ppxscal/embedding_network huggingface dataset:
Strongly improved stability and faster convergence of temporal sequence learning by using input correlations only
Authors: Bernd Porr, Florentin Wörgötter
Venue: Neural Computation (2006)

Currently all important, low-level, unsupervised network learning algorithms follow the paradigm of Hebb, where input and output activity are correlated to change the connection strength of a synapse. However, as a consequence, classical Hebbian learning always carries a potentially destabilizing autocorrelation term, which is due to the fact that every input is in a weighted form reflected in the neuron's output. This self-correlation can lead to positive feedback, where increasing weights will increase the output, and vice versa, which may result in divergence. This can be avoided by different strategies like weight normalization or weight saturation, which, however, can cause different problems. Consequently, in most cases, high learning rates cannot be used for Hebbian learning, leading to relatively slow convergence. Here we introduce a novel correlation-based learning rule that is related to our isotropic sequence order (ISO) learning rule (Porr & Wörgötter, 2003a), but replaces the derivative of the output in the learning rule with the derivative of the reflex input. Hence, the new rule uses input correlations only, effectively implementing strict heterosynaptic learning. This looks like a minor modification but leads to dramatically improved properties. Elimination of the output from the learning rule removes the unwanted, destabilizing autocorrelation term, allowing us to use high learning rates. As a consequence, we can mathematically show that the theoretical optimum of one-shot learning can be reached under ideal conditions with the new rule. This result is then tested against four different experimental setups, and we will show that in all of them, very few (and sometimes only one) learning experiences are needed to achieve the learning goal. As a consequence, the new learning rule is up to 100 times faster and in general more stable than ISO learning.
Design and Prototype Implementation of A Novel Automatic Vehicle Parking System
Authors: Li-Der Chou, Chun-Cheng Sheu, Home-Way Chen
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02 (2006)

Unmanned vehicle driving systems are getting interesting for the auto mobile industry. One of the key issues in the unmanned vehicle driving system is how to acquire the current position of the vehicle. In general, it can be achieved by Global Positioning System (GPS). However, if the unmanned vehicle will be parked in the parking garage by itself, GPS can not provide sufficient information to avoid collisions and the accuracy of GPS is not enough for the narrow parking space. An automatic vehicle parking system is proposed in the paper, where wireless sensor networks and infrared technologies are adopted simultaneously to enhance the accuracy of positioning. The prototype of the proposed automatic vehicle parking system is also implemented.
An Electrophysiological Correlate of Visual Motion Awareness in Man
Authors: Thomas Haarmeier, Peter Thier
Venue: Journal of Cognitive Neuroscience (1998)

It is usually held that perceptual spatial stability, despite smooth pursuit eye movements, is accomplished by comparing a signal reflecting retinal image slip with an internal reference signal, encoding the eye movement. The important consequence of this concept is that our subjective percept of visual motion reflects the outcome of this comparison rather than retinal image slip. In an attempt to localize the cortical networks underlying this comparison and therefore our subjective percept of visual motion, we exploited an imperfection inherent in it, which results in a movement illusion. If smooth pursuit is carried out across a stationary background, we perceive a tiny degree of illusionary background motion (Filehne illusion, or FI), rather than experiencing the ecologically optimal percept of stationarity. We have recently shown that this illusion can be modified substantially and predictably under laboratory conditions by visual motion unrelated to the eye movement. By making use of this finding, we were able to compare cortical potentials evoked by pursuit-induced retinal image slip under two conditions, which differed perceptually, while being identical physically. This approach allowed us to discern a pair of potentials, a parieto-occipital negativity (N300) followed by a frontal positivity (P300), whose amplitudes were solely determined by the subjective perception of visual motion irrespective of the physical attributes of the situation. This finding strongly suggests that subjective awareness of visual motion depends on neuronal activity in a parietooccipito-frontal network, which excludes the early stages of visual processing.
Reciprocal Image Features for Uncalibrated Helmholtz Stereopsis
Authors: Todd Zickler
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

Helmholtz stereopsis is a surface reconstruction method that exploits reciprocity for the recovery of 3D shape without an assumed BRDF model, and it has been shown to yield high quality results when the cameras and light sources are carefully calibrated. In many practical cases, however, accurate off-line calibration is difficult (or impossible) to achieve. We address this issue by exploring widebaseline matching in Helmholtz stereo images. We identify two classes of local image interest regions ('features') that can be reliably detected and matched between views in a Helmholtz stereo dataset; and by exploiting reciprocity, we show how these regions can be used to recover both geometric and radiometric calibration information. When used in conjunction with existing methods for dense reconstruction, this provides an automated shape recovery pipeline that operates independent of reflectance and does not require the acquisition of additional calibration images off-line.
Everything You Wanted to Know about Privacy (But Were Afraid to Ask)
Authors: Shari Lawrence Pfleeger
Venue: IEEE Security and Privacy (2006)

