Original Map Research Articles

Controllable Image Synthesis With Attribute-Decomposed GAN.

This paper proposes Attribute-Decomposed GAN (ADGAN) and its enhanced version (ADGAN++) for controllable image synthesis, which can produce realistic images with desired attributes provided in various source inputs. The core ideas of the proposed ADGAN and ADGAN++ are both to embed component attributes into the latent space as independent codes and thus achieve flexible and continuous control of attributes via mixing and interpolation operations in explicit style representations. The major difference between them is that ADGAN processes all component attributes simultaneously while ADGAN++ utilizes a serial encoding strategy. More specifically, ADGAN consists of two encoding pathways with style block connections and is capable of decomposing the original hard mapping into multiple more accessible subtasks. In the source pathway, component layouts are extracted via a semantic parser and the segmented components are fed into a shared global texture encoder to obtain decomposed latent codes. This strategy allows for the synthesis of more realistic output images and the automatic separation of un-annotated component attributes. Although the original ADGAN works in a delicate and efficient manner, intrinsically it fails to handle the semantic image synthesizing task when the number of attribute categories is huge. To address this problem, ADGAN++ employs the serial encoding of different component attributes to synthesize each part of the target real-world image, and adopts several residual blocks with segmentation guided instance normalization to assemble the synthesized component images and refine the original synthesis result. The two-stage ADGAN++ is designed to alleviate the massive computational costs required when synthesizing real-world images with numerous attributes while maintaining the disentanglement of different attributes to enable flexible control of arbitrary component attributes of the synthesized images. Experimental results demonstrate the proposed methods' superiority over the state of the art in pose transfer, face style transfer, and semantic image synthesis, as well as their effectiveness in the task of component attribute transfer. Our code and data are publicly available at https://github.com/menyifang/ADGAN.

Cyclic Self-attention for Point Cloud Recognition

Point clouds provide a flexible geometric representation for computer vision research. However, the harsh demands for the number of input points and computer hardware are still significant challenges, which hinder their deployment in real applications. To address these challenges, we design a simple and effective module named cyclic self-attention module (CSAM). Specifically, three attention maps of the same input are obtained by cyclically pairing the feature maps, thus exploring the features sufficiently of the attention space of the original input. CSAM can adequately explore the correlation between points to obtain sufficient feature information despite the multiplicative decrease in inputs. Meanwhile, it can direct the computational power to the more essential features, relieving the burden on the computer hardware. We build a point cloud classification network by simply stacking CSAM called cyclic self-attention network (CSAN). We also propose a novel framework for point cloud semantic segmentation called full cyclic self-attention network (FCSAN). By adaptively fusing the original mapping features and the CSAM extracted features, it can better capture the context information of point clouds. Extensive experiments on several benchmark datasets show that our methods can achieve competitive performance in classification and segmentation tasks.

CADASTRAL AND URBAN MAPS ENRICHMENTS USING SMART SPATIAL DATA FUSION

Abstract. Cadastral and urban map enrichment/upgrading is an essential requirement for smart urban management. The high pace of development and change in megacities can cause different challenges for urban organizations to reproduce their maps based on their need. New urban management aims and plans need new cadastral and urban maps with different standards and elements which may have existed in the other urban organization. Producing an original map or checking the maps of different organizations visually in a megacity is very costly and time-consuming. These challenges require an advanced integration approach to overcome them. Therefore, enriching maps with concerned organizations' maps and intelligent and automatically identifying as well as applying the changes in urban and cadastral maps will save time and cost for informed urban decision-making. This paper has employed the data of the third zone of the District six of the Municipality of Tehran, the capital of Iran, and identifies changes in the parcel’s geometry of the cadastre maps in comparison with the recently produced maps of the municipality of Tehran. After pre-processing the data, some spatial and attribute information are added to each feature, and the land parcels are enriched. By matching the algorithm and comparing the parcels geometry and attributes, suspicious parcels are identified by the logistic regression algorithm. The Accuracy and F1-Score of this model were 0.845 and 0.780, respectively. Finally, the suspicious parcels are checked and the parcels are located, deleted, merged, splitted and geometrically modified in the base map and the base map is enriched. This paper has successfully proposed a new framework for cadastral and urban map enrichment intelligently.

