Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized 8-PSK for totally blind channel estimation in OFDM

Channel estimation in OFDM systems can conveniently be done by inserting a stream of pilot symbols at the transmitter and by using FIR interpolation filters at the receiver. The drawback of this method is the decrease in spectral efficiency due to the pilot symbols. Alternatively, blind channel estimation makes pilot symbols unnecessary. Most blind channel estimation approaches are based on higher order statistics and converge slowly, making them unsuitable for mobile environments. Moreover, the channel estimate suffers from a phase blindness, which can only be resolved by pilot symbols. The concept of totally blind channel estimation makes pilots completely unnecessary, and even works in rapidly time varying environments. This is achieved by using two different modulation schemes, such as QPSK and 3-PSK, on adjacent subcarriers. We further develop the concept of totally blind channel estimation by applying a regular 8-PSK and a generalized 8-PSK to achieve totally blind channel estimation without the need for any pilot symbols. We enhance the original receiver design by applying a two-dimensional a posteriori probability (APP) calculation algorithm. We evaluate our system at high Doppler frequencies with COST 207 channels on the basis of extrinsic information transfer (EXIT) and BER charts.
An efficient implementation of an implicit FEM scheme for fractional-in-space reaction-diffusion equations

Fractional differential equations are becoming increasingly used as a modelling tool for processes with anomalous diffusion or spatial heterogeneity. However, the presence of a fractional differential operator causes memory (time fractional) or nonlocality (space fractional) issues, which impose a number of computational constraints. In this paper we develop efficient, scalable techniques for solving fractional-in-space reaction diffusion equations using the finite element method on both structured and unstructured grids, and robust techniques for computing the fractional power of a matrix times a vector. Our approach is show-cased by solving the fractional Fisher and fractional Allen-Cahn reaction-diffusion equations in two and three spatial dimensions, and analysing the speed of the travelling wave and size of the interface in terms of the fractional power of the underlying Laplacian operator.
Fractionation of SWNT/nucleic acid complexes by agarose gel electrophoresis

We show that aqueous dispersions of single-walled carbon nanotubes (SWNTs), prepared with the aid of nucleic acids (NAs) such as RNA or DNA, can be separated into fractions using agarose gel electrophoresis. In a DC electric field, SWNT/NA complexes migrate in the gel in the direction of positive potential to form well-defined bands. Raman spectroscopy as a function of band position shows that nanotubes having different spectroscopic properties possess different electrophoretic mobilities. The migration patterns for SWNT/RNA and SWNT/DNA complexes differ. Parallel elution of the SWNT/NA complexes from the gel during electrophoresis and subsequent characterization by AFM reveals differences in nanotube diameter, length and curvature. The results suggest that fractionation of nanotubes can be achieved by this procedure. We discuss factors affecting the mobility of the nanotube complexes and propose analytical applications of this technique.
Model-based calculating tool for pollen-mediated gene flow frequencies in plants

This study provide a model-based calculating tool/software that can accurately calculate pollen-mediated gene flow (PMGF) frequencies of wind-pollinated plant species by including five biological and climatic parameters. The calculating tool can easily be used by any users who are not familiar with mathematical modeling. The required parameters can be measured directly in the field without conducting PMGF experiments, which makes this tool practical to estimate PMGF frequencies. This tool will be helpful to estimate transgene flow and its potential impacts, in addition to determining isolation distances between GE and non-GE crops.
A Taint Based Approach for Smart Fuzzing

Fuzzing is one of the most popular test-based software vulnerability detection techniques. It consists in running the target application with dedicated inputs in order to exhibit potential failures that could be exploited by a malicious user. In this paper we propose a global approach for fuzzing, addressing the main challenges to be faced in an industrial context: large-size applications, without source code access, and with a partial knowledge of the input specifications. This approach integrates several successive steps, and we mostly focus here on an important one which relies on binary-level dynamic taint analysis. We summarize the main problems to be addressed in this step, and we detail the solution we implemented to solve them.
Psychological Contract Violations and Employees’ Work Behavior Reactions: the Moderating Effect of Job Opportunities

There are many previous discussions about how employees perceive and react to PC violations with job alternatives as moderators. However, if the moderating effects differed according to the type of job alternatives is still unclear. The purpose of the study was to explore how employees’ perceptions of internal/external alternative job opportunities played a role in the relationship between psychological contract (PC) violations and work behavior reactions. Using a questionnaire, the study surveyed 251 participants from two manufacturing companies in Taiwan. Two types of primary statistical techniques were used. First, a series of MANOVAs was conducted to determine participants' work behavior reaction patterns (loyalty, exit, neglect, and voice) through their perceptions of PC violations by the differences of alternative job opportunities. Second, hierarchical regression analyses were conducted to test the moderating effects of alternative job opportunities on the relationship between PC violations and work behavior reactions. Two primary findings were reached. First, there ! "#$%&'(&)*%2007 12 57~78 57
"This is how I want to learn": High Functioning Autistic Teens Co-Designing a Serious Game

