Miracle-dz/UniFAR-FTUs huggingface dataset:
A simple solution to the k‐core problem

We study the k‐core of a random (multi)graph on n vertices with a given degree sequence. We let n →∞. Then, under some regularity conditions on the degree sequences, we give conditions on the asymptotic shape of the degree sequence that imply that with high probability the k‐core is empty and other conditions that imply that with high probability the k‐core is non‐empty and the sizes of its vertex and edge sets satisfy a law of large numbers; under suitable assumptions these are the only two possibilities. In particular, we recover the result by Pittel, Spencer, and Wormald (J Combinator Theory 67 (1996), 111–151) on the existence and size of a k‐core in G(n,p) and G(n,m), see also Molloy (Random Struct Algor 27 (2005), 124–135) and Cooper (Random Struct Algor 25 (2004), 353–375). Our method is based on the properties of empirical distributions of independent random variables and leads to simple proofs. © 2006 Wiley Periodicals, Inc. Random Struct. Alg. ,, 2007
Intra-Auditory Integration Improves Motor Performance and Synergy in an Accurate Multi-Finger Pressing Task

Humans detect changes in the air pressure and understand the surroundings through the auditory system. The sound humans perceive is composed of two distinct physical properties, frequency and intensity. However, our knowledge is limited how the brain perceives and combines these two properties simultaneously (i.e., intra-auditory integration), especially in relation to motor behaviors. Here, we investigated the effect of intra-auditory integration between the frequency and intensity components of auditory feedback on motor outputs in a constant finger-force production task. The hierarchical variability decomposition model previously developed was used to decompose motor performance into mathematically independent components each of which quantifies a distinct motor behavior such as consistency, repeatability, systematic error, within-trial synergy, or between-trial synergy. We hypothesized that feedback on two components of sound as a function of motor performance (frequency and intensity) would improve motor performance and multi-finger synergy compared to feedback on just one component (frequency or intensity). Subjects were instructed to match the reference force of 18 N with the sum of all finger forces (virtual finger or VF force) while listening to auditory feedback of their accuracy. Three experimental conditions were used: (i) condition F, where frequency changed; (ii) condition I, where intensity changed; (iii) condition FI, where both frequency and intensity changed. Motor performance was enhanced for the FI conditions as compared to either the F or I condition alone. The enhancement of motor performance was achieved mainly by the improved consistency and repeatability. However, the systematic error remained unchanged across conditions. Within- and between-trial synergies were also improved for the FI condition as compared to either the F or I condition alone. However, variability of individual finger forces for the FI condition was not significantly decreased as compared to I condition alone. This result indicates an improvement in motor performance is consistent with Bayesian estimation, and changes in multi-finger interaction mostly result in the enhanced motor performance. These findings provide evidence that the central nervous system can take advantage of the intra-auditory integration in a statistically optimal (Bayesian) fashion to enhance motor performance by improving multi-finger synergy.
The H I and Ionized Gas Disk of the Seyfert Galaxy NGC 1144 = Arp 118: A Violently Interacting Galaxy with Peculiar Kinematics

We present observations of the distribution and kinematics of neutral and ionized gas in NGC 1144, a galaxy that forms part of the Arp 118 system. Ionized gas is present over a huge spread in velocity (1100 km s-1) in the disk of NGC 1144, but H I emission is detected over only one-third of this velocity range, in an area that corresponds to the NW half of the disk. In the nuclear region of NGC 1144, a jump in velocity in the ionized gas component of 600 km s-1 is observed. Faint, narrow H I absorption lines are also detected against radio sources in the SE part of the disk of NGC 1144, which includes regions of massive star formation and a Seyfert nucleus. The peculiar H I distribution, which is concentrated in the NW disk, seems to be the inverse of the molecular distribution that is concentrated in the SE disk. Although this may partly be the result of the destruction of H I clouds in the SE disk, there is circumstantial evidence that the entire H I emission spectrum of NGC 1144 is affected by a deep nuclear absorption line covering a range of 600 km s-1 and is likely blueshifted with respect to the nucleus. In this picture, a high column density H I stream is associated with the nuclear ionized gas velocity discontinuity, and the absorption effectively masks any H I emission that would be present in the SE disk of NGC 1144.
Singing Dialogue : Music therapy with persons in advanced stages of dementia.: A case study research design