Generating a conformational landscape of ubiquitin chains at atomistic resolution by back-mapping based sampling.

Ubiquitin chains are flexible multidomain proteins that have important biological functions in cellular signalling. Computational studies with all-atom molecular dynamics simulations of the conformational spaces of polyubiquitins can be challenging due to the system size and a multitude of long-lived meta-stable states. Coarse graining is an efficient approach to overcome this problem-at the cost of losing high-resolution details. Recently, we proposed the back-mapping based sampling (BMBS) approach that reintroduces atomistic information into a given coarse grained (CG) sampling based on a two-dimensional (2D) projection of the conformational landscape, produces an atomistic ensemble and allows to systematically compare the ensembles at the two levels of resolution. Here, we apply BMBS to K48-linked tri-ubiquitin, showing its applicability to larger systems than those it was originally introduced on and demonstrating that the algorithm scales very well with system size. In an extension of the original BMBS we test three different seeding strategies, i.e. different approaches from where in the CG landscape atomistic trajectories are initiated. Furthermore, we apply a recently introduced conformational clustering algorithm to the back-mapped atomistic ensemble. Thus, we obtain insight into the structural composition of the 2D landscape and illustrate that the dimensionality reduction algorithm separates different conformational characteristics very well into different regions of the map. This cluster analysis allows us to show how atomistic trajectories sample conformational states, move through the projection space and in sum converge to an atomistic conformational landscape that slightly differs from the original CG map, indicating a correction of flaws in the CG template.

Generative Models of Multichannel Data from a Single Example—Application to Dust Emission

The quest for primordial B-modes in the cosmic microwave background has emphasized the need for refined models of the Galactic dust foreground. Here we aim at building a realistic statistical model of the multifrequency dust emission from a single example. We introduce a generic methodology relying on microcanonical gradient descent models conditioned by an extended family of wavelet phase harmonic (WPH) statistics. To tackle the multichannel aspect of the data, we define cross-WPH statistics, quantifying non-Gaussian correlations between maps. Our data-driven methodology could apply to various contexts, and we have updated the software PyWPH, on which this work relies, accordingly. Applying this to dust emission maps built from a magnetohydrodynamics simulation, we construct and assess two generative models: (1) a (I, E, B) multi-observable input, and (2) a multifrequency input. The samples exhibit consistent features compared to the original maps. A statistical analysis of model 1 shows that the power spectra, distributions of pixels, and Minkowski functionals are captured to a good extent. We analyze model 2 by fitting the spectral energy distribution (SED) of both the synthetic and original maps with a modified blackbody (MBB) law. The maps are equally well fitted, and a comparison of the MBB parameters shows that our model succeeds in capturing the spatial variations of the SED from the data. Besides the perspectives of this work for dust emission modeling, the introduction of cross-WPH statistics opens a new avenue to characterize non-Gaussian interactions across different maps, which we believe will be fruitful for astrophysics.

Anthony Jenkinson’s original map as a source on the history of the Safavid Empire

Anthony Jenkinson’s original map as a source on the history of the Safavid Empire

A Time-Delay Approach to Extremum Seeking with Large Measurement Delays*

In this paper, we present a time-delay approach to gradient-based extremum seeking (ES) with known large distinct measurement delays, for N-dimensional (ND) static quadratic maps. We assume that the Hessian has a nominal known part and norm-bounded uncertainty, the extremum point belongs to a known ball, and the extremum value to a known interval. By using the orthogonal transformation, we first transform the original static quadratic map into a new one with the Hessian containing a nominal diagonal part. By applying a time-delay approach to the resulting ES system, we arrive at the neutral type system with a nominal linear time-delay system. We further present this system as a retarded one and employ variation of constants formula for practical stability analysis. To obtain tight bounds we exploit positivity of the fundamental matrix that corresponds to the nominal system with delays. Explicit conditions in terms of simple scalar inequalities depending on tuning parameters and delay bounds are established to guarantee the practical stability of the ES control systems. An example from the literature illustrates the efficiency of the new approach.

