Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparation of Poly(DL-Lactide/ε-Caprolactone)– β -TCP Compozites for Bone Tissue Repair

Poly-D,L-Lactide/e-caprolactone (PDLLA-e-CL) and β-tri-calcium phosphate (β-TCP) composites were prepared as an alternative for injectable filling material in bone tissue repair. β-TCP was synthesized by a wet precipitation method and characterised using Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffractometer (XRD), Zeta-SIZER and Brunauer–Emmett–Teller (BET) analyser. PDLLA-e-CL copolymer was synthesized under nitrogen (N2) atmosphere using ring-opening polymerization in the presence of stannous octoate as a catalyst. The chemical structures of copolymers were determined by FTIR and Proton Nuclear Magnetic Resonance (1H-NMR) analysis. The average molecular weights of copolymers were identified by Gel Permeation Chromatography (GPC). (PDLLA-e-CL)-β-TCP composites were then prepared by loading differing amounts of β-TCP to the copolymer phase. The resulting composites were easily shaped by hand. The degradation profiles of the composites were determined by monitoring the changes in the molecular weight of the co-polymers over a period of 42 days. Degradation rate of the composites decreased as the percentage of β-TCP in the structure increased.
Lane Mark Detection with Active Contour for Gradient Domain

The extraction of lane mark is an essential step for several vision-based systems in intelligent transportation applications. In this paper, we propose a novel method for lane detection using a new active contour model. The new model is a modified version of the classical C-V model in gradient domain. The initial level set curve can be anywhere in the image without changing the segmentation result. To select the lane mark contour, moment invariant is utilized to make shape analysis. The experiment results on real scene image show that the proposed algorithm could detect lane mark accurately.
Proactive neighbor localization based on distributed geographic table

Real-time tracking of massive numbers of mobile devices, either carried by humans or embedded into vehicles, is a challenging problem whose solution may pave the way for a large set of valuable applications, ranging from social networking to ambient intelligence. A centralized approach, i.e. a server collects position data and provides it to interested consumers, is highly questionable, as performance can hardly scale up to the needs several million concurrent users. On other hand, a decentralized peer-to-peer approach, for which positioning data would flow directly among mobile devices may be very appealing, provided that messages to be routed are not too frequent and too expensive in terms of bandwidth usage. In this context we propose a peer-to-peer overlay scheme called Distributed Geographic Table (DGT), where each participant can efficiently retrieve node or resource information (data or services) located near any chosen geographic position. In particular, we describe a DGT-based localization protocol, that allows each peer for proactively discovering and tracking all the peers that are geographically near to itself. We provide a performance analysis of our protocol, referring to a simulated (although realistic) scenario where several hundred vehicles move on a real map. Our results show that the solution is efficient, scalable and highly adaptable to different application scenarios.
Wireless mesh network planning: A multi-objective optimization approach

A modern wireless network can be neither successfully deployed nor successfully expanded without proper planning. In this paper we consider the wireless mesh network (WMN) planning problem where not much work has been done. We propose a more realistic multi-objective approach to model this problem where the two conflicting objectives of total deployment cost and network throughput are to be optimized while guaranteeing full coverage to all mesh clients. Previous contributions have mainly formulated and solved this problem by using single-objective integer linear programming formulations and exact methods. The main limitation of these approaches resides in their restriction to small sized instances. We propose a population-based meta-heuristic algorithm to solve the problem. This algorithm produces a set of good planning solutions for real-size networks thus enlarging the decision perspective of a network planner. We also discuss the effect of different parameters on the characteristics of the solutions.
Vesicular Glutamate Transporter 3 Is Required for Synaptic Transmission in Zebrafish Hair Cells

