Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Glutathione S-Transferase (GST) Genes from a Dark Septate Endophytic Fungus (Exophiala pisciphila) and Their Expression Patterns under Varied Metals Stress

Glutathione S-transferases (GSTs) compose a family of multifunctional enzymes that play important roles in the detoxification of xenobiotics and the oxidative stress response. In the present study, twenty four GST genes from the transcriptome of a metal-tolerant dark septate endophyte (DSE), Exophiala pisciphila, were identified based on sequence homology, and their responses to various heavy metal exposures were also analyzed. Phylogenetic analysis showed that the 24 GST genes from E. pisciphila (EpGSTs) were divided into eight distinct classes, including seven cytosolic classes and one mitochondrial metaxin 1-like class. Moreover, the variable expression patterns of these EpGSTs were observed under different heavy metal stresses at their effective concentrations for inhibiting growth by 50% (EC50). Lead (Pb) exposure caused the up-regulation of all EpGSTs, while cadmium (Cd), copper (Cu) and zinc (Zn) treatments led to the significant up-regulation of most of the EpGSTs (p < 0.05 to p < 0.001). Furthermore, although heavy metal-specific differences in performance were observed under various heavy metals in Escherichia coli BL21 (DE3) transformed with EpGSTN-31, the over-expression of this gene was able to enhance the heavy metal tolerance of the host cells. These results indicate that E. Pisciphila harbored a diverse of GST genes and the up-regulated EpGSTs are closely related to the heavy metal tolerance of E. pisciphila. The study represents the first investigation of the GST family in E. pisciphila and provides a primary interpretation of heavy metal detoxification for E. pisciphila.
TOPS Interferometry With TerraSAR-X

This paper presents results on SAR interferometry for data acquired in the Terrain Observation by Progressive Scans (TOPS) imaging mode. The rationale to retrieve accurate interferometric products in this mode is expounded, emphasizing the critical step of coregistration. Due to the particularities of the TOPS mode, a high Doppler centroid is present at burst edges, demanding a very high azimuth coregistration performance. A coregistration accuracy of around one tenth of a pixel, as it is usually recommended for stripmap interferometric data, could result in large undesired azimuth phase ramps in each TOPS burst. This paper presents two approaches based on the spectral diversity technique to precisely estimate this coregistration offset with the required accuracy and evaluates their performance. The effect of squint at burst edges in terms of an undesired impulse response shift during focusing and the impact on the interferometric coregistration performance is also addressed. Repeat-pass TOPS data acquired experimentally by TerraSAR-X are used to validate the proposed approaches.
Enhancing GSOM text clustering with Latent Semantic Analysis

Growing Self Organizing Map (GSOM) has proven benefits in text clustering. Latent Semantic Analysis (LSA) also has been used in text clustering to capture the latent concepts from text. This paper presents a novel combination of GSOM and LSA to improve text clustering results compared to using GSOM on its own. LSA is an inherently global algorithm that looks at trends and patterns globally and GSOM is a nearest neighborhood based algorithm which looks at local patterns. Combination of these two can be used to discover both the global and local patterns. In the proposed model, initial text corpus is converted into its vector space representation using the traditional Term Frequency — Inverse Document Frequency (TF-IDF) technique. Then the Singular Value Decomposition (SVD) followed by Frobenius norm is applied on the resulting high dimensional vector to come up with a new vector with an optimal number of dimensions. Experiments using the proposed model were conducted and compared with the original GSOM under the same conditions. Experiment results demonstrate that the new combination of these well known techniques enhances the accuracy of clustering results and the computational time than the GSOM alone.
Improving Statistical Machine Translation with Processing Shallow Parsing

Reordering is of essential importance for phrase based statistical machine translation (SMT). In this paper, we would like to present a new method of reordering in phrase based SMT. We inspired from (Xia and McCord, 2004) using preprocessing reordering approaches. We used shallow parsing and transformation rules to reorder the source sentence. The experiment results from English-Vietnamese pair showed that our approach achieves significant improvements over MOSES which is the state-of-the art phrase based system.
An improved data streams processing algorithm for sensor networks based on training weights

In order to enhance the performance of SPIRIT, in this paper we propose an improved multi-streams processing algorithm for data streams processing of sensor networks. The improved algorithm adapts the orthogonalization step and the training step for tracking weights vectors. Simulation experimental results show that compared to the original algorithm, the improved algorithm can reduce the reconstruction error, increase the energy fraction of reconstruction, and decrease the number of hidden variables, so it extract the principal components among streams more effectively.
The ICT Sector Evolution in an Industrial Region of Slovakia

