Miracle-dz/UniFAR-FTUs huggingface dataset:
Ground or obstacles? Detecting clear paths in vehicle navigation

Efficient estimation of obstacles is a crucial component in autonomous vehicle navigation. Fusing stereo vision and laser scan data is a widely used approach for obstacle detection and navigation. Stereo vision can detect obstacles and the ground. Planar laser range finders can detect obstacles, but false positives occur when the scans collide with the ground. This paper proposes an algorithm for cooperative fusion of stereo vision and laser data. The algorithm checks the consensus of the two signals before filtering out the false positive laser scans from ground hits. Experimental results for tests in real driving situations in an industrial environment are presented. The results confirm the effectiveness of the proposed algorithm for robust and safe navigation.
Association of Dopamine D2 Receptor Gene with Creative Ideation

Although several studies suggest that dopamine D2 receptor (DRD2) gene may contribute to creativity, the relationship between DRD2 and creativity still needs to be further validated. To further test the relevance of DRD2 and creativity, this study explored the association between DRD2 and creative ideation in 483 unrelated healthy Chinese undergraduate students. A total of 15 single nucleotide polymorphisms (SNPs) covering the DRD2 were genotyped, and creative ideation was assessed by the Runco Ideational Behavior Scale (RIBS). Single SNP analysis showed that 2 SNPs (rs4648317 and rs4938019) were nominally associated with fluency, 4 SNPs (rs4648317, rs4938019, rs4648319, and rs1800497) were nominally associated with flexibility, and 1 SNP (rs4648317) was nominally associated with originality. Haplotype analysis showed several haplotypes were nominally associated with various creative ideation indexes. However, none of these nominal associations survived correction for multiple testing. Overall, this study provides suggestive evidence for the genetic impact of DRD2 on creative ideation and supports the assumption that the genotype variations in DRD2 contribute to creativity.
A wireless full-duplex and multi-hop network with collision avoidance using directional antennas

Wireless full-duplexing enables multi-hop wireless networks to increase network performance, such as the end-to-end throughput. However, the combination of wireless full-duplexing and multi-hop communication produces a secondary transmission collision problem due to the increase of transmission opportunities. This paper proposes directional asynchronous full-duplex medium access control (DAFD-MAC) to avoid interference on primary and secondary nodes. DAFD-MAC suppresses the secondary transmission collision problem by sending a data frame with directional antennas and also a Network Allocation Vector (NAV) frame while receiving the data frame. The simulation results show that the proposed DAFD-MAC outperforms carrier sense multiple access/collision avoidance (CSMA/CA), full-duplex MAC (FD-MAC), and multi-hop full-duplex MAC (MFD-MAC).
A dynamic predictive framework for e-business workload management

Distributed e-business application platforms such as the web services framework will require sophisticated workload management infrastructures for the routing of client requests to the most appropriate services. This paper introduces a novel and dynamic predictive framework that uses both analytical performance modelling and historical data to extrapolate a request’s predicted performance under varying workloads and conditions on different systems. Such a framework can facilitate a routing algorithm in meeting quality of service-based metric requirements. An e-business benchmark is used as a demonstrator to provide an insight into the techniques used in predicting request response times.
Influence of excitation light rejection on forward model mismatch in optical tomography

Fluorescence enhanced tomography for molecular imaging requires low background for detection and accurate image reconstruction. In this contribution, we show that excitation light leakage is responsible for elevated background and can be minimized with the use of gradient index (GRIN) lenses when using fibre optics to collect propagated fluorescence light from tissue or other biological media. We show that the model mismatch between frequency-domain photon migration (FDPM) measurements and the diffusion approximation prediction is decreased when GRIN lenses are placed prior to the interference filters to provide efficient excitation light rejection. Furthermore, model mismatch is correlated to the degree of excitation light leakage. This work demonstrates the importance of proper light filtering when designing fluorescence optical imaging and tomography.
Generation of skeletal muscle cells from embryonic and induced pluripotent stem cells as an in vitro model and for therapy of muscular dystrophies

• Introduction • Transdifferentiation of somatic cells in skeletal myoblasts • Myogenic cell induction from ESCs ‐ The first method: generation of MMPs from ESCs and their differentiation into myogenic cells ‐ Isolation of MMPs ‐ Differentiation into skeletal myoblasts ‐ The second method: EB generation and differentiation into skeletal muscle ‐ Formation of EBs ‐ Differentiation of EBs into skeletal myoblasts • Protocols for myogenic cell induction from iPSCs • Mouse and human‐pluripotent derived myocytes as an in vitro model of MDs • Therapeutic development based on ESC‐ or iPSC‐derived cells • Conclusion
ON THE PERFORMANCE OF SEMI-AUTOMATIC BUILDING EXTRACTION

