Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuronal Encoding of Texture in the Whisker Sensory Pathway

A major challenge of sensory systems neuroscience is to quantify brain activity underlying perceptual experiences and to explain this activity as the outcome of elemental neuronal response properties. Rats make extremely fine discriminations of texture by “whisking” their vibrissae across an object's surface, yet the neuronal coding underlying texture sensations remains unknown. Measuring whisker vibrations during active whisking across surfaces, we found that each texture results in a unique “kinetic signature” defined by the temporal profile of whisker velocity. We presented these texture-induced vibrations as stimuli while recording responses of first-order sensory neurons and neurons in the whisker area of cerebral cortex. Each texture is encoded by a distinctive, temporally precise firing pattern. To look for the neuronal coding properties that give rise to texture-specific firing patterns, we delivered horizontal and vertical whisker movements that varied randomly in time (“white noise”) and found that the response probabilities of first-order neurons and cortical neurons vary systematically according to whisker speed and direction. We applied the velocity-tuned spike probabilities derived from white noise to the sequence of velocity features in the texture to construct a simulated texture response. The close match between the simulated and real responses indicates that texture coding originates in the selectivity of neurons to elemental kinetic events.
Visual analytics of large multidimensional data using variable binned scatter plots

The scatter plot is a well-known method of visualizing pairs of two-dimensional continuous variables. Multidimensional data can be depicted in a scatter plot matrix. They are intuitive and easy-to-use, but often have a high degree of overlap which may occlude a significant portion of data. In this paper, we propose variable binned scatter plots to allow the visualization of large amounts of data without overlapping. The basic idea is to use a non-uniform (variable) binning of the x and y dimensions and plots all the data points that fall within each bin into corresponding squares. Further, we map a third attribute to color for visualizing clusters. Analysts are able to interact with individual data points for record level information. We have applied these techniques to solve real-world problems on credit card fraud and data center energy consumption to visualize their data distribution and cause-effect among multiple attributes. A comparison of our methods with two recent well-known variants of scatter plots is included.
The Influence of Mobile Product Information on Brand Perception and Willingness to Pay for Green and Sustainable Products

Efficient product packaging does not allow companies to provide extensive information to customers, but effort for consumers to assess more information online is high. Therefore, mobile tagging in terms of extended packaging is considered one of the most promising technologies within Marketing and Information Systems (IS). It is considered to be a means to easily communicate information to consumers because it efficiently bridges the gap between product and information. However, evidence that allows managers to validate the positive perception of this technology is not yet available. This paper addresses this research gap by investigating how extended packaging influences brand perception and willingness to pay. We show that for green products that were produced organically and sustainably, this technology can be used to heighten the brand perception. Furthermore, we show that transmitting relevant information increases the willingness to pay significantly for organic goods, but not for sustainably produced goods.
TORQUE CONTROL OF AN EXOSKELETAL KNEE WITH EMG SIGNALS

Abstract This paper introduces a control scheme and algorithm for a powered orthosis. Recognizing the intended motion is based on real-time evaluation of EMG signals recorded from the operator’s leg muscles. The desired motion is executed with a torque controller for an electric linear actuator. In contrast to most of the previous approaches for similar applications, this is performed without pattern classification and without a dynamic biomechanical model of the human body.
Accuracy of Targeting and Implications of Rationing in Mahatma Gandhi National Rural Employment Guarantee Scheme: Evidence from West Bengal, India

Taking 66th round data of National Sample Survey (2009-10), this paper examines the implications of type 1 and type 2 rationing of work under Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) in West Bengal for poorer households. Using regression analysis and evidences from field, it attempts to find, if the households, which sought but did not get work, are poorer (by various indicators) than the ones, who got work (type 2) and then explores, if the poor get work for lesser number of days (type 1). The results show that poor households do not have significantly higher probability of getting work compared to non poor. This has left behind a substantial exclusion of poor, without work. It further gives evidence that non poor households get work for more number of days than poor households. The findings also highlight the importance of awareness of its entitlements to realize the desired impact.
Elevation of Matrix Metalloproteinases in Different Areas of Ascending Aortic Aneurysms in Patients with Bicuspid and Tricuspid Aortic Valves