Hair cells detect sound and movement and transmit this information via specialized ribbon synapses. Here we report that asteroid, a gene identified in an ethylnitrosourea mutagenesis screen of zebrafish larvae for auditory/vestibular mutants, encodes vesicular glutamate transporter 3 (Vglut3). A splice site mutation in exon 2 of vglut3 results in a severe truncation of the predicted protein product and morpholinos directed against the vglut3 ATG start site or the affected splice junction replicate the asteroid phenotype. In situ hybridization shows that vglut3 is exclusively expressed in hair cells of the ear and lateral line organ. A second transporter gene, vglut1, is also expressed in zebrafish hair cells, but the level of vglut1 mRNA is not increased in the absence of Vglut3. Antibodies against Vglut3 label the basal end of hair cells and labeling is not present in asteroid/vglut3 mutants. Based on the localization of Vglut3 in hair cells, we suspected that the lack of vestibulo-ocular and acoustic startle reflexes in asteroid/vglut3 mutants was attributable to a defect in synaptic transmission in hair cells. In support of this notion, action currents in postsynaptic acousticolateralis neurons are absent in asteroid/vglut3 mutants. At the ultrastructural level, mutant asteroid/vglut3 hair cells show a decrease in the number of ribbon-associated synaptic vesicles, indicating a role for Vglut3 in synaptic vesicle biogenesis and/or tethering to the ribbon body. Lack of postsynaptic action currents in the mutants suggests that the remaining hair-cell synaptic vesicles contain insufficient levels of glutamate for generation of action potentials in first-order neurons.
Frequent Items Computation over Uncertain Wireless Sensor Network

There is an increasing interest in uncertain and probabilistics databases arising in application domains such as sensor networks, information retrieval, mobile object data management, information extraction, and data integration. A range of different approaches have been proposed to find the frequent items in uncertain database. But there is little work on processing such query in distributed, in-network inference, such as sensor network. In sensor network, communication is the primary problem because of limited batteries. In this paper, a synopsis with minimum amount tuples is proposed, which sufficient for answering the top-k query. And this synopsis can be dynamic maintained with new tuples been added. A novel communication efficient algorithm is presented in taking advantage of this synopsis. The test results confirm the effectiveness and efficiency of our approaches.
Efficient privacy preserving ranked search over encrypted data

Cloud computing and its ever so increasing prominence has rendered it as an unavoidable component for data storage and other data services. The security challenges of storing sensitive data on the cloud is reduced to an extent by the Encryption of data, though in the process of encrypted data search, efficiency is compromised. The encrypted data on the cloud can be retrieved using Searchable Symmetric Encryption (SSE). The current work uses multi-keyword searchable encryption scheme with top-k retrieval to avoid compromises on data privacy occurred by using Order Preserving Encryption schemes. The encryption scheme uses homomorphic encryption and vector space model. The vector space model provides the required search accuracy. The homomorphic encryption allows majority of the computation to be done at the server side while concealing the sensitive data. The user alone can identify the final result of the relevance calculation and request for the actual file. In this paper, phrase searching is included to improve the search results on the encrypted data. To accomplish this we maintain a list of the keyword locations in the encrypted file index. The cloud server, which we assume to be honest-but-curious, operates on these encrypted values and identifies if the words occur in close proximity without knowing the actual locations of these words and the words itself.
Microscopy of myelination

We present a mini review of the microscopical techniques that may be used to investigate the process of myelination in the tissues of the mammalian central nervous system. The biological background to myelination, the process by which neuronal axons are electrically insulated to achieve higher conduction velocities is outlined, including an introduction to neurons and glia, some background discussion of the purpose of myelination in the mammalian nervous system and an outline of our current understanding of the composition and development of the myelin sheath itself. Specific focus will fall on the interaction of oligodendrocytes with the axons of the central nervous system and the unanswered questions surrounding the formation of the myelin sheath by these cells. The main body of this chapter reviews the various microscopical techniques that have been employed to image the myelination process. Examples drawn from the literature and recent results from our lab have been used to illustrate the current state of the art. We consider the use of light microscopy including the advantages of phase contrast imaging, scanning near field optical microscopy (SNOM) and immuno0fluorescencence methods. Results from transmission and scanning electron microscopy studies of myelination are also outlined and discussed together with data from the atomic force microscope (AFM). Finally we turn to recent results involving the application of the environmental scanning electron microscope (ESEM) in correlative fluorescence microscopy studies of oligodendrocyte co0cultures. This chapter concludes with the authors' thoughts on remaining challenges facing instrument developers and users.
Violent Cognitions: Do Violent Offenders Express Evaluations, Norms, and Mitigations of Responsibility for Violence?

