Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Hierarchical Clustering Algorithm via Root Searching

As an important branch of machine learning, clustering is wildly used for data analysis in various domains. Hierarchical clustering algorithm, one of the traditional clustering algorithms, has excellent stability yet relatively poor time complexity. In this paper, we proposed an efficient hierarchical clustering algorithm by searching given nodes' nearest neighbors iteratively, which depends on an assumption: the representative node (root) may exist in the densest data area. The experiments results preformed on 14 UCI datasets show that our algorithm exhibits the best accuracies on most datasets. Moreover, our method has a linear time complexity which is significantly better than other traditional clustering methods like UPGMA and K-Means.
D‐Aspartic acid is a novel endogenous neurotransmitter

D‐Aspartic acid (D‐Asp) is present in invertebrate and vertebrate neuroendocrine tissues’ where it carries out important physiological functions and is implicated in nervous system development. We show here that D‐Asp is a novel endogenous neurotransmitter in two distantly related animals’ a mammal (Rattus norvégiens) and a mollusk (Loligo vulgaris). Our main findings demonstrate that D‐Asp is present in high concentrations in the synaptic vesicles of axon terminals;synthesis for this amino acid occurs in neurons by conversion of L‐Asp to D‐Asp via D‐aspartate racemase;depolarization of nerve endings with K+ ions evokes an immediate release of D‐Asp in a Ca2+ dependent manner;specific receptors for D‐Asp occur at the postsynaptic membrane’ as demonstrated by binding assays and by the expansion of squid skin chromatophores;D‐aspartate oxidase’ the specific enzyme that oxidizes D‐Asp’ is present in the postsynaptic membranes;and stimulation of nerve endings with D‐Asp triggers signal transduction by increasing the second messenger cAMP. Taken together’ these data demonstrate that D‐Asp fulfills all criteria necessary to be considered a novel endogenous neurotransmitter. Given its known role inneurogenesis’ learning’ and neuropathologies’ our results have important implications for biomedical and clinical research. —D'Aniello, S. , Somorjai, I. , Garcia‐Fernàndez, J., Topo, E. , D'Aniello, A. D‐Aspartic acid is a novel endogenous neurotransmitter. FASEB J. 25’ 1014–1027 (2011). www.fasebj. org
A Robust Formant Extraction Algorithm Combining Spectral Peak Picking and Root Polishing

We propose a robust formant extraction algorithm that combines the spectral peak picking, formants location examining for peak merger checking, and the root extraction methods. The spectral peak picking method is employed to locate the formant candidates, and the root extraction is used for solving the peak merger problem. The location and the distance between the extracted formants are also utilized to efficiently find out suspected peak mergers. The proposed algorithm does not require much computation, and is shown to be superior to previous formant extraction algorithms through extensive tests using TIMIT speech database.
Derivative and Integral Sliding Mode Control for Rotary Drilling System

This paper presents a derivative and integral sliding mode controller in order to handle the stick-slip oscillation of rotary drilling system. The control method has induced integral of tracking error and the derivatives of tracking error are replaced by state variables, the developed controller uses the nonlinear derivative control for suppressing system oscillation and increasing system damping as error approximates to zero. Lyapunov principles have been carried out to verify the stability and robustness of system. The simulation results show that the controller has faster dynamic response, can further increase accurate rate of the control system, and can suppress stick-slip oscillation of rotary drilling system.
Exploring concept graphs for biomedical literature mining

Full-text publications in an electronic form become more prevalent than ever before. It is a difficult challenge to extract concepts from unstructured document collections data because different concepts and their relationships are buried in them and ample term variations make the challenge compound. Extracted concepts are useful instruments of managing and searching large document collections and play a pivotal role in indexing electronic documents and building digital libraries. In this paper we explore a biomedical concept extraction technique based on a ranking algorithm of concept graphs. The proposed technique comprises two major steps: the first step is to represent documents with graphs whose nodes and edges are created by Named Entity Recognition and UMLS Semantic Network. The second step is rank concepts with relative importance algorithms. We evaluate our technique with a set of biomedical full-texts and compare it to various different key-phrase extraction and graph ranking techniques. The experimental results show that our technique achieves the best performance over other compared algorithms. We further take a close look at the properties of the network to examine how concepts are related to each other and what concept plays a dominant role in the network. To this end, we build the network with 526 full-text articles published in PubMed Central and measure the significance of nodes by centrality.
One Protoplast Is Not the Other!1[W]