Multimodal spatial-temporal feature representation and its application in action recognition

目的 在人体行为识别研究中，利用多模态方法将深度数据与骨骼数据相融合，可有效提高动作的识别率。针对深度图像信息数据量大、冗余度高等问题，提出一种通过获取关键时程信息动作帧序列降低冗余的算法，即质心运动路径松弛算法，并根据不同模态数据的特点，提出一种新的时空特征表示方法。方法 质心运动路径松弛算法根据质心在相邻帧之间的运动距离，计算图像差分后获得的活跃部分的相似系数，然后剔除掉相似度高的帧，获得足以表达行为的关键时程信息。根据图像动态部分的变化特性、人体各部分在运动中的协同性和局部显著性特征构建一种新的时空特征表示方法。结果 在MSR-Action3D数据集上对本文方法的效果进行验证。在3个子集中进行交叉验证的平均分类识别率为95.743 2%，分别比Multi-fused，CovP3DJ，D3D-LSTM（densely connected 3DCNN and long short-term memory），Joint Subset Selection方法高2.443 2%，4.763 2%，0.343 2%，0.213 2%。本文方法在使用完整数据集的扩展实验中进行交叉验证的分类识别率为93.040 3%，具有很好的鲁棒性。结论 实验结果表明，本文提出的去冗余算法在降低冗余后提升了识别效果，提取的特征之间具有相关性低的特点，在组合识别中具有良好的互补性，有效提高了分类识别的精确度。;Objective Human body motion-related recognition has been developing in the context of computer vision and pattern recognition like auxiliary human-computer interaction，motion analysis，intelligent monitoring，and virtual reality. To obtain two-dimensional information for its behavioral recognition，conventional motion behavior recognition is mainly used the RGB image sequence captured by RGB camera. To improve the ability to detect short-duration fragments，current feature descriptors for RGB image sequences are employed to characterize human behavior，such as histogram of oriented gradient（HOG），histogram of optical flow （HOF），and a three-dimensional feature pyramid. Some researchers are focused on the feature that image depth is insensitive to ambient light since RGB images are oriented to behavior image sequences of objects in terms of two-dimensional information. The depth information of the image is coordinated with the features of RGB image to describe the related behavior. Human behavior recognition-relevant multi-modal method can be used to fuse depth data and skeleton data，which can improve the recognition rate of action effectively. Recent depth map is widely used in relevant to human behavior recognition. But，the collection of depth information data is required to be optimized because of time complexity of feature extraction and space complexity of feature storage. To resolve the problems，we develop an algorithm to optimize frames of the depth map and resource consumption. At the same time，a new representation of motion features is facilitated as well according to the motion information of the centroid. Method First，the temporal feature vector is used in terms of depth map sequence-extracted time sequence information. The centroid motion path relaxation algorithm is used to realize depth image de-duplication and de redundancy，and the skeleton map-extracted spatial structure feature vector from are spliced to form the spatio-temporal feature input. Next，spatial features are extracted in terms of the original skeleton points coordinates-spliced three-channel spatial feature map. Finally，the fusion probability of spatio-temporal features and spatial features is used for classification and recognition. Our centroid motion path relaxation algorithm is focused on the optimization of redundant information，the time complexity of feature extraction，and the space complexity of feature storage. For the skeleton data，the global feature of motion direction is proposed to fully reflect the integrity and coordination of limb movements. The extracted features are concatenated to obtain the spatio-temporal feature vector，and they can be fused and enhanced through the original coordinates of skeleton points-built three-channel spatial feature map. Its effectiveness is verified on the MSR-Action3D dataset. Result The experimental setting 1 demonstrate that it is 0. 826 0% higher than the depth motion map（DMM）-local binary pattern（LBP）algorithm，1. 015 2% higher than DMM-CRC（collaborative representation classifier），3. 450 1% higher than gradient local auto correlation（DMM-GLAC） algorithm，0. 605 8% higher than EigenJoint algorithm，and 0. 605 8% higher than space-time auto correlation of gradient （STACOG）algorithm is 10. 624 5% higher. After removing redundancy，the result of experimental setting 1 is 0. 126 1% higher as well. The cross-validation on experimental setting 2 show that the average classification and recognition rate in the three subsets is 95. 743 2%，2. 443 2% higher than multi-fused method，4. 763 2% higher than CovP3DJ method，0. 343 2% higher than D3D-LSTM method，and 0. 213 2% higher than joint subset selection method. For the overall data set，it is 2. 030 3% higher than low latency method，0. 240 3% higher than combination of deep models method，and 2. 340 3% higher than complex network coding method. The experimental setting 2 illustrates that the average classification recognition rate of cross-validation in three subsets is 95. 743 2%，and the classification recognition rate of the complete dataset is 93. 040 3%. Conclusion Our algorithm proposed can improve the recognition effect based on redundancy-optimized，and the featuresextracted have lower correlation mutually，which can improve the accuracy of classification recognition effectively.