Past research has suggested that the term attitudes has often been used as an umbrella term for a variety of cognitive constructs, including evaluations, norms, and mitigations. In order to better understand the nature of these cognitions, I identified statements from interviews with 44 violent offenders that seemed to reflect definitions for each cognition, and quantitized these statements according to their support or aversion to the use of violence. Inter-rater reliability analyses suggested that a second coder and I reliably identified each of these cognitions in ten randomly selected interviews, and that we reliably coded the valence of statements reflecting evaluations and norms. Correlations between these cognitive statements and two indices of violence were examined, and evaluations were associated with prior convictions for violence. These results suggest that theoretical conceptualizations of evaluations, norms, and mitigations correspond to how offenders talk about violence, and that evaluative statements were associated with violence.
Adaptive clustering and CSI acquisition for FDD massive MIMO systems with two-level precoding

In this paper, we target a massive multiple-input/multiple-output (MIMO) system operating in frequency-division duplexing (FDD) mode, assuming the adoption of a two-level linear precoding strategy at the BS. We propose a novel strategy to effectively acquire the channel state information (CSI) at the base station (BS). In particular, we devise a cross-layer dynamic algorithm for user grouping, CSI acquisition and user scheduling that takes into account fairness considerations, application characteristics and quality of service (QoS) constraints of the users. We assess the merit of the proposed algorithm for a proportional fairness objective by comparing its performance with what is achieved by a relevant baseline algorithm in which user grouping is static and based only on the second order statistics, i.e., joint space division and multiplexing (JSDM). Our numerical findings illustrate that the proposed algorithm outperforms the baseline in terms of both fairness and speed of convergence to a steady state, and for different network topologies.
Sparse 2 D Fast Fourier Transform

This paper extends the concepts of the Sparse Fast Fourier Transform (sFFT) Algorithm introduced in [1] to work with two dimensional (2D) data. The 2D algorithm requires several generalizations to multiple key concepts of the 1D sparse Fourier transform algorithm. Furthermore, several parameters needed in the algorithm are optimized for the reconstruction of sparse image spectra. This paper addresses the case of the exact k-sparse Fourier transform but the underlying concepts can be applied to the general case of finding a k-sparse approximation of the Fourier transform of an arbitrary signal. The proposed algorithm can further be extended to even higher dimensions. Simulations illustrate the efficiency and accuracy of the proposed algorithm when applied to real images.
NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function

NNScore is a neural-network-based scoring function designed to aid the computational identification of small-molecule ligands. While the test cases included in the original NNScore article demonstrated the utility of the program, the application examples were limited. The purpose of the current work is to further confirm that neural-network scoring functions are effective, even when compared to the scoring functions of state-of-the-art docking programs, such as AutoDock, the most commonly cited program, and AutoDock Vina, thought to be two orders of magnitude faster. Aside from providing additional validation of the original NNScore function, we here present a second neural-network scoring function, NNScore 2.0. NNScore 2.0 considers many more binding characteristics when predicting affinity than does the original NNScore. The network output of NNScore 2.0 also differs from that of NNScore 1.0; rather than a binary classification of ligand potency, NNScore 2.0 provides a single estimate of the pKd. To facilitate use, NNScore 2.0 has been implemented as an open-source python script. A copy can be obtained from http://www.nbcr. net/software/nnscore/.
TRPM8-Dependent Dynamic Response in a Mathematical Model of Cold Thermoreceptor