Our aim is to investigate the elevation of matrix proteins in tissues obtained from distal, above the sinotubular junction (proximal), concave, and convex sites of aneurysms in the ascending aorta using a simultaneous multiplex protein detection system. Tissues were collected from 41 patients with ascending aortic aneurysms. A total of 31 patients had a bicuspid aortic valve (BAV), whereas 10 had a tricuspid aortic valve (TAV). Concave and convex aortic site samples were collected from all patients, whereas proximal and distal convexity samples were obtained from 19 patients with BAV and 7 patients with TAV. Simultaneous detection of matrix metalloproteinases (MMPs) and their inhibitors (TIMPs) was performed at each of the four aortic sites. MMP-2 levels were higher in the concave aortic sites than in the convex aortic sites. In contrast, MMP-8 levels were higher in the convex sites than in the concave sites, as were MMP-9 levels. In both BAV and TAV patients, TIMP-3 levels were higher in the concave sites than in the convex sites. However, TIMP-2 and TIMP-4 levels were significantly elevated in the sinotubular proximal aorta of BAV patients. Simultaneous detection of MMPs and TIMPs revealed different levels at different aortic sites in the same patient.
On Model Order Determination For Complex Exponential Signals: Performance Of An FFT-initialized ML Algorithm

We present an algorithm for model order determination and simultaneous maximum likelihood parameter estimation for complex exponential signal modeling. The algorithm exploits initial nonparametric (i.e., FFT) frequency location estimates and Cram&-Rru, Bound (CRB) resolution limits to significantly reduce the search space for the correct model order and parameter estimates. The algorithm initially overestimates the model order. After iterative minimization to obtain maximum likelihood (ML) parameter estimates for that order, a post-processing step eliminates the extraneous sinusoidal modes using CFU3 resolution limits and statistical detection tests. Because the algorithm searches on only a limited set of model orders and parameter regions, it is computationally tractable even for large data lengths and model orders. In this paper we analyze the performance of the algorithm and compare with other existing approaches.
Position Recognition to Support Bedsores Prevention

In this paper, a feasibility study where small wireless devices are used to classify some typical user's positions in the bed is presented. Wearable wireless low-cost commercial transceivers operating at 2.4 GHz are supposed to be widely deployed in indoor settings and on people's bodies in tomorrow's pervasive computing environments. The key idea of this study is to leverage their presence by collecting the received signal strength (RSS) measured among fixed devices, deployed in the environment, and the wearable one. The RSS measurements are used to classify a set of user's positions in the bed, monitoring the activities of patients unable to make the desirable bodily movements. The collected data are classified using both support vector machine and K-nearest neighbor methods, in order to recognize the different user's position, and thus supporting the bedsores issue.
An Improved Performance of Watermarking In DWT Domain Using SVD

In this paper, the concept of hybridizing the transforms is used in order to enhance the performance of watermarking method. Apart from this, the original image is first performed with DCT compression operation before decomposition of image which reduces the mean square error (MSE) and hence increases the quality of watermarked image. The performance of proposed DWT-SVD based methods is evaluated both quantitatively as well as qualitatively. For quantitative evaluation, the metrics like: peak signal to noise ratio (PSNR) by con- sidering human visual system and correlation coefficient (CC) are computed. The results indicate that there is an enhancement in the quality of watermarked image by reducing the MSE. Robustness of the method is also tested by performing various attacks on the watermarked image.
Data Clustering via Principal Direction Gap Partitioning

We explore the geometrical interpretation of the PCA based clustering algorithm Principal Direction Divisive Partitioning (PDDP). We give several examples where this algorithm breaks down, and suggest a new method, gap partitioning, which takes into account natural gaps in the data between clusters. Geometric features of the PCA space are derived and illustrated and experimental results are given which show our method is comparable on the datasets used in the original paper on PDDP.
Retrieval of Optimal Subspace Clusters Set for an Effective Similarity Search in a High-Dimensional Spaces

High dimensional data is often analysed resorting to its distribution properties in subspaces. Subspace clustering is a powerfull method for elicication of high dimensional data features. The result of subspace clustering can be an essential base for building indexing structures and further data search. However, a high number of subspaces and data instances can conceal a high number of subspace clusters some of which are difficult to analyse within search algorithm. This paper presents a model of generic indexing approach based on detected subspace clusters and the way to find an optimal set of clusters to have an acceptable tradeoff between search speed and relevance.
Scaling Reinforcement Learning Paradigms for Motor Control

Reinforcement learning offers a general framework to explain reward related learning in artificial and biological motor control. However, current reinforcement learning methods rarely scale to high dimensional movement systems and mainly operate in discrete, low dimensional domains like game-playing, artificial toy problems, etc. This drawback makes them unsuitable for application to human or bio-mimetic motor control. In this poster, we look at promising approaches that can potentially scale and suggest a novel formulation of the actor-critic algorithm which takes steps towards alleviating the current shortcomings. We argue that methods based on greedy policies are not likely to scale into high-dimensional domains as they are problematic when used with function approximation – a must when dealing with continuous domains. We adopt the path of direct policy gradient based policy improvements since they avoid the problems of unstabilizing dynamics encountered in traditional value iteration based updates. While regular policy gradient methods have demonstrated promising results in the domain of humanoid notor control, we demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. Based on this, it is proved that Kakade’s ‘average natural policy gradient’ is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges with probability one to the nearest local minimum in Riemannian space of the cost function. The algorithm outperforms nonnatural policy gradients by far in a cart-pole balancing evaluation, and offers a promising route for the development of reinforcement learning for truly high-dimensionally continuous state-action systems.
Prioritized Constraints with Data Sampling Scores for Automatic Test Data Generation