Protoplasts are widely used to study a variety of cellular processes because they are easy to transform, manipulate and image at high resolution. Protoplasts are thought to loose their original cell identity and therefore their origin is of minor unimportance. Hence, leaf mesophyll protoplasts are generally used to study processes active in other cell-types. However, there is no consensus whether data from protoplasts hold true for cells in intact tissues. We developed a procedure to isolate protoplasts from petals and compared their features with those of leaf protoplasts and intact tissues. Petal protoplasts derived from epidermis or mesophyll can be easily distinguished by their color and accurately reproduce the cell-specific differences in gene regulation and protein sorting observed in the intact tissue. We show that protoplasts maintain the features of the original cells in the intact tissue and, depending on their origin may produce completely different results in gene expression or protein localization assays. Consequently, analyses in leaf mesophyll protoplasts of genes or proteins that are expressed in other cell-types should be interpreted with caution. We conclude that with a few simple adjustments, to distinguish different cell-types, protoplasts can provide an excellent tool to reliably analyze highly cell-specific processes.
Praf2 Is a Novel Bcl-xL/Bcl-2 Interacting Protein with the Ability to Modulate Survival of Cancer Cells

Increased expression of Bcl-xL in cancer has been shown to confer resistance to a broad range of apoptotic stimuli and to modulate a number of other aspects of cellular physiology, including energy metabolism, cell cycle, autophagy, mitochondrial fission/fusion and cellular adhesion. However, only few of these activities have a mechanistic explanation. Here we used Tandem Affinity purification to identify novel Bcl-xL interacting proteins that could explain the pleiotropic effects of Bcl-xL overexpression. Among the several proteins co-purifying with Bcl-xL, we focused on Praf2, a protein with a predicted role in trafficking. The interaction of Praf2 with Bcl-xL was found to be dependent on the transmembrane domain of Bcl-xL. We found that Bcl-2 also interacts with Praf2 and that Bcl-xL and Bcl-2 can interact also with Arl6IP5, an homologue of Praf2. Interestingly, overexpression of Praf2 results in the translocation of Bax to mitochondria and the induction of apoptotic cell death. Praf2 dependent cell death is prevented by the co-transfection of Bcl-xL but not by its transmembrane domain deleted mutant. Accordingly, knock-down of Praf2 increases clonogenicity of U2OS cells following etoposide treatment by reducing cell death. In conclusion a screen for Bcl-xL-interacting membrane proteins let us identify a novel proapoptotic protein whose activity is strongly counteracted exclusively by membrane targeted Bcl-xL.
Reactive navigation by fuzzy controlG

Reactive navigation can improve the performance of robotic systems by solving simple subtasks without requiring the update of internal representations or plans: it involves only the mapping of perceptual situations onto control commands. Fuzzy systems and neural networks are both eecient methods for learning such a mapping from training data, avoiding its annoying and error-prone programming. Moreover fuzzy systems allow the use of a-priori knowledge about the task and the veriication of the strategy acquired during the learning phase. This paper presents a control system for wall-following which is intended to be the rst brick of a more articulated control system including all the low-level behaviors needed for the safe navigation of an autonomous mobile vehicle. The wall-follower works on data supplied by an ultrasonic sensor ring. The mapping between the input space and the control command space is generated automatically from training data acquired during operator-driven runs of the vehicle. The rst experimental results, obtained by controlling a TRC Labmate with the obtained fuzzy system inside an indoor environment, point out that even very simple training sessions allow the derivation of fuzzy rules which have proved eeective in solving the task at hand.
Capacitor array structure and switching control scheme to reduce capacitor mismatch effects for SAR analog-to-digital converters

This paper presents a new capacitor array structure developed for SAR analog-to-digital converters and its switching algorithm that can alleviate capacitor mismatch effects. The capacitor mismatch can induce many missing codes. The proposed capacitor array structure is based on the junction-splitting method as is efficient in terms of power consumption. To reduce the capacitor mismatch effects, two capacitor arrays are employed to enable redundant search. Simulation results show that the proposed method significantly reduces the number of missing codes caused by the capacitance mismatch, and reduces the energy consumption by more than 70% compared to the conventional charge redistribution method.
Long-Run Behavior of Games with Many Players

We discuss similarities and differencies between systems of many interacting players maximizing their individual payoffs and particles minimizing their interaction energy. We analyze long-run behavior of stochastic dynamics of many interacting agents in spatial and adaptive population games. We review results concerning the effect of the number of players and the noise level on the stochastic stability of Nash equilibria. In particular, we present examples of games in which when the number of players or the noise level increases, a population undergoes a transition between its equilibria.
Single-channel speech separation based on NLMSE quasi-KLT bases