The information and communication industry (ICT) has become more significant in the regional economic structure in the last two decades, having a notable impact on other industries. Although young in age, the number of firms or employees in the industry is considerable. Bathelt, Malmberg and Maskell (2004) and Trippl et al. (2009) have highlighted two different knowledge interactions within the ICT sector-local (i.e. local buzz) and trans-local (i.e. global pipelines). Furthermore, this knowledge interaction pattern can be different according to the size, economic structure and history of the cities/regions under consideration. The article is based on empirical research, focusing on the ICT sector knowledge interactions in the region of Eastern Slovakia. It highlights the pre-existing industry related basis of ICT, the importance of local and trans-local interactions, its social capital level as well as clustering potential.
Markov Interacting Importance Samplers

We introduce a new Markov chain Monte Carlo (MCMC) sampler called the Markov Interacting Importance Sampler (MIIS). The MIIS sampler uses conditional importance sampling (IS) approximations to jointly sample the current state of the Markov Chain and estimate conditional expectations, possibly by incorporating a full range of variance reduction techniques. We compute Rao-Blackwellized estimates based on the conditional expectations to construct control variates for estimating expectations under the target distribution. The control variates are particularly efficient when there are substantial correlations between the variables in the target distribution, a challenging setting for MCMC. An important motivating application of MIIS occurs when the exact Gibbs sampler is not available because it is infeasible to directly simulate from the conditional distributions. In this case the MIIS method can be more efficient than a Metropolis-within-Gibbs approach. We also introduce the MIIS random walk algorithm, designed to accelerate convergence and improve upon the computational efficiency of standard random walk samplers. Simulated and empirical illustrations for Bayesian analysis show that the method significantly reduces the variance of Monte Carlo estimates compared to standard MCMC approaches, at equivalent implementation and computational effort.
A research on autonomous position method for mobile robot manipulator based on fusion feature

The cooperative work process and the automation work flow of the no man keeping watch library which takes the robot as its center and uses the internet of things technology are firstly introduced in this paper. A new data format of the RFID electronic label for books is designed, and the robustness of positioning of the manipulator is improved by the information fusion of the RFID label source and the CCD sensor source installed on the manipulator. A position method named THREE STEPS by the combination of fuzzy position, area position and accurate position is advanced, and the complex books positioning and grasping question is transformed into the world coordinate solving question of some point on the objective book. The fuzzy CMAC (cerebella model articulation controller) neuron network is adapted to realize the non-line relationship of fusion feature and manipulator position. And direct vision servo control architecture is used when designing the manipulator position system. The experiment result has proved this method can help the manipulator realize the accurate position and rapid catching.
Whole‐heart cine MRI using real‐time respiratory self‐gating

Two‐dimensional (2D) breath‐hold cine MRI is used to assess cardiac anatomy and function. However, this technique requires cooperation from the patient, and in some cases the scan planning is complicated. Isotropic nonangulated three‐dimensional (3D) cardiac MR can overcome some of these problems because it requires minimal planning and can be reformatted in any plane. However, current methods, even those that use undersampling techniques, involve breath‐holding for periods that are too long for many patients. Free‐breathing respiratory gating sequences represent a possible solution for realizing 3D cine imaging. A real‐time respiratory self‐gating technique for whole‐heart cine MRI is presented. The technique enables assessment of cardiac anatomy and function with minimum planning or patient cooperation. Nonangulated isotropic 3D data were acquired from five healthy volunteers and then reformatted into 2D clinical views. The respiratory self‐gating technique is shown to improve image quality in free‐breathing scanning. In addition, ventricular volumetric data obtained using the 3D approach were comparable to those acquired with the conventional multislice 2D approach. Magn Reson Med 57:606–613, 2007. © 2007 Wiley‐Liss, Inc.
Hardware-based hash functions for network applications

For rich network services, it is indispensable to reconstruct TCP stream in the middle of the network. In this reconstruction function, managing a large number of streams is a crucial as an embedded hardware for achieving high-throughput processing. In this case, hashing is a well-used solution as an ID key of TCP streams. Since CRC hashes have simple tree structure of XOR logics and are perfectly composed of combination logics, they can easily be hardware implemented and enables low-latency calculation of the hash values. Therefore, CRC hash has been used in network applications where low-latency and high speed operations are necessary. Some of their usages include data identification, load balancing, encryption, and checksum. Although CRC hashes may be well suited for hardware implementation, it is not the top choice when collision rates and distribution of hash values are considered. For some network workloads, CRC hashing shows very uneven distribution of hash values and can lead to decrease in the overall performance of network systems. In this study, two hashes, Jenkins hash and MurmurHash, are implemented on a FPGA as TCP connection manager and evaluated for their aptitude for hashing used in hardware-based network applications.
SiO in C-rich circumstellar envelopes of AGB stars: effects of non-LTE chemistry and grain adsorption