This paper presents a project that developed a Serious Game with a Natural User Interface, via a Participatory Design approach with two adolescents with High-Functioning Autism (HFA). The project took place in a highly specialized school for young people with Special Educational Needs (SEN). The teenagers were empowered by assigning them specific roles across several sessions. They could express their voice as user, informant, designer and tester. As a result, teachers and young people developed a digital educational game based on their experience as video gamers to improve academic skills in Geography. This paper contributes by describing the sensitive and flexible approach to the design process which promoted stakeholders' participation.
A low cost Bi-directional grid tied solar PV microinverter

This paper presents a new microinverter topology focused for the cost driven market in the developing countries. This topology is based on the series loaded resonant converter and synchronous rectifiers to allow the bi-directional power flow between the PV source and grid. The main advantages of this topology are as follows: (1) Low cost; (2) Low component count; (3) Bi-directional power flow capability to support reactive power and (4) High semiconductor integration. The proposed topology has the potential for high efficiency energy conversion with component optimization. A 300 Watt laboratory prototype of the topology has been designed, built and tested. Experimental results show that the proposed topology can realize very simple design and is particularly suitable for high voltage (with MPP voltage range of 50 to 60V) low current PV panels.
Sense-based Interpretation of Logical Metonymy Using a Statistical Method

The use of figurative language is ubiquitous in natural language texts and it is a serious bottleneck in automatic text understanding. We address the problem of interpretation of logical metonymy, using a statistical method. Our approach originates from that of Lapata and Lascarides (2003), which generates a list of non-disambiguated interpretations with their likelihood derived from a corpus. We propose a novel sense-based representation of the interpretation of logical metonymy and a more thorough evaluation method than that of Lapata and Lascarides (2003). By carrying out a human experiment we prove that such a representation is intuitive to human subjects. We derive a ranking scheme for verb senses using an unannotated corpus, WordNet sense numbering and glosses. We also provide an account of the requirements that different aspectual verbs impose onto the interpretation of logical metonymy. We tested our system on verb-object metonymic phrases. It identifies and ranks metonymic interpretations with the mean average precision of 0.83 as compared to the gold standard.
Application of classical clustering methods for online tool condition monitoring in high speed milling processes

Tool Condition Monitoring (TCM) is a necessary action during end-milling process as worn milling-tool might irreversibly damage the work-piece. So, there is an urgent need for a TCM system to provide an evaluation of the tool-wear progress and resulted surface roughness. Principally, in-process tool-wear and surface roughness measurements requires costly stopping of the milling machine. However, to implement the condition monitoring system, resulted signals of milling process are utilized to form a reference model that detects the performance of the system non-intrusively. Therefore, the needed milling-process reference model have to apply more beneficial feature extraction and AI techniques. Since the signals are continuous, their time-frequency analysis are applied for feature extraction. Also, proper AI-based modeling techniques have to be joined together to form a repeatable and generalizable reference model. As one of the available AI techniques that can make an insightful change in traditional AI based modeling techniques for the process, clustering methods are applied on the wavelet features of milling signal as an interpretation layer between the sensor signals and the next artificial intelligent blocks. This paper illustrates the consistency and repeatability of different clustering methods on wavelet features of force and vibration signal as well as a comparison in accordance to their performance and possible generalization for online condition monitoring and sequential clustering. Finally, fuzzy C-means clustering method is shown to be a useful AI-based block for a noise-robust and generalizable ball-nose milling reference model while it provides suitable platform for further investigations regarding online fault diagnosis and prognosis and sequential clustering.
A structure from motion inequality

We state an elementary inequality for the structure from motion problem for m cameras and n points. This structure from motion inequality relates space dimension, camera parameter dimension, the number of cameras and number points and global symmetry properties and provides a rigorous criterion for which reconstruction is not possible with probability 1. Mathematically the inequality is based on Frobenius theorem which is a geometric incarnation of the fundamental theorem of linear algebra. The paper also provides a general mathematical formalism for the structure from motion problem. It includes the situation the points can move while the camera takes the pictures.
Entropy-Based Denial of Service Attack Detection in Cloud Data Center