A Light Solution for Device Diversity Problem in a Wireless Local Area Network Fingerprint Indoor Positioning System

The development of location-based services requires an increasingly accurate positioning system technology. Research on outdoor positioning systems has achieved satisfactory accuracy and has been commonly used in various location-based services. The research trend is now shifting toward the Indoor Positioning System (IPS). One technique that is widely used in Wi-Fi-based IPS is fingerprinting. The fingerprinting technique on Wi-Fi uses the Received Signal Strength Indicator (RSSI) value. The problem that occurs is that the results of RSSI measurements on smartphones of different brands will produce different RSSI values, also known as device diversity. Device diversity will cause a decrease in system accuracy. This study aims to offer a solution to the problem of device diversity in Wi-Fi IPS based on RSSI Fingerprinting, i.e., to get a minor distance error. The proposed solution is to modify the original database radio map into two new databases: the difference database and the ratio database. The Difference Database Radiomap was able to reduce the average value of distance errors by 24.3% in Meizu and 28% in OPPO. Then, using the Radiomap database ratio, the average value of distance errors could be reduced by 13% in Meizu and 24% in OPPO. From the calculation, Radiomap database ratio can provide solutions to the problem of device diversity for an Indoor Positioning System better than the difference database radiomap if we looked at reduced distance error.

Safety Helmet Wearing Detection Model Based on Improved YOLO-M

In recent years, construction accidents have occurred frequently. The safety guarantee measures for construction personnel are thrown into sharp focus. Wearing helmets is one of the most important requirements to protect the safety of construction personnel, and the detection of wearing safety helmets has become necessary. For the problems of the existing helmet wearing detection algorithm such as too many parameters, substantial detection interferences, and low detection accuracy, in this paper a helmet wearing detection model YOLO-M is proposed. Firstly, MobileNetv3 is adopted as the backbone network of YOLOv5s for the feature extraction, which can reduce the number of model parameters and model size. Secondly, a residual edge is introduced in the feature fusion. The original feature map information is fused during feature fusion, and the detection ability of small targets is enhanced. At last, by changing the connection between CAM and SAM, a new attention module BiCAM is designed. The comparison experiments show that the detection accuracy of YOLO-M is 2.22% higher than YOLOv5s, and the model parameter quantity is reduced to 3/4 of YOLOv5s. Under the same detection conditions, the detection speed of YOLO-M is better than the other models, which meets the accuracy requirements of helmet detection in the construction scene.