Aims. New SiO multi-transition millimetre line observations of a sample of carbon stars, including J = 8 → 7 observations with the APEX telescope, are used to probe the role of non-equilibrium chemistry and the influence of grains in circumstellar envelopes of carbon stars. Methods. A detailed radiative transfer modelling, including the effect of dust emission in the excitation analysis, of the observed SiO line emission is performed. A combination of low- and high-energy lines are important in constraining the abundance distribution. Results. It is found that the fractional abundance of SiO in these C-rich environments can be several orders of magnitude higher than predicted by equilibrium stellar atmosphere chemistry. In fact, the SiO abundance distribution of carbon stars closely mimic that of M-type (O-rich) AGB stars. A possible explanation for this behaviour is a shock-induced chemistry, but also the influence of dust grains, both as a source for depletion as well as production of SiO, needs to be further investigated. As observed for M-type AGB stars, a clear trend that the SiO fractional abundance decreases as the mass-loss rate of the star increases is found for the carbon stars. This indicates that SiO is accreted onto dust grains in the circumstellar envelopes.
CPHP_A_168956 1..17

What is the relation between the material, conventional symbol structures that we encounter in the spoken and written word, and human thought? A common assumption, that structures a wide variety of otherwise competing views, is that the way in which 10 these material, conventional symbol-structures do their work is by being translated into some kind of content-matching inner code. One alternative to this view is the tempting but thoroughly elusive idea that we somehow think in some natural language (such as English). In the present treatment I explore a third option, which I shall call the ‘‘complementarity’’ view of language. According to this third view the actual symbol 15 structures of a given language add cognitive value by complementing (without being replicated by) the more basic modes of operation and representation endemic to the biological brain. The ‘‘cognitive bonus’’ that language brings is, on this model, not to be cashed out either via the ultimately mysterious notion of ‘‘thinking in a given natural language’’ or via some process of exhaustive translation into another inner code. Instead, 20 we should try to think in terms of a kind of coordination dynamics in which the forms and structures of a language qua material symbol system play a key and irreducible role. Understanding language as a complementary cognitive resource is, I argue, an important part of the much larger project (sometimes glossed in terms of the ‘‘extended mind’’) of understanding human cognition as essentially and multiply hybrid: as involving 25 a complex interplay between internal biological resources and external non-biological resources.
Out-of-sequence packet arrivals due to layer 2 ARQ and its impact on TCP performance in W-CDMA networks

Data transmission over wireless networks has attracted more attention with the growth of the Internet and wireless networks. However, TCP is tuned to perform well in wired networks where the error rates are very low and packet losses occur mostly because of congestion. Therefore, the performance of TCP can be severely affected by high bit error rate characterizing wireless links. We clarify the performance of TCP over wireless links of IMT-2000 in which selective-repeat ARQ is performed for recovering lost PDUs of Layer 2 on the links. ARQ can cause out-of-order delivery of SDUs, which will result in TCP performance degradation. In particular we examine the impact of the timeout timer employed to maintain sequence integrity.
Dual Selective Code Compression

Code compression has been shown to be efficient in code size reduction and, recently, performance improvement and energy savings. In this paper we use a compression method, the ComPacket, which has a very fast decompressor in hardware, to compress selectively regions of the code to improve performance and complementary regions to sustain the code size reduction both at the same time. Using the leon (SPARC v8) platform and benchmarks from Mediabench and MiBench suites we reached, on average, 25% of code memory area reduction, and a speed-up of 1.5 simultaneously.
Gulfstream: Incremental Static Analysis for Streaming JavaScript Applications

The advent of Web 2.0 has led to the proliferation of client-side code that is typically written in JavaScript. Recently, there has been an upsurge of interest in static analysis of client-side JavaScript. However, most approaches in static analysis literature assume that the entire program is available to analysis. This, however, is in direct contradiction with the nature of Web 2.0 programs that are essentially being streamed at the user’s browser. Users can see data being streamed to pages in the form of page updates, but the same thing can be done with code, essentially delaying the downloading of code until it is needed. In essence, the entire program is never completely available, by interacting with the application, more and more code is sent over to the browser. This paper explores incremental static analysis as a way to analyze streaming JavaScript programs. In particular, we advocate the use of combined offline-online static analysis as a way to accomplish fast, online incremental analysis at the expense of a more thorough and costly offline analysis on the static code. We find that in normal use, where updates to the code are small, we can incrementally update static analysis results quickly enough to be acceptable for everyday use. We demonstrate this hybrid approach to be advantageous in a wide variety of settings, especially in mobile devices.
Key Management for Wireless Sensor Networks in Hostile Environments