Cloud data centers today usually lack network resource isolation. Meanwhile, it is easy to deploy and terminate large number of malicious virtual machines (VMs) in a few seconds while the administrator is probably difficult to identify these malicious VMs immediately. These features open doors for attackers to launch denial-of-service (DoS) attacks that target at degrading the quality of cloud service. This paper studies an attack scenario that malicious tenants use cloud resources to launch DoS attack targeting at data center subnets. Unlike traditional data flow based detections, which heavily depend on the pattern of data flows, we propose an approach that takes advantage of virtual machine status including CPU usage and network usage to identify the attack. We notice that malicious virtual machines exhibit similar status patterns when attack is launched. Based on this observation, information entropy is applied in monitoring the status of virtual machines to identify the attack behaviors. We conduct our experiments in the campus-wide data center, and the results show our detection system can promptly and accurately response to DoS attacks.
A High-Linearity Digital-to-Time Converter Technique: Constant-Slope Charging

A digital-to-time converter (DTC) controls time delay by a digital code, which is useful, for example, in a sampling oscilloscope, fractional-N PLL, or time-interleaved ADC. This paper proposes constant-slope charging as a method to realize a DTC with intrinsically better integral non-linearity (INL) compared to the popular variable-slope method. The proposed DTC chip realized in 65 nm CMOS consists of a voltage-controlled variable-delay element (DTC-core) driven by a 10 bit digital-to-analog converter. Measurements with a 55 MHz crystal clock demonstrate a full-scale delay programmable from 19 ps to 189 ps with a resolution from 19 fs to 185 fs. As available oscilloscopes are not good enough to reliably measure such high timing resolution, a frequency-domain method has been developed that modulates a DTC edge and derives INL from spur strength. An INL of 0.17% at 189 ps full-scale delay and 0.34% at 19 ps are measured, representing 8-9 bit effective INL-limited resolution. Output rms jitter is better than 210 fs limited by the test setup, while the DTC consumes 1.8 mW.
Efficacy of two rotary retreatment systems in removing Gutta-percha and sealer during endodontic retreatment with or without solvent: A comparative in vitro study

Objectives: The aim of this in vitro study was to compare the efficacy of two retreatment rotary systems in the removal of Gutta-percha (GP) and sealer from the root canal walls with or without solvent. Materials and Methods: Forty-eight extracted human mandibular first premolars were prepared and obturated with GP and AH Plus sealer. Samples were then randomly divided into four groups. Group I was retreated with MtwoR rotary system without solvent, Group II was retreated with MtwoR rotary system with Endosolv R as the solvent, Group III with D-RaCe rotary system without solvent, and Group IV with D-RaCe rotary system and Endosolv R solvent. The cleanliness of canal walls was determined by stereomicroscope (×20) and AutoCAD software. Kruskal–Wallis test and Mann–Whitney U-test were used to compare the data. Results: Results showed that none of the retreatment systems used in this study was able to completely remove the root canal filling material. D-RaCe with or without solvent showed significantly (P > 0.05) less filling material at all levels compared to MtwoR with/without solvent. Conclusion: Within the limitation of the current study, D-RaCe rotary retreatment system is more effective in removing filling material from root canal walls when compared to MtwoR rotary retreatment system.
Pose-graph visual SLAM with geometric model selection for autonomous underwater ship hull inspection

This paper reports the application of vision based simultaneous localization and mapping (SLAM) to the problem of autonomous ship hull inspection by an underwater vehicle. The goal of this work is to automatically map and navigate the underwater surface area of a ship hull for foreign object detection and maintenance inspection tasks. For this purpose we employ a pose-graph SLAM algorithm using an extended information filter for inference. For perception, we use a calibrated monocular camera system mounted on a tilt actuator so that the camera approximately maintains a nadir view to the hull. A combination of SIFT and Harris features detectors are used within a pairwise image registration framework to provide camera-derived relative-pose constraints (modulo scale). Because the ship hull surface can vary from being locally planar to highly three-dimensional (e.g., screws, rudder), we employ a geometric model selection framework to appropriately choose either an essential matrix or homography registration model during image registration. This allows the image registration engine to exploit geometry information at the early stages of estimation, which results in better navigation and structure reconstruction via more accurate and robust camera-constraints. Preliminary results are reported for mapping a 1,300 image data set covering a 30 m by 5 m section of the hull of a USS aircraft carrier. The post-processed result validates the algorithm's potential to provide in-situ navigation in the underwater environment for trajectory control, while generating a texture-mapped 3D model of the ship hull as a byproduct for inspection.
Chromosomal integration of a synthetic expression control sequence achieves poly‐γ‐glutamate production in a Bacillus subtilis strain

Poly‐γ‐glutamate (γ‐PGA) has applications in food, medical, cosmetic, animal feed, and wastewater industries. Bacillus subtilis DB430, which possesses the γ‐PGA synthesis ywsC‐ywtAB genes in its chromosome, cannot produce γ‐PGA. An efficient synthetic expression control sequence (SECS) was introduced into the upstream region of the ywtABC genes, and this resulted in γ‐PGA‐producing B. subtilis mutant strains. Mutant B. subtilis PGA6‐2 stably produces high levels of γ‐PGA in medium A without supplementation of extra glutamic acid or ammonium chloride. The mutant B. subtilis PGA 6‐2 is not only a γ‐PGA producer, but it is also a candidate for the genetic and metabolic engineering of γ‐PGA production. © 2010 American Institute of Chemical Engineers Biotechnol. Prog. , 2010
On Distributed Graph Coloring with Iterative Recoloring