A review of the book, "Privacy: What Developers and IT Professionals Should Know," by J.C. Cannon.
Distributed Mining of Constrained Patterns from Wireless Sensor Data
Authors: Carson Kai-Sang Leung, Quamrul I. Khan, Boyu Hao
Venue: Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology (2006)

With the advance in technology, wireless sensor networks have been widely used in many application areas such as agricultural and environmental monitoring. Sensors distributed in these networks serve as good sources for data. This calls for distributed data mining, which searches for implicit, previously unknown, and potentially useful patterns that might be embedded in the distributed data. Many existing distributed data mining algorithms do not allow users to express the patterns to be mined according to their intension via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous patterns that are not interesting to users. In this paper, we propose an efficient tree-based system for mining patterns that satisfy user-defined constraints from a distributed environment such as a wireless sensor network. Experimental results show effectiveness of our proposed system.
Reverse Nearest Neighbor Search in Metric Spaces
Authors: Yufei Tao, Man Lung Yiu, Nikos Mamoulis
Venue: IEEE Transactions on Knowledge and Data Engineering (2006)

Given a set {\cal D} of objects, a reverse nearest neighbor (RNN) query returns the objects o in {\cal D} such that o is closer to a query object q than to any other object in {\cal D}, according to a certain similarity metric. The existing RNN solutions are not sufficient because they either 1) rely on precomputed information that is expensive to maintain in the presence of updates or 2) are applicable only when the data consists of "Euclidean objects” and similarity is measured using the L_2 norm. In this paper, we present the first algorithms for efficient RNN search in generic metric spaces. Our techniques require no detailed representations of objects, and can be applied as long as their mutual distances can be computed and the distance metric satisfies the triangle inequality. We confirm the effectiveness of the proposed methods with extensive experiments.
DASS: efficient discovery and p-value calculation of substructures in unordered data
Authors: Jens Hollunder, Maik Friedel, Andreas Beyer, Christopher T. Workman, Thomas Wilhelm
Venue: Bioinformatics (2007)

Motivation: Pattern identification in biological sequence data is one of the main objectives of bioinformatics research. However, few methods are available for detecting patterns (substructures) in unordered datasets. Data mining algorithms mainly developed outside the realm of bioinformatics have been adapted for that purpose, but typically do not determine the statistical significance of the identified patterns. Moreover, these algorithms do not exploit the often modular structure of biological data. Results: We present the algorithm DASS (Discovery of All Significant Substructures) that first identifies all substructures in unordered data (DASSSub) in a manner that is especially efficient for modular data. In addition, DASS calculates the statistical significance of the identified substructures, for sets with at most one element of each type (DASSPset), or for sets with multiple occurrence of elements (DASSPmset). The power and versatility of DASS is demonstrated by four examples: combinations of protein domains in multi-domain proteins, combinations of proteins in protein complexes (protein subcomplexes), combinations of transcription factor target sites in promoter regions and evolutionarily conserved protein interaction subnetworks. Availability: The program code and additional data are available at http://www.fli-leibniz.de/tsb/DASS Contact: wilhelm@fli-leibniz.de Supplementary information: Supplementary information is available at Bioinformatics online.
High-order compact finite-difference methods on general overset grids
Authors: Scott E. Sherer, James N. Scott
Venue: Journal of Computational Physics (2005)

This work investigates the coupling of a very high-order finite-difference algorithm for the solution of conservation laws on general curvilinear meshes with overset-grid techniques originally developed to address complex geometric configurations. The solver portion of the algorithm is based on Pade-type compact finite-differences of up to sixth-order, with up to 10th-order filters employed to remove spurious waves generated by grid non-uniformities, boundary conditions and flow non-linearities. The overset-grid approach is utilized as both a domain-decomposition paradigm for implementation of the algorithm on massively parallel machines and as a means for handling geometric complexity in the computational domain. Two key features have been implemented in the current work; the ability of the high-order algorithm to accommodate holes cut in grids by the overset-grid approach, and the use of high-order interpolation at non-coincident grid overlaps. Several high-order/high-accuracy interpolation methods were considered, and a high-order, explicit, non-optimized Lagrangian method was found to be the most accurate and robust for this application. Several two-dimensional benchmark problems were examined to validate the interpolation methods and the overall algorithm. These included grid-to-grid interpolation of analytic test functions, the inviscid convection of a vortex, laminar flow over single- and double-cylinder configurations, and the scattering of acoustic waves from one- and three-cylinder configurations. The employment of the overset-grid techniques, coupled with high-order interpolation at overset boundaries, was found to be an effective way of employing the high-order algorithm for more complex geometries than was previously possible.
PathExpander: Architectural Support for Increasing the Path Coverage of Dynamic Bug Detection
Authors: Shan Lu, Pin Zhou, Wei Liu, Yuanyuan Zhou, Josep Torrellas
Venue: Proceedings of the 39th Annual IEEE/ACM International Symposium on Microarchitecture (2006)

