and morphological examinations of larval NMJs to analyze the mechanisms underlying persistently enhanced evoked vesicle release in two independent mutants. We show that persistent strengthening of junctional vesicle release relies on the recruitment of additional active zones, the spacing of which correlated with the evoked presynaptic Ca2+ dynamics of individual presynaptic terminals. Knock-out mutants of the postsynaptic glutamate receptor (GluR) subunit DGluR-IIA, which showed a reduced quantal size, developed NMJs with a smaller number of presynaptic boutons but a strong compensatory increase in the density of active zones. This resulted in an increased evoked vesicle release on single action potentials and larger evoked Ca2+signals within individual boutons; however, the transmission of higher frequency stimuli was strongly depressed. A second mutant (pabpP970/+), which showed enhanced evoked vesicle release triggered by elevated subsynaptic protein synthesis, developed NMJs with an increased number of presynaptic boutons and active zones; however, the density of active zones was maintained at a value typical for wild-type animals. This resulted in wild-type evoked Ca2+ signals but persistently strengthened junctional signal transmission. These data suggest that the consolidation of strengthened signal transmission relies not only on the recruitment of active zones but also on their equal distribution in newly grown boutons.
Coarsening Rates in Off-Critical Mixtures

We study coarsening of a binary mixture within the Mullins--Sekerka evolution in the regime where one phase has small volume fraction $\phi \ll1$. Heuristic arguments suggest that the energy density, which represents the inverse of a typical length scale, decreases as $\phi t^{-1/3}$ as $t \to \infty$. We prove rigorously a corresponding weak lower bound. Moreover, we establish a stronger result for the two-dimensional case, where we find a lower bound of the form $ \phi(\ln \phi^{-1})^{1/3}t^{-1/3}$. Our approach follows closely the analysis in [R. V. Kohn and F. Otto, Comm. Math. Phys., 229 (2002), pp. 375-395], which exploits the relation between two suitable length scales. Our main contribution is an isoperimetric inequality in the two-dimensional case.
Compressive Data Persistence in Large-Scale Wireless Sensor Networks

This paper considers a large-scale wireless sensor network where sensor readings are occasionally collected by a mobile sink, and sensor nodes are responsible for temporarily storing their own readings in an energy-efficient and storage-efficient way. Existing data persistence schemes based on erasure codes do not utilize the correlation between sensor data, and their decoding ratio is always larger than one. Motivated by the emerging compressive sensing theory, we propose compressive data persistence which simultaneously achieves data compression and data persistence. In the development of compressive data persistence scheme, we design a distributed compressive sensing encoding approach based on Metropolis-Hastings random walk. When the maximum step of random walk is 400, our proposed scheme can achieve a decoding ratio of 0.36 for 10%-sparse data. We also compare our scheme with a state-of-the-art Fountain code based scheme. Simulation shows that our scheme can significantly reduce the decoding ratio by up to 63%.
Real-time student assessment using a system of RF clickers

The paper presents an analysis of a one-semester technical course ldquoElectrical facilitiesrdquo. The goal of the paper is to show how a system of real-time knowledge testing at undergraduate level, using a radio-frequency clicker system and conventional knowledge assessment methods, was implemented on a single course at the University of Zagreb. Using statistical analysis, students are clustered in groups to show the influence of clickers on the student population. It is shown that clickers have a favorable impact on students and work in accordance with each studentpsilas abilities, as confirmed using conventional assessment methods.
Security Analysis of a Privacy-preserving ECC-based Grouping-proof Protocol

Batina et al. have proposed a privacy-preserving grouping-proof RFID protocol with colluding tag prevention (CTP) recently which relies exclusively on the use of Elliptic Curve Cryptography (ECC). In this paper, we show that this proposed protocol is not secure against the tracking attack. To make this attack successfully, the adversary needs to execute three phases. Firstly, the attacker just eavesdrops on the messages exchanged between Reader and Tags. Secondly, the attacker impersonates Reader to replay the message which is got from the first phase. Finally, the adversary acts as a man in the middle to tamper the messages exchanged between Reader and Tag B. Then we propose an enhancement and prove that the revision is secure against the tracking attack which keeps other security properties.
Successive Projection Method for Well-Conditioned Matrix Approximation Problems

Matrices are often required to be well-conditioned in a wide variety of areas including signal processing. Problems to find the nearest positive definite matrix or the nearest correlation matrix that simultaneously satisfy the condition number constraint and sign constraints are presented in this paper. Both problems can be regarded as those to find a projection to the intersection of the closed convex cone corresponding to the condition number constraint and the convex polyhedron corresponding to the other constraints. Thus, we can apply a successive projection method, which is a classical algorithm for finding the projection to the intersection of multiple convex sets, to these problems. The numerical results demonstrated that the algorithm effectively solved the problems.
TFEB ameliorates the impairment of the autophagy-lysosome pathway in neurons induced by doxorubicin

Doxorubicin, a commonly used chemotherapy agent, induces severe cardio- and neurotoxicity. Molecular mechanisms of cardiotoxicity have been extensively studied, but mechanisms by which doxorubicin exhibits its neurotoxic properties remain unclear. Here, we show that doxorubicin impairs neuronal autophagy, leading to the accumulation of an autophagy substrate p62. Neurons treated with doxorubicin contained autophagosomes, damaged mitochondria, and lipid droplets. The brains from mice treated with pegylated liposomal doxorubicin exhibited autophagosomes, often with mitochondria, lipofuscin, and lipid droplets. Interestingly, lysosomes were less acidic in doxorubicin-treated neurons. Overexpression of the transcription factor EB (TFEB), which controls the autophagy-lysosome axis, increased survival of doxorubicin-treated neurons. 2-Hydroxypropyl-β-cyclodextrin (HPβCD), an activator of TFEB, also promoted neuronal survival, decreased the levels of p62, and lowered the pH in lysosomes. Taken together, substantial changes induced by doxorubicin contribute to neurotoxicity, cognitive disturbances in cancer patients and survivors, and accelerated brain aging. The TFEB pathway might be a new approach for mitigating damage of neuronal autophagy caused by doxorubicin.
Modularity "for free" in genome architecture?