In this paper, we propose a new single-channel speech separation algorithm by l0 optimization using nonhomogeneous linear mean square estimation (NLMSE) quasi-KLT basis. First, we propose to construct NLMSE quasi-KLT bases from the single-channel mixture by diagonalizing the approximate autocorrelation matrices of speech sources, which are constructed by a linear combination of generated templates. Second, based on the NLMSE quasi-KLT bases, single-channel speech separation is performed by l0 optimization. Finally, simulation results demonstrating the effective performance of single-channel separation of two speech sources using the NLMSE quasi-KLT bases are presented.
Digital image library development in academic environment: designing and testing usability

Purpose – By reporting the experience gained in the development of a digital image library in the academic environment, this paper aims at providing perspective developers with insights on the main usability issues raised by this type of project.Design/methodology/approach – The paper addresses three common needs in academia with respect to image collections: preservation, access, and reuse. In the framework of the specific project experience, it discusses how usability issues have been tackled at design time, highlights the usability problems revealed by tests on the first implemented prototype, and advances proposals on how these problems may be addressed.Findings – Team formation and high turn‐over impact usability design; collection management functionalities effect final product usability; usability and resource reuse levels are severely reduced if the services are limited to those of classic digital libraries.Research limitations/implications – All usability issues are discussed with respect to the .
DCSK Multi-Access Scheme for UHF RFID System

DCSK modulation in chaos communication is a robust non-coherent modulation scheme. In this paper, the multiple-access DCSK scheme based on the OVSF code is proposed. Using the multiple-access DCSK scheme in RFID system, a DCSK-RFID system is presented. In the presented DCSK-RFID system, we use the DCSK for tag modulation for its low complexity and the simple receiver of the DCSK scheme is applied in reader part. The tag's BER performance of the proposed DCSK-RFID system is carefully generalized both in theoretic analysis and in simulations. From the simulation results, the theoretical and simulation values match closely with each other. Then, we design an anti-collision MAC protocol based on multi-access DCSK-RFID scheme. We theoretically analyze the throughout in given number of tags. The simulation shows that the proposed algorithm has better throughout than S-Aloha system.
Power constrained linear estimation of correlated sources in hierarchical wireless sensor networks

We propose a power allocation scheme for estimation in hierarchical wireless sensor networks. The sensors in the network are divided into disjoint clusters and each cluster observes a random source which is correlated with the sources being observed by other clusters. The estimation is performed in two steps: in the first step, the sensors in each cluster send a scaled version of their noisy measurements to their respective cluster-head (CH) which forms a preliminary estimate of the underlying source; and in the second step, the CHs send their partial estimates to a remote fusion center (FC) for final estimation. The estimates are based on LMMSE estimation rule. The communication between the sensors and the CHs, and between the CHs and the FC takes place on orthogonal channels. The proposed power allocation scheme minimizes the estimation distortion subject to constraints on the network power consumption. Effectiveness of the scheme is illustrated with simulation examples.
EXPERIMENTS OF ZERNIKE MOMENTS FOR LEAF IDENTIFICATION

So far, plant identification has challenges for sev eral researchers. Various methods and features have been proposed. However, there are still many approaches could be investigated to develop robust plant identification systems. This paper reports several experiments in using Zernike moments to build folia ge plant identification systems. In this case, Zernike moments were combined with other features: geometric features, color moments and gray-level co-occurrenc e matrix (GLCM). To implement the identifications systems, two approaches has been investigated. Firs t approach used a distance measure and the second u sed Probabilistic Neural Networks (PNN). The results sh ow that Zernike Moments have a prospect as features in leaf identification systems when they are combin ed with other features.
Information-preserving surface restoration and feature extraction for digital elevation models

Preprocessing such as filtering data in order to remove or at least reduce noise is a crucial step because information which is lost during this filtering cannot be recovered in subsequent steps. It is a well-known fact, that linear filtering does not only reduce noise, but may also lead to a loss of information due to the global smoothing, regardless of structures in the data. In order to overcome these drawbacks, we propose using an algorithm for parameter free information- preserving surface restoration. As we do not want to evaluate the results of the filtering only qualitatively by visual inspection, we examine the influence of pre-processing on feature extraction for digital elevation models and discuss quantities for the evaluation of these influences.
Shadow Matching: A New GNSS Positioning Technique for Urban Canyons

The Global Positioning System (GPS) is unreliable in dense urban areas, known as urban canyons, which have tall buildings or narrow streets. This is because the buildings block the signals from many of the satellites. Combining GPS with other Global Navigation Satellite Systems (GNSS) significantly increases the availability of direct line-of-sight signals. Modelling is used to demonstrate that, although this will enable accurate positioning along the direction of the street, the positioning accuracy in the cross-street direction will be poor because the unobstructed satellite signals travel along the street, rather than across it. A novel solution to this problem is to use 3D building models to improve cross-track positioning accuracy in urban canyons by predicting which satellites are visible from different locations and comparing this with the measured satellite visibility to determine position. Modelling is used to show that this shadow matching technique has the potential to achieve metre-order cross-street positioning in urban canyons. The issues to be addressed in developing a robust and practical shadow matching positioning system are then discussed and solutions proposed.
A Practical Approach for Generating Failure Data for Assessing and Comparing Failure Prediction Algorithms