GeSANet: Geospatial-Awareness Network for VHR Remote Sensing Image Change Detection

The characteristics of very high resolution (VHR) remote sensing images (RSIs) have higher spatial resolution inherently, and are easier to obtain globally compared with hyperspectral images (HSIs), making it possible to detect small-scale land cover changes in multiple applications. RSI change detection (RSI-CD) based on deep learning has been paid attention to and become a frontier research field in recent years, and is currently facing two challenging problems: The first is high dependence on registration between bi-temporal images caused by high spatial resolution; The other is high pseudo-change information response caused by low spectral resolution. In order to address the above-mentioned two problems, a novel RSI-CD framework called Geospatial-Awareness Network (GeSANet) based on the geospatial Position Matching Mechanism (PMM) with multi-level adjustment and the geo-spatial Content Reasoning Mechanism (CRM) with diverse pseudo-change information filtering is proposed. First of all, the PMM assigns independent two-dimensional offset coordinates to each position in the previous temporal image, afterwards, bilinear interpolation is employed to obtain the subpixel feature value after the offset, and the sparse results based on the difference are transmitted to the next level prediction to realize multi-level geospatial correction. The CRM extracts global features from the corrected sparse feature map in terms of dimensions, implementing effective discriminant feature extraction on basis of the original feature map in a stepwise refinement manner through the cross-dimension exchange mechanism, to filter out various pseudo-change information as well as maintain real change information. Comparison experiments with five recent SOTA methods are carried out on two popular datasets with diverse changes, the results show that the proposed method has good robustness and validity for multi-temporal RSI-CD. In particular, it has a strong comparative advantage in detecting small entity changes and edge details. The source code of the proposed framework can be downloaded from https://github.com/zxylnnu/GeSANet.

Research on Multi-Sensor Fusion SLAM Algorithm Based on Improved Gmapping

Simultaneous Localization and Mapping (SLAM) is the core technology of the intelligent robot system, and it is also the basis for its autonomous movement. In recent years, it has been found that SLAM using a single sensor has certain limitations, such as Inertial Measurement Unit (IMU) noise and serious drift, and 2D radar can only detect environmental information on the same horizontal plane. In this regard, this paper constructs a multi-sensor back-end fusion SLAM algorithm that combines vision, laser, encoder and IMU information. Experiments have proved that compared with using a single sensor, the application of a multi-sensor fusion system makes the edges of the constructed map clearer and the noise reduced. Aiming at the problem of increased calculation caused by particle degradation and too many particles, this paper improves the Gmappping algorithm, and uses the combination of selective resampling and Kullback-Leibler Distance (KLD) sampling to complete resampling. It has been proved by experiments that compared with the original algorithm of Gmapping, the application of the improved algorithm increases the particle convergence speed by 39.85% in the process of indoor mapping. Aiming at the problems that the traditional loop detection algorithm is easily affected by environmental factors, resulting in low detection accuracy, and the loop detection algorithm based on deep convolutional neural network has a large amount of calculation and takes a long time to detect. The main research of this paper is to apply a deep learning-based loop detection algorithm on the multi-sensor fusion framework, and use the combination of high-dimensional and low-dimensional features of the image for loop detection. This paper uses different algorithms to conduct comparative experiments on the dataset CityCentre. The experimental results show that compared with the traditional algorithms Bag of Words (BoW), AlexNet algorithm, VGG19 algorithm, and ResNet32 algorithm, the accuracy of the algorithm proposed in this paper has increased by 31.26%, 14.21%, 3.05%, and 1.56%, respectively. In addition, the comparison experiment results of SLAM mapping with the original Real-Time Appearance-Based Mapping (RTAB-MAP) algorithm prove that the loop closure detection algorithm based on deep learning proposed in this paper can enable the system to better build a globally consistent map, including more environmental information.

Combining Structured and Unstructured Data using Co-Attention for Car Price Prediction

Automatic price appraisal is a relatively new feature in e-commerce sites that aims to help sellers and buyers with price suggestions. To this end, we explore the problem of predicting prices from public online car listings. A listing usually contains the car specification along with a description in the form of unstructured text. We leverage these structured and unstructured information for predicting prices by combining them in a neural network model. Our model has a fully-connected neural branch for handling structured data that learn patterns from the tabular attributes; and another branch that models interactions between the tabular attributes and textual description by employing a co-attention mechanism. The combined outputs of these branches are fed into a regression layer that predicts the car price. The latent representation of the car listings, as mapped by the intermediate layers of the learned model, can be used as a feature set by any regression algorithm to perform car price prediction. This learned feature encodes to a vector the relevant aspects of the car listing, as demonstrated by our experiments. We evaluate our solution in a dataset that was built by scraping multiple car classifieds websites. Our findings show that: (1) our network achieved lower prediction errors than the evaluated regressors using the same input features; (2) by using our learned representation, the regressors surpassed their performance using the original feature mapping; and (3) more competitive algorithms, such as LightGBM and Random Forest, using the raw features, are outperformed by simple linear models using our embeddings.