Persons suffering from primary degenerative dementia at later stages of the disease experience problems in perceiving environmental information and in expressing themselves in verbal language. This leads to difficulties in entering and maintaining dialogue. Failing possibilities of entering dialogue, psychosocial needs are not easily fulfilled, which leads to serious secondary symptoms of dementia. In this research the use of familiar songs in music therapy is suggested as a way of entering dialogue, where the communication is adjusted to the individual person. A flexible mixed-method research design is carried out based on video observations, heart rate data, and observations from staff, external assessors, and the music therapist. One part of the research consists of 6 case studies where physiological data are used to validate observational data. Next part is a hermeneutic analysis of observations done by external assessors, leading to a catalogue of gestural responses and a coding and categorization of the qualities of these responses. In a third part examples from the case studies are analysed, using the categories that evolved in the previous part and describing various levels of communication. The results show that; 1) Singing has a positive influence on the 6 participants, defined by degree of compliance, by changes in heart rate levels, and by various ways of taking part in the music therapy; 2) The six participants communicate responsively, and this communication can be recognised by a system of communicative signs, representing different levels of communication: emotional valence, receptive participation, sociality, active participation, communicative musicality, and dialogue. There exists a relationship between a balanced arousal level and communication at more intensive levels for all six participants; 3) In 5 of 6 concrete cases music therapy shows an influence on aspects in residential daily life, defined in a statistical significant decrease in heart rate levels pre/post therapy, for persons with severe dementia showing agitated behaviour. The participants clearly profit from the music therapy sessions, and most important: these persons suffering from severe dementia are communicating and are able to be brought into a state where a communicative dialogue takes place. The songs offer a structure, which functions in focussing attention by mediating stability, as well as social and contextual cues. Additionally the songs are used in regulating the arousal level of the participant towards environmental attention and a state most optimal for entering dialogue.
Correlating XML data streams using tree-edit distance embeddings

We propose the first known solution to the problem of correlating, in small space, continuous streams of XML data through approximate (structure and content) matching, as defined by a general tree-edit distance metric. The key element of our solution is a novel algorithm for obliviously embedding tree-edit distance metrics into an L1 vector space while guaranteeing an upper bound of O(log2 n log* n) on the distance distortion between any data trees with at most n nodes. We demonstrate how our embedding algorithm can be applied in conjunction with known random sketching techniques to: (1) build a compact synopsis of a massive, streaming XML data tree that can be used as a concise surrogate for the full tree in approximate tree-edit distance computations; and, (2) approximate the result of tree-edit distance similarity joins over continuous XML document streams. To the best of our knowledge, these are the first algorithmic results on low-distortion embeddings for tree-edit distance metrics, and on correlating (e.g., through similarity joins) XML data in the streaming model.
3D LIDAR–camera intrinsic and extrinsic calibration: Identifiability and analytical least-squares-based initialization

In this paper we address the problem of estimating the intrinsic parameters of a 3D LIDAR while at the same time computing its extrinsic calibration with respect to a rigidly connected camera. Existing approaches to solve this nonlinear estimation problem are based on iterative minimization of nonlinear cost functions. In such cases, the accuracy of the resulting solution hinges on the availability of a precise initial estimate, which is often not available. In order to address this issue, we divide the problem into two least-squares sub-problems, and analytically solve each one to determine a precise initial estimate for the unknown parameters. We further increase the accuracy of these initial estimates by iteratively minimizing a batch nonlinear least-squares cost function. In addition, we provide the minimal identifiability conditions, under which it is possible to accurately estimate the unknown parameters. Experimental results consisting of photorealistic 3D reconstruction of indoor and outdoor scenes, as well as standard metrics of the calibration errors, are used to assess the validity of our approach.
LOADng Routing Protocol Evaluation for Bidirectional Data flow in AMI Mesh Networks

This research work denotes a novel evaluation of LOADng the routing protocol for Wireless Sensor Network. The LOADng protocol implementation is a part of ITU-T G.9903 recommendation based on the framework of the LLN On-demand Ad hoc Distance-vector Routing Protocol - Next Generation (LOADng) proposed by IETF specified by the IETF Internet-Draft draft-clausen-lln-loadng-11 and currently still in its design phase. LOADng is a reactive on demand distance vector routing protocol derived from AODV the Ad hoc On-demand distance vector protocol proposed by IETF. This work was motivated by the need for a novel protocol implementation for smart metering applications providing better performance and less complexity than RPL the Routing Protocol for Low power and lossy networks and adapted to (LLNs) requirements and constraints. Our implementation was successfully integrated into the communication layer of Contiki OS the Wireless Sensor Network operating system and evaluated through extensive simulations for AMI Mesh Networks.
Dimensionality in Pettigrew and Meertens' Blatant Subtle Prejudice Scale