Identifying the sets of operations that can be executed simultaneously is an important problem appearing in many parallel applications. By modeling the operations and their interactions as a graph, one can identify the independent operations by solving a graph coloring problem. Many ecient sequential algorithms are known for this NP-Complete problem, but they are typically unsuitable when the operations and their interactions are distributed in the memory of large parallel computers. On top of an existing distributed-memory graph coloring algorithm, we investigate two compatible techniques in this paper for fast and scalable distributed-memory graph coloring. First, we introduce an improvement for the distributed post-processing operation, called recoloring, which drastically improves the number of colors. We propose a novel and ecient communication scheme for recoloring which enables it to scale gracefully. Recoloring must be seeded with an existing coloring of the graph. Our second contribution is to introduce a randomized color selection strategy for initial coloring which quickly produces solutions of modest quality. We extensively evaluate the impact of our new techniques on existing distributed algorithms and show the time-quality tradeos. We show that combining an initial randomized coloring with multiple recoloring iterations yields better quality solutions with the smaller runtime at large scale.
RNAi-mediated oncogene silencing confers resistance to crown gall tumorigenesis

Crown gall disease, caused by the soil bacterium Agrobacterium tumefaciens, results in significant economic losses in perennial crops worldwide. A. tumefaciens is one of the few organisms with a well characterized horizontal gene transfer system, possessing a suite of oncogenes that, when integrated into the plant genome, orchestrate de novo auxin and cytokinin biosynthesis to generate tumors. Specifically, the iaaM and ipt oncogenes, which show ≈90% DNA sequence identity across studied A. tumefaciens strains, are required for tumor formation. By expressing two self-complementary RNA constructions designed to initiate RNA interference (RNAi) of iaaM and ipt, we generated transgenic Arabidopsis thaliana and Lycopersicon esculentum plants that are highly resistant to crown gall disease development. In in vitro root inoculation bioassays with two biovar I strains of A. tumefaciens, transgenic Arabidopsis lines averaged 0.0–1.5% tumorigenesis, whereas wild-type controls averaged 97.5% tumorigenesis. Similarly, several transformed tomato lines that were challenged by stem inoculation with three biovar I strains, one biovar II strain, and one biovar III strain of A. tumefaciens displayed between 0.0% and 24.2% tumorigenesis, whereas controls averaged 100% tumorigenesis. This mechanism of resistance, which is based on mRNA sequence homology rather than the highly specific receptor–ligand binding interactions characteristic of traditional plant resistance genes, should be highly durable. If successful and durable under field conditions, RNAi-mediated oncogene silencing may find broad applicability in the improvement of tree crop and ornamental rootstocks.
Integrated device for combined optical neuromodulation and electrical recording for chronic in vivo applications

Studying brain function and its local circuit dynamics requires neural interfaces that can record and stimulate the brain with high spatiotemporal resolution. Optogenetics, a technique that genetically targets specific neurons to express light-sensitive channel proteins, provides the capability to control central nervous system neuronal activity in mammals with millisecond time precision. This technique enables precise optical stimulation of neurons and simultaneous monitoring of neural response by electrophysiological means, both in the vicinity of and distant to the stimulation site. We previously demonstrated, in vitro, the dual capability (optical delivery and electrical recording) while testing a novel hybrid device (optrode-MEA), which incorporates a tapered coaxial optical electrode (optrode) and a 100 element microelectrode array (MEA). Here we report a fully chronic implant of a new version of this device in ChR2-expressing rats, and demonstrate its use in freely moving animals over periods up to 8 months. In its present configuration, we show the device delivering optical excitation to a single cortical site while mapping the neural response from the surrounding 30 channels of the 6 × 6 element MEA, thereby enabling recording of optically modulated single-unit and local field potential activity across several millimeters of the neocortical landscape.
Multi-objective techniques in genetic programming for evolving classifiers

The application of multi-objective evolutionary computation techniques to the genetic programming of classifiers has the potential to both improve the accuracy and decrease the training time of the classifiers. The performance of two such algorithms is investigated on the even 6-parity problem and the Wisconsin breast cancer, Iris and Wine data sets from the UCI repository. The first method explores the addition of an explicit size objective as a parsimony enforcement technique. The second represents a program's classification accuracy on each class as a separate objective. Both techniques give a lower error rate with less computational cost than was achieved using a standard GP with the same parameters.