From floor to roof of a batholith: geology and petrography of the north-eastern Serre Batholith (Calabria, southern Italy)

ABSTRACT We present an original geological map of the north-eastern Serre Batholith (southern Italy), together with new field and petrographic data of its main five granitoid units. Our study provides an overall picture of a c. 13-thick floor-to-roof batholith exposure, focusing on the relationships between the magmatic units, which were sequentially emplaced at depth from c. 23 to c. 6 km, in a time interval from c. 297 Ma to c. 292 Ma. Indeed, this composite and zoned batholith, with its crustal scale exposure and large compositional and structural diversity, is a real natural laboratory where to test models of granitoid magma production and batholith construction. The new geological map and related field and petrographic data provide a valuable addition to the existing knowledge of the Serre Batholith and, at the same time, a new starting point for further in-depth multidisciplinary investigations aimed to better understand its architecture and build-up mechanisms.

Reconstruction of Metaphorical Mapping as an Instrument of the Pre-translation Analysis of Poetry

Although it can help avoid far too literal translations of metaphorical expressions, the reconstruction of metaphorical mapping is still an unclaimed instrument of pre-translation analysis. This research aims at a pre-translation analysis of a book of poems, Babylon in a Jar (1998) by Andrew Hudgins, which has not been previously translated into Russian. The method for linguistic metaphor identification created by G. Steen is applied for the first time in this study as an instrument of pre-translation analysis. Firstly, key conceptual metaphors of the book are identified: death is departure, life is a journey, plants are people, and emotional intimacy is physical closeness. Secondly, the relationship between source and target domains is analysed. For example, the source domain for the metaphor people are plants is a daffodil, a tree, or a chinaberry. In Western cultures, a chinaberry symbolizes the tree of knowledge and in A. Hudgins’ poem, this idea is mapped onto knowledge about sinful human nature. Thirdly, translation strategies are outlined. In the translated version, another plant is introduced to preserve the original cross-domain mapping because the chinaberry tree is not familiar to Russian readers. Based on conceptual metaphor theory, this research seeks to integrate the reconstruction of metaphorical mapping into a pre-translation analysis, which allows for the preservation of the spirit of the original version. These insights advance interdisciplinary research on poetry interpretation and the practice of translation.

Purification, full-length sequencing and genomic origin mapping of eccDNA.

Extrachromosomal circular DNA (eccDNA) was discovered more than half a century ago. However, its biogenesis and function have just begun to be elucidated. One hurdle that has prevented our understanding of eccDNA is the difficulty in obtaining pure eccDNA from cells. The current eccDNA purification methods mainly rely on depleting linear DNAs by exonuclease digestion after obtaining crude circles by alkaline lysis. Owing to eccDNA's low abundance and heterogeneous size, the current purification methods are not efficient in obtaining pure eccDNA. Here we describe a new three-step eccDNA purification (3SEP) procedure that adds a step to recover circular DNA, but not linear DNA that escape from the exonuclease digestion, whereby 3SEP results in eccDNA preparations with high purity and reproducibility. Additionally, we developed a full-length eccDNA sequencing technique by combining rolling-circle amplification with Nanopore sequencing. Accordingly, we developed a full-length eccDNA caller (Flec) to call the consensus sequence of multiple tandem copies of eccDNA contained within the debranched rolling-circle amplification product and map the consensus to its genomic origin. Collectively, our protocol will facilitate eccDNA identification and characterization, and has the potential for diagnostic and clinical applications. For a well-trained molecular biologist, it takes ~1-2 d to purify eccDNAs, another 5-6 d to carry out Nanopore library preparation and sequencing, and 1-5 d for an experienced bioinformatic scientist to analyze the data.