A Semi-Automatic Building Extraction system using two or more digitized overlapping aerial images has been enhanced by increased automation for the measurement of saddle-back-roof buildings, hip-roof buildings and boxes. All newly developed modules have been incorporated in the object oriented design of the system. The new methods consist of a ground-point and roof-top matching tool and a robust determination of shape parameters, like e.g. gutter length and width. The current performance of building extraction is quantitatively and qualitatively evaluated. We examine the increased efficiency using the automated tools, the success rate of individual modules and the overall success rate using a combination of methods. A methodology for quantitative comparison is tested on footprints of buildings from classical stereo measurements and from semi-automatic measurements. A qualitative comparison in 3D of multiple measurements of complete buildings is performed on three different datasets.
A Knowledge Based Method for Chinese Word Sense Induction

Word sense induction is usually viewed as a cluster problem in natural language processing. The context of the target word is represented as a vector and the cluster algorithms such as k-means, EM are applied. Different from the traditional methods, we proposed a new way based on “one sense per collocation” assumption which is proposed by Yarwosky (1993). Each sentence which contains the polysemous words is first parsed by Stanford parser, in order to find the collocation word of the polysemous word. Then, according to the collocation words’ semantic category, the sentences are divided into different clusters. The experiments were run on the benchmark data set, and the results show the effect of the method.
On the Necessity of U-Shaped Learning

A U-shaped curve in a cognitive-developmental trajectory refers to a three-step process: good performance followed by bad performance followed by good performance once again. U-shaped curves have been observed in a wide variety of cognitive-developmental and learning contexts. U-shaped learning seems to contradict the idea that learning is a monotonic, cumulative process and thus constitutes a challenge for competing theories of cognitive development and learning. U-shaped behavior in language learning (in particular in learning English past tense) has become a central topic in the Cognitive Science debate about learning models. Antagonist models (e.g., connectionism versus nativism) are often judged on their ability of modeling or accounting for U-shaped behavior. The prior literature is mostly occupied with explaining how U-shaped behavior occurs. Instead, we are interested in the necessity of this kind of apparently inefficient strategy. We present and discuss a body of results in the abstract mathematical setting of (extensions of) Gold-style computational learning theory addressing a mathematically precise version of the following question: Are there learning tasks that require U-shaped behavior? All notions considered are learning in the limit from positive data. We present results about the necessity of U-shaped learning in classical models of learning as well as in models with bounds on the memory of the learner. The pattern emerges that, for parameterized, cognitively relevant learning criteria, beyond very few initial parameter values, U-shapes are necessary for full learning power! We discuss the possible relevance of the above results for the Cognitive Science debate about learning models as well as directions for future research.
Detecting Characteristic Curves on Point Sets

We perform an effective algorithm for detecting characteristic curves on point sets. Our implication based on multi-step refinement operations: feature points are detected according to the biggish principal curvature of each point, and then enhance the points by projecting them onto their local nearest potential feature curves. The results indicate that our algorithm is sensitive to both sharp and smooth characteristic curves of point set, and it supports multi-resolution extraction. Keywords-ridges; valleys; valley-ridge extraction; MLS surface fitting; Characteristic Curves.
A genetic algorithm based scheduler for cloud environment

Cloud Computing is a computing model that is widely accepted both in the academia and industry mainly because it offers resources on-demand and they are rapidly provisioned. Such a provisioning sytem calls for efficient scheduling mechanisms for allocation and de-allocation of resources. A good scheduling mechanism should satisfy the QoS requirements of the user and at the same time make an efficent utilization of resources. Scheduling algorithms which are application-centric tend to optimize the performance of each individual application whereas those that are resource-centric tend to optimize the performance of each resource. Hence there exists a need for a good scheduling algorithm that optimizes both these factors. This paper proposes a bi-ojective Genetic Algorithm based scheduler for cloud that optimizes the makespan (application-centric) and average processor utilization (resource-centric).
Aligning Data Records Using WordNet

Current automatic wrappers using DOM tree to align data records generally have limitations such as the inability to align iterative (repetitive and similar) and disjunctive (optional) data items. Our study on the properties of data records shows that these data items can be aligned based on their semantic properties. In this context, we propose an ontological technique using existing lexical database for English (WordNet) for the alignment of data records. Regular expression rules are developed to align the data items extracted so that they can be used for further processing. Experimental results indicate that our technique is robust and performs better than the existing state of the art wrappers.
Enabling mobility in heterogeneous wireless sensor networks cooperating with UAVs for mission-critical management