Dynamic software bug detection tools are commonly used because they leverage run-time information. However, they suffer from a fundamental limitation, the Path Coverage Problem: they detect bugs only in taken paths but not in non-taken paths. In other words, they require bugs to be exposed in the monitored execution. This paper makes one of the first attempts to address this fundamental problem with a simple hardware extension. First, we propose PathExpander , a novel design that dynamically increases the code path coverage of dynamic bug detection tools with no programmer involvement. As a program executes, PathExpander selectively executes non-taken paths in a sandbox without side effects. This enables dynamic bug detection tools to find bugs that are present in these non-taken paths and would otherwise not be detected. Second, we propose a simple hardware extension to control the huge overhead in its pure software implementation to a moderate level. To further minimize overhead, PathExpander provides an optimization option to execute non-taken paths on idle cores in chip multi-processor architectures that support speculative execution. To evaluate PathExpander, we use three dynamic bug detection methods: dynamic software-only checker (CCured), dynamic hardware-assisted checker (iWatcher) and assertions; and conduct side-by-side comparison with PathExpander's counterpart software implementation. Our experiments with seven buggy programs using general inputs that do not expose the tested bugs show that PathExpander is able to help these tools detect 21 (out of 38) tested bugs that are otherwise missed. This is because PathExpander increases the code coverage of each test case from 40% to 65% on average, based on the branch coverage metric. When applications are tested with multiple inputs, the cumulative coverage also significantly improves by 19%. We also show that PathExpander introduces modest false positives (4 on average) and overhead (less than 9.9%). The 3.4 orders of magnitude lower overhead compared with pure-software implementation further justifies the hardware design in PathExpander.
Fast short-time orthogonal wavelet packet transform algorithms
Authors: B. Carnero, A. Drygajlo
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

Orthogonal wavelet packet transforms, presented recently as powerful tools for nonstationary signal processing, suffered from the lack of efficient and time-domain transparent algorithms for their implementation. We present an overlapped block lattice structure that allows fast short-time wavelet packet transform algorithms to be built in an equivalent manner to other short-time transforms, such as the short-time Fourier transform (STFT) based on the fast Fourier transform (FFT) algorithms. The outputs obtained with the overlapped block lattice are equivalent to those achieved by the well known tree-structured filter bank approach, though the implementation algorithms are different.
Broadcast capacity in multihop wireless networks
Authors: Alireza Keshavarz-Haddad, Vinay Ribeiro, Rudolf Riedi
Venue: Proceedings of the 12th annual international conference on Mobile computing and networking (2006)

In this paper we study the broadcast capacity of multihop wireless networks which we define as the maximum rate at which broadcast packets can be generated in the network such that all nodes receive the packets successfully in a limited time. We employ the Protocol Model for successful packet reception usually adopted in network capacity studies and provide novel upper and lower bounds for the broadcast capacity for arbitrary connected networks. In a homogeneous dense network these bounds simplify to Θ(W/max(1,Δd)) where W is the wireless channel capacity, Δ the interference parameter, and d the number of dimensions of space in which the network lies. Interestingly, we show that the broadcast capacity does not change by more than a constant factor when we vary the number of nodes, the radio range, the area of the network, and even the node mobility. To address the achievability of capacity, we demonstrate that any broadcast scheme based on a backbone of size proportional to the Minimum Connected Dominating Set guarantees a throughput within a constant factor of the broadcast capacity. Finally, we demonstrate that broadcast capacity, in stark contrast to unicast capacity, does not depend on the choice of source nodes or the dimension of the network.
GPSenseCar -A Collision Avoidance Support System Using Real-Time GPS Data in a Mobile Vehicular Network
Authors: Yin-Jun Chen, Ching-Chung Chen, Shou-Nian Wang, Han-En Lin, Roy C. Hsu
Venue: Proceedings of the International Conference on Systems and Networks Communication (2006)

Currently, several smart phones have GPS receivre built- in, such as Motorola E1000, HP iPAQ 6515 and Mio A700. Equipped with GPS receiver, the mobile phone has enabled its owner to become a personal position locator. The proposed project GPSenseCar is to use the WiFi smart phone with the built-in GPS receiver to develop a collision avoidance support system for vehicle users. Optical based vehicle collision avoidance system, such as RADAR or CCD camera, has the drawback of vision corner. A vehicle collision avoidance system based on WiFi and GPS can compensate the drawbacks of optical based system to prevent the collision when vehicle is driving in high speed or under near-zero visibility. The GPSenseCar locates the moving objects by calculating on the GPS data, which was emitted from the other users' cell phone and collected by the WiFi network of the driver's vehicle, such that different level of warning sound can be provided to the driver according to the relative distance between vehicle owners and the other moving vehicles or passengers to create an Augment Reality by sensing the upcoming danger and avoiding collision.
The Source and Seat of Creativity in Human Beings: A Position Paper
Authors: Sumit Ghosh
Venue: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (2006)