Many automatic test data generation approaches use constraint solvers to find data values. One problem with this method is that it cannot generate test data when the constraints are not solvable, either because there is no solution or the constraints are too complex. We propose a constraint prioritization method using data sampling scores to generate valid test data even when a set of constraints is not solvable. Our case study illustrates the effectiveness of this method.
A calculation simplified DPA attack based on reduced template

In a traditional template attack, during the template building phase, a large amount of calculation is usually needed, or, what is worse, some impossible calculation might be occurred to get the covariance matrix. Through a deep researching of template attack techniques, this paper has improved the template building method and proposed a new way which greatly simplified the template structure to avoid these problems. Also, we independently completed the development of a side channel power data acquisition platform and compiled the supporting side channel attack software. Targeting at the AES encryption algorithm implemented on a Mage16 single-chip board, we carried out a DPA attack based on template built by our method, the experimental results show that this method can effectively avoid the numerical problems that may be occurred in the traditional template attack, and compared to the ordinary DPA attacks the number of energy traces required by the attacks reduced substantially. It is strongly proved that the DPA attack based on our template is effective and practical.
Robust servomechanism with preview action for polytopic uncertain systems

This paper considers the robust servomechanism design for a polytopic uncertain system subject to a previewable reference signal. Based on the robust mixed LQ/ℋ∞ criterion, we develop a design method of a state feedback controller with integral and preview actions achieving the robust tracking performance in terms of linear matrix inequalities. A numerical example is also included to show the applicability of the present design method. Copyright © 2000 John Wiley & Sons, Ltd.
Extended Bayesian LASSO for Multiple Quantitative Trait Loci Mapping and Unobserved Phenotype Prediction

The Bayesian LASSO (BL) has been pointed out to be an effective approach to sparse model representation and successfully applied to quantitative trait loci (QTL) mapping and genomic breeding value (GBV) estimation using genome-wide dense sets of markers. However, the BL relies on a single parameter known as the regularization parameter to simultaneously control the overall model sparsity and the shrinkage of individual covariate effects. This may be idealistic when dealing with a large number of predictors whose effect sizes may differ by orders of magnitude. Here we propose the extended Bayesian LASSO (EBL) for QTL mapping and unobserved phenotype prediction, which introduces an additional level to the hierarchical specification of the BL to explicitly separate out these two model features. Compared to the adaptiveness of the BL, the EBL is “doubly adaptive” and thus, more robust to tuning. In simulations, the EBL outperformed the BL in regard to the accuracy of both effect size estimates and phenotypic value predictions, with comparable computational time. Moreover, the EBL proved to be less sensitive to tuning than the related Bayesian adaptive LASSO (BAL), which introduces locus-specific regularization parameters as well, but involves no mechanism for distinguishing between model sparsity and parameter shrinkage. Consequently, the EBL seems to point to a new direction for QTL mapping, phenotype prediction, and GBV estimation.
On Finite Model Theory

The subject of this paper is the part of finite model theory intimately related to the classical model theory. In the very beginning of our career in computer science, we attended a few lectures on database theory where databases were inconspicuously allowed to be infinite and then classical model-theoretical theorems were applied. The use of infinite databases aroused our suspicion and prompted us to investigate the status of some most famous modeltheoretical theorems in the case of finite structures [Gu84]. The theorems miserably fail. One theorem (a theorem of Roger Lyndon: Every sentence monotone in a predicate P is logically equivalent to a sentence positive in P [Ly59]) resisted the attack and was refuted by Miklos Ajtai and ourselves later [AG87]. In Section 1, we give some old and new counter-examples to classical model-theoretic theorems in the finite case. Of course, some classical theorems survive the transition to the finite case. For example, Ehrenfeucht’s game-theoretic characterization [Eh61] of the indistinguishability of two structures by sentences of a bounded depth remains valid in a very obvious way. For a while, it seemed however that no classical model-theoretic theorems (especially those whose proof involves the compactness theorem in one way or another) survive the transition to the finite case in a non-trivial way. The first, to our knowledge, positive result into this direction was published by Miklos Ajtai and ourselves [AG89]. The result is explained in Section 2. The second positive result was proven recently by Saharon Shelah and ourself [GS90]; this is the subject of Section 3. This article reflects a lecture given to Workshop on Feasible Mathematics that took place in June 1989 in Cornell University. (It was a wonderful, very well organized workshop with many interesting lectures and plenty time for discussions.) The reflection is not very faithful though. First, we skipped the part on logics tailored for computational complexity ∗Final version in: “Feasible Mathematics, Workshop, Cornell University, June, 1989” (ed. S.R. Buss and P.J. Scott), Perspectives in Computer Science, Birkhauser, 1990. †Address: EECS Department, University of Michigan, Ann Arbor, MI 48109-2122, USA. The work was partially supported by NSF grants DCR 85-03275 and CCR 89-04728.
The effectiveness of teaching introductory electronics in an integrated teaching studio environment