Wireless sensor networks have the promise of revolutionizing the capture, processing, and communication of mission-critical data for the use of first operational forces. Their low cost, low power, and size make it feasible to embed them into environment monitoring tags in critical care regions, first responders uniform gear, and data collector sinks attached to unmanned aerial vehicles. The ability to actively change the location of sensors can be used to mitigate some of the traditional problems associated with static sensor networks. On the other hand, sensor mobility brings its own challenges. These include challenges associated with in-network aggregation of sensor data, routing, and activity monitoring of responders. Moreover, all different mobility patterns (e.g., sink mobility, sensor mobility) have their special properties, so that each mobile device class needs its own approach. In this article, we present a platform which benefits from both static and mobile sensors and addresses these challenges. The system integrates WSNs, UAVs, and actuators into a disaster response setting, and provides facilities for event detection, autonomous network repair by UAVs, and quick response by integrated operational forces.
Formal approach and automated tool for constructing ontology from object-oriented database model

Extracting domain knowledge from databases can facilitate the development of Web ontologies. In this paper, a formal approach and an automated tool for constructing ontologies from Object-oriented database models (OODMs) are developed. The approach and tool can automatically translate an OODM and its corresponding database instances into the ontology structure and ontology instances, respectively. Case studies show that the approach is feasible and the automated construction tool is efficient.
ISP and Egress Path Selection for Multihomed Networks

Multihoming has been used by stub networks for several years as a form of redundancy, improving the availability of Internet access. More recently, Intelligent Route Control (IRC) products allow multihomed networks to dynamically switch parts of their egress or ingress traffic between ISPs, also improving cost and performance. IRC products assume that the set of upstream ISPs is given and fixed. Typically, however, a multihomed network has several ISP choices and the actual selection of ISPs can significantly affect cost, availability, and performance. In the first part of this work, we develop a methodology to select the best set of upstream ISPs, optimizing monetary cost and availability. Our results, based on measurements of actual Internet traffic and topology, show that the proposed algorithm selects the best possible set of ISPs in terms of resiliency to inter-AS single-link failures. The algorithm also performs well in the presence of double or triple link failures. In the second part of this work, we focus on the egress path selection problem. Specifically, we propose a stochastic search algorithm, based on simulated annealing, to allocate the network’s egress traffic between upstream ISPs. The objectives are to minimize cost, also ensuring that the selected paths to the major destinations of egress traffic are congestion-free. Simulation results show that the proposed algorithm performs very well in meeting the previous objectives, when congestion-free paths exist.
Concurrency Verification: Introduction to Compositional and Noncompositional Methods

This is a systematic and comprehensive introduction both to compositional proof methods for the state-based verification of concurrent programs, such as the assumption-commitment and rely-guarantee paradigms, and to noncompositional methods, whose presentation culminates in an exposition of the communication-closed-layers (CCL) paradigm for verifying network protocols. Compositional concurrency verification methods reduce the verification of a concurrent program to the independent verification of its parts. If those parts are tightly coupled, one additionally needs verification methods based on the causal order between events. These are presented using CCL. The semantic approach followed here allows a systematic presentation of all these concepts in a unified framework which highlights essential concepts. The book is self-contained, guiding the reader from advanced undergraduate level to the state-of-the-art. Every method is illustrated by examples, and a picture gallery of some of the subject's key figures complements the text.
Documenting Children's Spiritual Development in a Preschool Program

The teachers of Northminster Learning Center, a faith-based early childhood education program, are adept at documenting and sharing children's learning. They photograph children involved in Godly Play and religious activities, record what children say and do, and engage children in conversation about spiritual issues. When they combined a study group on children's spirituality with increased documentation they were surprised at the depth of children's thoughts. A documentation display of what they learned not only communicated how Christian formation occurs in the program but also significantly changed viewers ideas about the importance of spirituality in the early years.
Languages flex cultural thinking

Recent studies have revealed remarkable interactions between language and emotion. Here, we show that such interactions influence judgments made regarding cultural information. Balanced Welsh–English bilinguals categorized statements about their native Welsh culture as true or false. Whilst participants categorized positive statements as true when they were true, they were biased towards categorizing them as true also when they were false, irrespective of the language in which they read them. Surprisingly, participants were unbiased when categorizing negative statements presented in their native language Welsh, but showed a reverse bias - categorizing sentences as false, even when they were true - for negative statements when they read them in English. The locus of this behavior originated from online semantic evaluation of the statements, shown in corresponding modulations of the N400 peak of event-related brain potentials. These findings suggest that bilinguals perceive and react to cultural information in a language-dependent fashion.
Genesis Dynamic Collaborative Business Process Formulation Based on Business Goals and Criteria