Run-Time Remapping Algorithm of Dataflow Actors on NoC-Based Heterogeneous MPSoCs

Multiprocessor system-on-chip (MPSoC) platforms have been emerging as the main solution to cope with processor frequency ceiling and power density issues while still improving performances. Then, network-on-chip (NoC) has been adopted to provide the increasing number of processors with the required communication bandwidth as well as with the necessary flexibility. Video processing and streaming applications are adopting dynamic dataflow model of computation as the need for high performance parallel computing is growing. Dataflow applications executed on modern MPSoC-based architectures are becoming increasingly dynamic and more data-dependent. Different tasks execute concurrently with significant modifications in their workloads and resource demanding over time depending on the input data. Hence, adopting any static or offline dynamic scheduling for mapping tasks will not cope with the computation variations. This article introduces an original run-time mapping algorithm based on the Move Based (MB) method targeting a dedicated heterogeneous NoC-based MPSoC architecture to achieve workload balancing and optimized communication traffic. The performance of the proposed algorithm is verified by conducting cycle-accurate SystemC simulations of the adopted NoC implementing a real MPEG4-SP decoder. The obtained results reveal the effectiveness of our proposed algorithm. For various real-life videos, the proposed algorithm systematically succeeded to enhance significantly the performance.

One Click Is Not Enough: Anatomy-Based Fitting in Experienced Cochlear Implant Users.

To evaluate a new methodological approach of applying anatomy-based fitting (ABF) in experienced cochlear implant (CI) users. Three experienced unilateral and bilateral CI users with postlingual hearing loss. Postoperative imaging, via a high-volume Dyna computed tomography, and exact electrode measurement positions were integrated into the clinical fitting software following a new procedure, which adapted individual frequency bandwidths within the audio processor. Speech perception in quiet and noise, clinical mapping, and self-perceived level of auditory benefit were assessed. For each CI user, ABF mapping provided better speech perception in quiet and in noise compared with the original clinical fitting mapping. In addition, ABF mapping was accepted in CI users despite unequal bilateral array insertion depths and lengths; however, acceptance was only established if the point of first electrode contact was less than 230 Hz. ABF mapping increased the acceptance in CI users with longer electrode arrays and in bilateral CI users who were unsatisfied with their device experience. A larger prospective, randomized investigation is currently underway to assess longitudinal outcomes with ABF mapping.

Revision of the genus Piptatherum P. Beauv. (Poaceae) in Bosnia and Herzegovina

The paper reviews the results of detailed revision of herbarium material of the genus Piptatherum from Poaceae collection kept in the Herbarium of the National Museum of Bosnia and Herzegovina (SARA). Revision of the genus Piptatherum P. Beauv. in Bosnia and Herzegovina is presented, with brief nomenclature citations and the key for identification. The original photographs and distributional map, accompany the synopsis for each taxon: Piptatherum holciforme (M. Bieb.) Roem. & Schult. subsp. holciforme, P. miliaceum (L.) Coss. subsp. miliaceum, P. miliaceum subsp. thomasii (Duby) Freitag and P. virescens (Trin.) Boiss.

Robot path planning based on concept lattice

Path planning is a popular topic in research on mobile robots. In this paper, we apply the concept lattice to the path planning problem for the first time in the literature to propose a static path planning algorithm. The given grid map is first transformed into the formal context of the grid, and locational relations between rectangular regions are mapped into partial-order relations in the graph of rectangular regions based on a lattice of region concepts. Following this, the path planning problem on the original grid map is transformed into a path searching problem in the graph of rectangular regions. The graph derived from the sublattice of region concepts is used to avoid the high time complexity of applying the complete concept lattice. Finally, a novel path planning algorithm that can generate a path consisting of rectangular regions is constructed. This path can also be converted into the path consisting of inflection points. The results of simulations verified the effectiveness of the proposed method. It can generate a path with significantly fewer inflection points than the A* algorithm for a four-direction (left, right, front, back) mobile robot.