In this study, we analyzed dimensionality for Pettigrew and Meertens’ Blatant and Subtle Prejudice Scale (1995). More specifically, we sought to verify two hypotheses: first, that the scale is one-dimensional, and second, that the difference in the items featured in the two subscales (blatant and subtle) is exclusively due to their different locations on the scale of the latent variable. Expressing the latter hypothesis in the language of the Item Response Theory measurement approach we expected items from the blatant subscale to be more difficult (they require a greater level of prejudice to be positively answered by persons) than those of the subtle subscale. To this end, we used two databases ( 1 = 323; 2 = 268), which we analyzed through confirmatory factor analysis and the partial credit model. Results confirmed our hypotheses, and highlighted the usefulness of the Item Response Theory models in analyzing prejudice scales.
Iterative learning control and repetitive control for engineering practice

This paper discusses linear iterative learning and repetitive control, presenting general purpose control laws with only a few parameters to tune. The method of tuning them is straightforward, making tuning easy for the practicing control engineer. The approach can then serve the same function for learning/repetitive control, as PID controllers do in classical control. Anytime one has a controller that is to perform the same tacking command repeatedly, one simply uses such a law to adjust the command given to an existing feedback controller and achieves a substantial decrease in tracking error. Experiments with the method show that decreases by a factor between 100 and 1000 in the RMS tracking error on a commercial robot, performing a high speed trajectory can easily be obtained in 8 to 12 trials for learning. It is shown that in engineering practice, the same design criteria apply to learning control as apply to repetitive control. Although the conditions for stability are very different for the two problems, one must impose a good transient condition, and once such a condition is formulated, it is likely to be the same for both learning and repetitive control.
A comparative analysis on the performance of static handwritten verification systems on realistic scenarios

An analysis on realistic conditions for static handwritten signature verification systems is presented. Evaluation conditions are studied so to approximate as accurately as possible the conditions of a real-world operational scenario. Also, the problem of modeling realistic attacks by using databases containing forgeries is considered in the analysis. To do so, a database collected with different levels of knowledge and training is used. Performance evaluation results with these restrictions are given over several databases. Results show the effect of the amount of training and the information available to the forger on the attack resistance of some algorithms.
Navigation via continuously adapted music

Listening to music on personal, digital devices while mobile is an enjoyable, everyday activity. We explore a scheme for exploiting this practice to immerse listeners in navigation cues. Our prototype, Ontrack, continuously adapts audio, modifying the spatial balance and volume to lead listeners to their target destination. An initial lab-based evaluation has demonstrated the approach's efficacy: users were able to complete tasks within a reasonable time and their subjective feedback was positive. Encouraged by these findings, we are building a pocket-sized prototype for further testing.
Extended Focus & Context for Visualizing Abstract Data on Maps

Traditionally, the subject of cartography has been geographic data, while information visualization has dealt with abstract, non-spatial information. With the ever increasing amount of data harvested worldwide, more and more of this data includes both geographic and abstract information at the same time. Efficient visualization of this data calls for the combination of maps with techniques for complex, structured information spaces, a key concept among these being focus & context techniques. In this paper, an extended focus & context concept is proposed that efficiently supports the combination of visualization techniques with maps.
From Receptive Field Dynamics to the Rate of Transmitted Information: Some Facets of the Thalamocortical Auditory System

In this article, we first evaluate the literature describing reorganizations of auditory cortex topography after behavioral training. We then review the studies showing that receptive fields of auditory thalamocortical neurons express large dynamics in unanesthetized animals. During the time course of different behavioral training protocols, the frequency tuning curves of thalamocortical neurons can be selectively modified to code for the learned importance of acoustic stimuli. In other circumstances, when the vigilance state shifts from waking to sleep, the functional properties of thalamic and cortical neurons exhibit drastic modifications. Finally, we point out new lines of research. First, investigations describing the responses of neurons to communication signals (e.g. species-specific vocalizations) are important because they reveal how the thalamocortical auditory system processes biologically relevant sounds. Second, we suggest that the spike timing precision can largely increase the amount of information transmitted in the thalamocortical auditory system. This urges for more systematic studies in which the temporal organization of spike trains will be considered at presentation of natural stimuli.
A Study of the Use and Evaluation of Confidence Measures in Automatic Speech Recognition

Con dence measures have been found to be useful for a number tasks within the eld of Automatic Speech Recognition (ASR). For example, the use of con dence measures has been reported in the utterance veri cation, keyword spotting and Out-of-Vocabulary (OOV) word spotting literature. In this report, it is shown that so called 'hybrid Arti cial Neural Network/Hidden Markov Model' (HMM/ANN) systems are well suited to the task of generating con dence measures, due to their ability to provide local phone class posterior probability estimates which may be used to generate con dence measures in a computationally e cient manner. A number of evaluation metrics are also described and the performance of ve con dence measures derived from the ABBOT hybrid HMM/ANN system for the tasks of utterance veri cation and OOV word spotting are evaluated using these metrics. Besides the tasks described above, con dence measures may also be used for tasks such as ltering the acoustics for a number of conditions prior to decoding, guiding the search over the space of alternative decodings and also as diagnostic tools. The use of con dence measures as diagnostic tools can indicate how components of an ASR system can be improved. For example, con dence measures can be used to identify poor pronunciation models and also to guide the search for improved models, where the generation of accurate pronunciation models remains an important challenge in the creation of practical recognition systems.
A Batchwise Monotone Algorithm for Dictionary Learning