This technical paper discusses the architectural design and implementation details of Genesis – a novel Web application which formulates business process definitions dynamically, given a user business goal and underlying business criteria (e.g. total order cost, type of sourcing methods, etc.). Guided by an algorithm that references a hierarchical ontology file containing business process task decomposition, Genesis traverses through the ontology and dynamically produces two types of output: (1) a graphical breakdown of task sequences and decompositions required to fulfill the user’s business goals, and (2) an abstract BPEL file containing the control flow structures, and web service invocation points needed to execute the collaborative business processes in a service-oriented environment. The outputs demonstrate the potential of Genesis as a standalone module which can provide dynamic capabilities, thereby complementing current service-oriented architecture (SOA) business-to-business (B2B) information systems which require hard-coded, inflexible business process definitions. An online demonstration of Genesis is available at http://imss.simtech. a-star.edu. sg/Genesis/.
Buddy-RAM: Improving the Performance and Efficiency of Bulk Bitwise Operations Using DRAM

Bitwise operations are an important component of modern day programming. Many widely-used data structures (e.g., bitmap indices in databases) rely on fast bitwise operations on large bit vectors to achieve high performance. Unfortunately, in existing systems, regardless of the underlying architecture (e.g., CPU, GPU, FPGA), the throughput of such bulk bitwise operations is limited by the available memory bandwidth. We propose Buddy, a new mechanism that exploits the analog operation of DRAM to perform bulk bitwise operations completely inside the DRAM chip. Buddy consists of two components. First, simultaneous activation of three DRAM rows that are connected to the same set of sense amplifiers enables us to perform bitwise AND and OR operations. Second, the inverters present in each sense amplifier enables us to perform bitwise NOT operations, with modest changes to the DRAM array. These two components make Buddy functionally complete. Our implementation of Buddy largely exploits the existing DRAM structure and interface, and incurs low overhead (1% of DRAM chip area). Our evaluations based on SPICE simulations show that, across seven commonly-used bitwise operations, Buddy provides between 10.9X---25.6X improvement in raw throughput and 25.1X---59.5X reduction in energy consumption. We evaluate three real-world data-intensive applications that exploit bitwise operations: 1) bitmap indices, 2) BitWeaving, and 3) bitvector-based implementation of sets. Our evaluations show that Buddy significantly outperforms the state-of-the-art.
Analysis of hepatitis C virus RNA dimerization and core–RNA interactions

The core protein of hepatitis C virus (HCV) has been shown previously to act as a potent nucleic acid chaperone in vitro, promoting the dimerization of the 3′-untranslated region (3′-UTR) of the HCV genomic RNA, a process probably mediated by a small, highly conserved palindromic RNA motif, named DLS (dimer linkage sequence) [G. Cristofari, R. Ivanyi-Nagy, C. Gabus, S. Boulant, J. P. Lavergne, F. Penin and J. L. Darlix (2004) Nucleic Acids Res., 32, 2623–2631]. To investigate in depth HCV RNA dimerization, we generated a series of point mutations in the DLS region. We find that both the plus-strand 3′-UTR and the complementary minus-strand RNA can dimerize in the presence of core protein, while mutations in the DLS (among them a single point mutation that abolished RNA replication in a HCV subgenomic replicon system) completely abrogate dimerization. Structural probing of plus- and minus-strand RNAs, in their monomeric and dimeric forms, indicate that the DLS is the major if not the sole determinant of UTR RNA dimerization. Furthermore, the N-terminal basic amino acid clusters of core protein were found to be sufficient to induce dimerization, suggesting that they retain full RNA chaperone activity. These findings may have important consequences for understanding the HCV replicative cycle and the genetic variability of the virus.
A HAPTIC BASE HUMAN ROBOT INTERACTION APPROACH FOR ROBOTIC GRIT BLASTING

This paper proposes a remote operation method for a robot arm in a complex environment by using the Virtual Force (VF) based approach. A virtual robot arm is manipulated by a steering force, at the end-effecter, which is generated according to the movement of a feedback haptic. A three-dimensional force field (3D-F 2 ) is employed in collision detection and avoidance. Repulsive forces from the 3D-F 2 are produced and feedback to the haptic device that enables the operator to have a sense of touch on the encountered obstacle and then steer the arm to avoid it. As a result, collision-free poses of the virtual robot arm can then be used to command the real robot. Experiments are conducted in a mock up bridge environment where the real robot arm is steered to target points by the operator. Experiment results have shown successful collision avoidance and emulation of the actual command force and the virtual forces in remote operations.
Annotations for (more) Precise Points-to Analysis

We extend an existing points-to analysis for Java in two ways. First, we fully support . NET which has structs and parameter passing by reference. Second, we increase the precision for calls to nonanalyzable methods. A method is non-analyzable when its code is not available either because it is abstract (an interface method or an abstract class method), it is virtual and the callee cannot be statically resolved, or because it is implemented in native code (as opposed to managed bytecode). For such methods, we introduce extensions that model potentially affected heap locations. We also propose an annotation language that permits a modular analysis without losing too much precision. Our annotation language allows concise specification of points-to and read/write effects. Our analysis infers points-to and read/effect information from available code and also checks code against its annotation, when the latter is provided.