Background: Recent models of genome-proteome evolution have shown that some of the key traits displayed by the global structure of cellular networks might be a natural result of a duplication-diversification (DD) process. One of the consequences of such evolution is the emergence of a small world architecture together with a scale-free distribution of interactions. Here we show that the domain of parameter space were such structure emerges is related to a phase transition phenomenon. At this transition point, modular architecture spontaneously emerges as a byproduct of the DD process. Results: Although the DD models lack any functionality and are thus free from meeting functional constraints, they show the observed features displayed by the real proteome maps when tuned close to a sharp transition point separating a highly connected graph from a disconnected system. Close to such boundary, the maps are shown to display scale-free hierarchical organization, behave as small worlds and exhibit modularity. Conclusions: It is conjectured that natural selection tuned the average connectivity in such a way that the network reaches a sparse graph of connections. One consequence of such scenario is that the scaling laws and the essential ingredients for building a modular net emerge for free close to such transition.
Distributed Growing Radial Basis Function Neural Networks for Location Estimation in Indoor Wireless Networks

Location aware computing is popularized and use of location information has become important due to huge application of mobile computing devices and local area wireless networks. To serve us well, mobile computing applications need to know the physical location of things so that they can record them and report them to us. Therefore in the future ubiquitous services, location estimation will be a key technology. This paper present distributed growing radial basis function neural networks (DGRBFNN) for location estimation of mobile device in wireless networks. DGRBFNN partitions the location space into clusters on the basis of availability of signals from various access points, and employ separate neural network architecture for each cluster to estimate the location of mobile device in indoor wireless networks. It provides better location estimation results than other approaches and systematically caters for unavailable signals at estimation time.
Thresholding video images for text detection

Thresholding video images is very challenging due to the fact that image background generally has low resolution and is also more complicated and highly distorted than document images. As a result, thresholding methods that work well for document images may not work effectively for video images in some applications. This paper investigates the issue of thresholding video images for text detection and further develops a relative entropy-based thresholding approach that can effectively extract text from complicated video images. In order to demonstrate its performance a comparative study is conducted among the proposed thresholding method and several thresholding techniques which are widely used for document and gray scale images. The experimental results show that thresholding video images is far more difficult than thresholding document images and simple histogram-based methods generally do not perform well.
Unsupervised acoustic model training

This paper describes some recent experiments using unsupervised techniques for acoustic model training in order to reduce the system development cost. The approach uses a speech recognizer to transcribe unannotated raw broadcast news data. The hypothesized transcription is used to create labels for the training data. Experiments providing supervision only via the language model training materials show that including texts which are contemporaneous with the audio data is not crucial for success of the approach, and that the acoustic models can be initialized with as little as 10 minutes of manually annotated data. These experiments demonstrate that unsupervised training is a viable training scheme and can dramatically reduce the cost of building acoustic models.
A New Approach to Conformant Planning via Classical Planners

In this paper, we introduce a new approach to conformant planning via classical planners. We view a conformant planning problem as a set of classical planning problems, called sub-problems, and solve it using a generate-and-complete algorithm. Key to this algorithm is a procedure which takes a solution of a sub-problem and generates a solution for other sub-problems. We implement this algorithm in a new planner, called CPCL and evaluate it empirically against state-of-theart conformant planners using various benchmarks. The experimental results show that CPCL is superior to other planners in most benchmarks, both in performance and in scala-
Using Conjunctive Methods to Explore Factors Associated with On-Time Grade Attainment Among High Risk Youth

Abstract This study examines risk and protective factors associated with on-time grade attainment among a cohort of high risk elementary age youth 3 years post truancy intervention. Conjunctive Analysis of Case Configurations was used to identify dominant subpopulation profiles and assess distinctions in the likelihood of on-time grade completion among groups with unique combinations of characteristics. Comparisons of subpopulation profiles revealed substantial contextual variability in the influence of race, gender, service completion, psychosocial risk factors, and truancy. Findings indicate race is not influential across all grades but appears to be dependent on truancy level. Although psychosocial risk factors are not present for K-3rd profiles, they are critical among 4-5th graders, specifically lack of motivation. Findings from this study allow practitioners to better understand risk and protective factors influencing grade progression and aids in the development of individualized case management and intervention. Findings indicate better policies are needed to address early truancy.
Complete genome sequence of Meiothermus silvanus type strain (VI-R2T)

Meiothermus silvanus (Tenreiro et al. 1995) Nobre et al. 1996 belongs to a thermophilic genus whose members share relatively low degrees of 16S rRNA gene sequence similarity. Meiothermus constitutes an evolutionary lineage separate from members of the genus Thermus, from which they can generally be distinguished by their slightly lower temperature optima. M. silvanus is of special interest as it causes colored biofilms in the paper making industry and may thus be of economic importance as a biofouler. This is the second completed genome sequence of a member of the genus Meiothermus and only the third genome sequence to be published from a member of the family Thermaceae. The 3,721,669 bp long genome with its 3,667 protein-coding and 55 RNA genes is a part of the GenomicEncyclopedia ofBacteria andArchaea project.