Large-scale wireless sensor networks (WSNs) are highly vulnerable to attacks because they consist of numerous resource-constrained devices and communicate via wireless links. These vulnerabilities are exacerbated when WSNs have to operate unattended in a hostile environment, such as battlefields. In such an environment, an adversary poses a physical threat to all the sensor nodes. An adversary may capture any node, compromising critical security data including keys used for encryption and authentication. Consequently, it is necessary to provide security services to these networks to ensure their survival. We propose a novel, self-organizing key management scheme for large-scale and long-lived WSNs, called Survivable and Efficient Clustered Keying (SECK). SECK provides administrative services that ensures the survivability of the network. SECK is suitable for managing keys in a hierarchical WSN consisting of low-end sensor nodes clustered around more capable gateway nodes. Using cluster-based administrative keys, SECK provides five efficient security administration mechanisms: 1) clustering and key setup, 2) node addition, 3) key renewal, 4) recovery from multiple node captures, and 5) re-clustering. All of these mechanisms have been shown to localize the impact of attacks and considerably improve the efficiency of maintaining fresh session keys. Using simulation and analysis, we show that SECK is highly robust against node capture and key compromise while incurring low communication and storage overhead.
Plexe: A platooning extension for Veins

Cooperative driving in general and Cooperative Adaptive Cruise Control (CACC) or platooning in particular require blending control theory, communications and networking, as well as mechanics and physics. Given the lack of an integrated modeling framework and theory as well as the prohibitively high costs of using prototypes for what-if studies, simulation remains the fundamental instrument to evaluate entire cooperative driving systems. This work presents Plexe, an Open Source extension to Veins that offers researchers a simulation environment able to run experiments in realistic scenarios, taking into account physics and mechanics of the vehicles, communications and networking impairments, and Inter-Vehicle Communication (IVC) protocol stacks. Plexe is easily extensible and already implements protocols to support platooning and cooperative driving applications and several state of the art cruise control models. We describe the structure of the simulator and the control algorithms that Plexe implements and provide two use cases which show the potential of our framework as a powerful research tool for cooperative driving systems.
A Novel Genetic Algorithm for 3D Facial Landmark Localization

This paper introduces Evolutionary Putsuit (EP) as a novel approach for 3D facial landmark localization. Leveraging the global optimization ability of genetic algorithm (GA), an innovative framework for detecting local features, like nose tips and eye-corners, on 3D face data is proposed. Facial landmarks are localized by combining responses of several weak classifiers as basic building blocks. A genetic algorithm is employed for searching the optimal way to combine these building blocks, in order to construct a classifier with high detection accuracy. This GA-based facial landmark detection method is evaluated on the FRGC v1 database; it shows promising results that the GA searching process is able to produce an effective feature classifier from a relatively small training set, achieving a strong landmark localization ability.
Sparse method for ringing artifact detection

The problem of non-reference ringing detection is considered. The idea of the proposed method is to decompose the input edges into the sums of blurred edge, ringing oscillations and the residual using sparse representation approach with the pre-generated dictionary. Edges with ringing effect are modeled by applying ideal low-pass filter to the step edge with different cut-off frequencies. Then they are separated into edge and ringing components by subtracting the ideal step edge blurred with Gaussian filter. The shifted and rotated blur and ringing components form the dictionary used for sparse representation. The presented method performs well on images with ringing from different source types and is robust for the images with noise.
Learning a compact latent representation of the Bag-of-Parts model

The Bag-of-Parts (BoP) model, which employs distinctive parts to represent images, has shown superior performance in vision recognition tasks. Our work is motivated by the need of reducing redundancy in tens of thousands parts. We propose a novel method to learn a compact latent representation from redundant part responses. We address this problem by employing spectral clustering and a multi-column coding scheme. The BoP model is viewed as a multi-scale convolutional model and additional sparse autoencoders are used to infer the latent patterns embedded in high-dimensional part-based representations. Spatial and semantic information is preserved by sparse learning on multiple spatial regions individually. Experiments demonstrate that the learnt representation achieves competitive performance with state-of-the-art methods on PASCAL VOC 2007 dataset.
Defining knowledge of government human capital management: A qualitative study

Knowledge is organizational value, it represent organizational core business. Knowledge embodies in human capital as organizational intangible asset. Acquiring organizational knowledge and the types of knowledge is important to identify existing core knowledge and requirement of knowledge. Human capital management concept began to be famous in Indonesian government since a new legislation of civil servant was released. UU ASN No. 5 Year 2014 transform civil servant management activities into human capital management based. It's affect to the three government institution duties and functions as human capital managers they are KEMENPAN & RB, BKN and LAN. Identifying organizational knowledge is important to create an effective collaboration and managing knowledge to enhanced organizational performance. Qualitative analysis is used to process the expert interview data. Content analysis used to codify the knowledge. Result show that 18 core knowledge were identify, which is classify into organizational core knowledge in each government human capital managers and some of them are intersect with others.