City University of Hong Kong has initiated a studio approach to teaching, starting with modules in introductory science and engineering. Studio teaching replaces the traditional large-group lecture, small-group tutorial and separate laboratory work with an integrated approach. It utilises computer-based teaching materials that emphasise multimedia and interactive learning. This paper describes the introduction of studio teaching for an introductory electronic engineering course designed for first-year Mechatronics Engineering degree students. The initial results for assessing the effectiveness of this type of teaching method are presented.
Measurement of the length of pedestrian crossings - a navigational aid for blind people

This paper describes a fast as well as simple method for measurement of the length of pedestrian crossings using a single camera for the purpose of a travel aid for blind people. In a crossing, the usual black road surface is painted with constant width periodic white lines. In Japan, this width is 45 cm. The crossing region as well as its length is detected using this concept. At first, the crossing direction is determined from the power spectrum using fast Fourier transform. The periodic white and black bands are detected using integration along the crossing direction and differentiation of the integral data perpendicular to the crossing direction. Then the crossing length is calculated using the number of white and black bands in the crossing region. Experiment is performed using real road scenes with pedestrian crossing. The technique is successful in measuring the length of the crossing with high accuracy if the image resolution is good and the white paintings on the crossing are fine.
Quantitative evaluation of coreference algorithms in an information extraction system

Algorithms for performing coreference resolution can only be precisely evaluated given a benchmark corpus of coreference-annotated texts, together with techniques for evaluating the algorithms' output against the corpus. Such a corpus and such techniques have become available for the rst time as part of the Message Understanding Conference 6 (MUC-6) evaluations of information extraction systems. In this paper we describe the MUC-6 coreference task and the approach to taken to it by the Large Scale Information Extraction (LaSIE) system developed at the University of Sheeeld. The basic coreference algorithm used by this system is described in detail, as well as a set of variants, which allow us to experiment with diierent constraints such as restrictions to certain classes of anaphor, distance restrictions between anaphor and antecedent, and weighting factors in assessing semantic similarity of potential coreferents. Quantitative evaluation results are presented for these variants, demonstrating both the utility of quantative analysis for assessing coreference algorithms and the exibility of our approach to coreference which provides a framework that facilitates experimentation with alternative techniques.
Using Multiple Coordinated Views for Multiple Datasets Analysis

The goal of this paper is to present a web based information visualization application with the use of Silverlight technology. We have chosen this technology to better use the processing power of the client application in order to support the use of advanced interactions and the same interface components of a desktop application.Also, the information visualization application uses multiple coordinated views and multiple simultaneous datasets. We highlight the application layout configuration by the user, including the flexibility to specify the number of data views and to associate different datasets for each visualization techniques.
SDMOQL: An OQL-based Data Mining Query Language for Map Interpretation Tasks

Spatial data mining denotes the extraction of patterns from both spatial and aspatial data, possibly stored in a spatial database. An important application of spatial data mining methods is the extraction of knowledge from a Geographic Information System. INGENS (Inductive Geographic Information System) is a prototype GIS which integrates data mining tools to assist users in their task of topographic map interpretation. The system can mine geographical concepts that are not explicitly available in the database. The spatial data mining process is aimed at a user who controls the parameters of the process by means of a mining query written in a mining query language. This paper presents SDMOQL, a spatial mining query language, based on the standard OQL, which permits the specification of the task-relevant data, the kind of knowledge to be mined, the background knowledge and the hierarchies, and the interestingness measures. SDMOQL currently supports two data mining tasks in INGENS: inducing classification rules and discovering association rules. Only the part of the language defined for classification tasks is presented in the paper. Some constraints on the query language are identified by the particular mining task. The syntax of the query language is described and the application to a real repository of maps is briefly reported.