We propose a batchwise monotone algorithm for dictionary learning. Unlike the state-of-the-art dictionary learning algorithms which impose sparsity constraints on a sample-by-sample basis, we instead treat the samples as a batch, and impose the sparsity constraint on the whole. The benefit of batchwise optimization is that the non-zeros can be better allocated across the samples, leading to a better approximation of the whole. To accomplish this, we propose procedures to switch non-zeros in both rows and columns in the support of the coefficient matrix to reduce the reconstruction error. We prove in the proposed support switching procedure the objective of the algorithm, i.e., the reconstruction error, decreases monotonically and converges. Furthermore, we introduce a block orthogonal matching pursuit algorithm that also operates on sample batches to provide a warm start. Experiments on both natural image patches and UCI data sets show that the proposed algorithm produces a better approximation with the same sparsity levels compared to the state-of-the-art algorithms.
The Role of Interfirm Knowledge Spillovers for Innovation in Mass-Produced Environmental Technologies: Evidence from the Solar Photovoltaic Industry

Knowledge spillovers play a potentially important role for innovation and competitive dynamics in mass-produced environmental technologies. Currently, however, we lack research that studies knowledge spillovers in such technologies at the firm level. To address this shortcoming, in this article we investigate the drivers of technological innovation in the solar photovoltaic industry. We find clear evidence for the existence of interfirm knowledge spillovers and show that besides investments in R&D, investments in manufacturing equipment have served as a channel of knowledge absorption. Our findings shed new light on the narrative linking environmental innovation and competitive advantage. Moreover, by pointing to the role of process technology as a means of assimilating and exploiting external knowledge, we highlight an important but frequently neglected channel of absorptive capacity.
An Event-Oriented Multi-pass Sieve Module for Coreference Resolution

Coreference resolution is one of the key issues in the natural language processing, it can eliminate uncertain problems of event in the event-oriented natural language processing, and that is important for the upper application of event. This paper builds an event-oriented multi-pass sieve module for coreference resolution, and combined with the characteristics of the event, we add the constraint conditions to each sieve to improve the accuracy of each sieve. We use this module to carry on experiment for object elements of the event. Compared with machine learning method based on C4.5 decision tree, it has a very big enhancement on the performance.
Design of an Extensible Network Testbed with Heterogeneous Components

Virtualized network infrastructures are currently deployed in both research and commercial contexts. The complexity of the virtualization layer varies greatly in different deployments, ranging from cloud computing environments, to carrier Ethernet applications using stacked VLANs, to networking testbeds. In all of these cases, there are many users sharing the resources of one provider, where each user expects their resources to be isolated from all other users. Our work in this area is focused on network testbeds. In particular, we present the design of the latest version of the Open Network Laboratory (ONL) testbed. This redesign generalizes the underlying infrastructure to support resource extensibility and heterogeneity at a fundamental level. New types of resources (e.g., multicore PCs, FPGAs, network processors, etc) can be added to the testbed without modifying any testbed infrastructure software. Resource types can also be extended to support multiple distinct sets of functionality (e.g., an FPGA might act as a router, a switch, or a traffic generator). Moreover, users can dynamically add new resource extensions without any modification to the existing infrastructure. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Design of an Extensible Network Testbed with Heterogeneous Components Charlie Wiseman, Jyoti Parwatikar, Ken Wong, John DeHart, and Jonathan Turner Washington University in St. Louis wiseman@wustl.edu, {jp,kenw,jdd}@arl.wustl.edu, jon.turner@wustl.edu
Logarithmic Fourier PCA: A New Approach to Face Recognition

This paper proposes two face recognition algorithms namely Logarithmic Fourier Domain Principal Component Analysis (Log Fourier PCA) and Log fourier PCA with Independent Component Analysis (ICA) which successfully tackles multiple variations of face images. Neural network is used as classifier for both these methods. The Log Fourier PCA method proves to be resilient against illumination variations of the face images. However it is observed that the performance of this method decreases if there are multiple variations. Using ICA as a feature extractor further makes the recognition system robust to multiple parametric variations. Experimental results using Yale, ORL, FERET and PIE database shows that the proposed method Log fourier PCA followed by ICA and Neural network can tackle multiple variations in the face images. Their comparisons with other hybrid systems such as ICA-Neural Network and Fourier PCA-Neural network methods are also presented.