Failure Prediction allows improving the dependability of computer systems, but its use is still uncommon due to scarcity of failure-related data that can be used for training, assessing and comparing alternative failure predictors. As failures are rare events and the characteristics of failure data varies from system to system, in this paper we propose the use of realistic software fault injection to facilitate the generation of failure data on a particular system installation. In practice, we propose a comprehensive experimental approach that allows generating failure data in short time and we study the applicability and limitations of such process in assessing and comparing alternative failure prediction algorithms. A case study is presented comparing four algorithms for predicting failures in a system based on a Windows OS. Results show that using fault injection allows to dramatically speed up the generation of failure data and that the proposed procedure can be used in practice.
Asset Recovery and Incorporation into Product Lines

Software product lines aim in having a common platform from which several similar products can be derived. The elements of the platform are called assets and they are managed in an asset base being part of the product line infrastructure. The products are then built on top of the assets. Assets can include own developments, open source or third-party software modules, as well as design and project documents. In the context of the European-wide project FAMILIES we concentrated on techniques used to build the platform with focus on the recovery of these assets from existing systems. We present an approach on how to incorporate existing assets into the product line infrastructure. Thereby we explicitly distinguish the asset origins and the different information sources available. The incorporation is a quality-driven process that is backed up by a set of reverse engineering techniques to evaluate the asset’s internal quality. The quality assessment of an asset is the critical measurement for industrial development organizations in order to incorporate assets into their product line infrastructure.
Unified Vdd Vth Optimization Based DVFM Controller for a Logic Block

In this paper analytical expressions for optimal V<sub>dd</sub> and V<sub>th</sub> to minimize energy for a given speed constraint are derived. These expressions are based on the EKV model for transistors and are valid in both strong inversion and sub threshold regions. The effect of gate leakage on the optimal V<sub>dd</sub> and V<sub>th</sub> is analyzed. A new gradient based algorithm for controlling V<sub>dd</sub> and V<sub>th</sub> based on delay and power monitoring results is proposed. A V<sub>dd</sub>-V<sub>th</sub> controller which uses the algorithm to dynamically control the supply and threshold voltage of a representative logic block (sum of absolute difference computation of an MPEG decoder) is designed. Simulation results using 65 nm predictive technology models are given.
“Microstructured Nanostructures” – Nanostructuring by Means of Conventional Photolithography and Layer-expansion Technique

A new and simple method for nanostructuring using conventional photolithography and layer expansion or pattern-size reduction technique is presented, which can further be applied for the fabrication of different nanostructures and nano-devices. The method is based on the conversion of a photolithographically patterned metal layer to a metal-oxide mask with improved pattern-size resolution using thermal oxidation. With this technique, the pattern size can be scaled down to several nanometer dimensions. The proposed method is experimentally demonstrated by preparing nanostructures with different configurations and layouts, like circles, rectangles, trapezoids, “fluidic-channel”-, “cantilever”- and meander-type structures.
A Review of Passive and Active Battery Balancing based on MATLAB / Simulink

– Battery systems are affected by many factors, a key one being the cells unbalancing. Without the balancing system, the individual cell voltages will differ over time, battery pack capacity will decrease quickly. That will result in the failure of the total battery system. Thus cell balancing acts an important role on the battery life preserving. Different cell balancing methodologies have been proposed for battery pack. This paper presents a review and comparisons between the different proposed balancing topologies for battery string based on MATLAB/Simulink ® simulation. The comparison has been carried out according to circuit design, balancing simulation, voltage/current stress, practical implementations, application, balancing speed, complexity
An information theoretic kernel algorithm for robust online learning

Kernel methods are widely used in nonlinear modeling applications. In this paper, a robust information theoretic sparse kernel algorithm is proposed for online learning. In order to reduce the computational cost and make the algorithm suitable for online applications, we investigate an information theoretic sparsification rule based on the mutual information between the system input and output to determine the update of the dictionary (support vectors). According to the rule, only novel and informative samples are selected to form a sparse and compact dictionary. Furthermore, to improve the generalization ability, a robust learning scheme is proposed to avoid the algorithm over learning the redundant samples, which assures the convergence of the learning algorithm and makes the learning algorithm converge to its steady state much faster. Experiment are conducted on practical and simulated data and results are shown to validate the effectiveness of our proposed algorithm.