All cultures and nations have long recognized that creativity is the ultimate cause of the quantum leaps in virtually every discipline of knowledge and the quiet and invisible force behind all progress of human civilization. Leading thinkers across all disciplines have advocated the importance of creativity and consistently ranked it well beyond intelligence. In the recent literature, creativity has been clarified as a phenomenon that categorically differs from intelligence in that the ideas that result from it may not be deduced by applying current reasoning techniques on the state-of-the-art knowledge. Thus, creativity transcends intelligence. Throughout history, close examination reveals that deep sadness and profound grief have always been associated with every creative activity and every individual engaged in a creative invention or discovery. The lives of sage Valmiki, Socrates, Plato, Archimedes, Genghiz Khan, Leonardo Da Vinci, Galileo, John Harrison, Alfred Nobel, and Jagadish Bose, to name a few, offer undeniable testimony. This paper pioneers the analytical examination of a key question, namely, is the apparent association between creativity and sadness mere coincidence or does there exist a scientific reasoning, arising from a deep and fundamental connection between the two concepts? The potential benefits of this exercise, if successful, are two-fold. First, it encapsulates a systematic attempt to resolve a deep intellectual curiosity that has intrigued society for millennia. Second, as a practical benefit, a careful understanding of the theory of the underlying processes may help us analyze how best to nurture the course of any given creative activity and limit the grief or sadness so it does not spin out of control and consume the individual, denying civilization, today and for the future, the enormous benefits that may follow from the inventions and discoveries. It is an undeniable fact that sadness may affect every individual, at one time or another and this paper does not focus on assessing whether sadness is a prelude to a creative invention or discovery. Instead, its objective is to examine whether creativity, by nature, triggers a counter-posing force that leads to profound grief; or whether creativity and sadness are so intimately intertwined in their seat in human beings that evoking creativity automatically triggers intense grief.
Using the h-index to rank influential British researchers in information science and librarianship: Brief Communication
Authors: Charles Oppenheim
Venue: Journal of the American Society for Information Science and Technology (2007)

The recently developed h-index has been applied to the literature produced by senior British-based academics in librarianship and information science. The majority of those evaluated currently hold senior positions in UK information science and librarianship departments; however, a small number of staff in other departments and retired &ldquo;founding fathers&rdquo; were analyzed as well. The analysis was carried out using the Web of Science (Thomson Scientific, Philadelphia, PA) for the years from 1992 to October 2005, and included both second-authored papers and self-citations. The top-ranking British information scientist, Peter Willett, has an h-index of 31. However, it was found that Eugene Garfield, the founder of modern citation studies, has an even higher h-index of 36. These results support other studies suggesting that the h-index is a useful tool in the armory of bibliometrics. &copy; 2007 Wiley Periodicals, Inc.
A Neural Network Adaptive Controller for End-effector Tracking of Redundant Robot Manipulators
Authors: B. Daachi, A. Benallegue
Venue: Journal of Intelligent and Robotic Systems (2006)

In this paper we propose a neural network adaptive controller to achieve end-effector tracking of redundant robot manipulators. The controller is designed in Cartesian space to overcome the problem of motion planning which is closely related to the inverse kinematics problem. The unknown model of the system is approximated by a decomposed structure neural network. Each neural network approximates a separate element of the dynamical model. These approximations are used to derive an adaptive stable control law. The parameter adaptation algorithm is derived from the stability study of the closed loop system using Lyapunov approach with intrinsic properties of robot manipulators. Two control strategies are considered. First, the aim of the controller is to achieve good tracking of the end-effector regardless the robot configurations. Second, the controller is improved using augmented space strategy to ensure minimum displacements of the joint positions of the robot. Simulation examples are also presented to verify the effectiveness of the proposed approach.
Programmable Ad Hoc Networks
Authors: Daiki Okada, Daisuke Kasamatsu, Norihiko Shinomiya, Tadashi Ohta
Venue: Proceedings of the International Conference on Software Engineering Advances (2006)

Ad hoc networks can be instantly constructed without using an existing network infrastructure. But services that users can receive are restricted to those provided beforehand by service providers. Therefore, it is impossible to satisfy users' diverse demands. This paper proposes programmable ad hoc networks where users can, themselves, create new services. An emulator for ad hoc network experiments which can emulate multiple mobile nodes and radio zones on a single standard PC is also proposed. It was confirmed that the proposed programmable ad hoc networks and the emulator are effective.