Cold-sensitive nerve terminals (CSNTs) encode steady temperatures with regular, rhythmic temperature-dependent firing patterns that range from irregular tonic firing to regular bursting (static response). During abrupt temperature changes, CSNTs show a dynamic response, transiently increasing their firing frequency as temperature decreases and silencing when the temperature increases (dynamic response). To date, mathematical models that simulate the static response are based on two depolarizing/repolarizing pairs of membrane ionic conductance (slow and fast kinetics). However, these models fail to reproduce the dynamic response of CSNTs to rapid changes in temperature and notoriously they lack a specific cold-activated conductance such as the TRPM8 channel. We developed a model that includes TRPM8 as a temperature-dependent conductance with a calcium-dependent desensitization. We show by computer simulations that it appropriately reproduces the dynamic response of CSNTs from mouse cornea, while preserving their static response behavior. In this model, the TRPM8 conductance is essential to display a dynamic response. In agreement with experimental results, TRPM8 is also needed for the ongoing activity in the absence of stimulus (i.e. neutral skin temperature). Free parameters of the model were adjusted by an evolutionary optimization algorithm, allowing us to find different solutions. We present a family of possible parameters that reproduce the behavior of CSNTs under different temperature protocols. The detection of temperature gradients is associated to a homeostatic mechanism supported by the calcium-dependent desensitization.
An Improved JPEG Compression Scheme Using Human Visual System Model

A perceptual image coder for the compression of monochrome images is presented here, in which the coding structure is coupled with a vision model to produce coded images with an improved visual quality at low bit-rates. The coder is an improvement on the Joint Photographic Experts Group (JPEG -Discrete Cosine Transform (DCT) based) image compression standard, and the structure can be easily extended to implement as an improvement on the new JPEG standard. The proposed coding structure incorporates the human vision model in all stages of compression, and gives very good results compared to the existing JPEG standard. Though the mathematical model used are not new, the simple structure of it which is similar to the coding structure of the JPEG, incorporates the visual processing stages in a very systematic manner in the same way as the visual neurons process the signal. The results presented in this paper reveal that the proposed structure gives very good perceptual quality compared to the JPEG scheme especially as we go for lower bit rates. One of the major advantages of the proposed scheme is that it can be easily extended to a structure in which the rate control optimization can be incorporated, as in the new JPEG scheme.
Dropped pronoun generation for dialogue machine translation

Dropped pronoun (DP) is a common problem in dialogue machine translation, in which pronouns are frequently dropped in the source sentence and thus are missing in its translation. In response to this problem, we propose a novel approach to improve the translation of DPs for dialogue machine translation. Firstly, we build a training data for DP generation, in which the DPs are automatically added according to the alignment information from a parallel corpus. Then we model the DP generation problem as a sequence labelling task, and develop a generation model based on recurrent neural networks and language models. Finally, we apply the DP generator to machine translation task by completing the source sentences with the missing pronouns. Experimental results show that our approach achieves a significant improvement of 1.7 BLEU points by recalling possible DPs in the source sentences.
Depression Impairs Learning Whereas Anticholinergics Impair Transfer Generalization in Parkinson Patients Tested on Dopaminergic Medications

In a study of acquired equivalence in Parkinson disease (PD), in which patients were tested on normal dopaminergic medication, we found that comorbid clinical depression impairs initial acquisition, whereas the use of anticholinergic therapy impairs subsequent transfer generalization. In addition, this study provides a replication of the basic finding of Myers et al (2003) that patients with PD on dopaminergic therapy are impaired at initial acquisition, but normal at subsequent transfer generalization, generalizing these results to an Arabic-speaking population including many participants with no formal education. These results are consistent with our past computational modeling, which argues that acquisition of incrementally acquired, feedback-based learning tasks is dependent on cortico-striatal circuits, whereas transfer generalization is dependent on medial temporal (MT) structures. They are also consistent with prior computational modeling, and with empiric work in humans and animals, suggesting that anticholinergic drugs may particularly impair cognitive abilities that depend on the MT lobe.
The three-dimensional XY universality class: a high precision Monte Carlo estimate of the universal amplitude ratio A+/A−

We simulate the improved three-dimensional two-component ϕ4 model on the simple cubic lattice in the low and the high temperature phase for reduced temperatures down to |T − Tc|/Tc ≈ 0.0017 on lattices of size up to 3503. Our new results for the internal energy and the specific heat are combined with the accurate estimates of βc and data for the internal energy and the specific heat at βc recently obtained in Campostrini et al (2006 Preprint cond-mat/0605083). We find Rα = (1 − A+/A−)/α = 4.01(5), where α is the critical exponent of the specific heat and A ± is the amplitude of the specific heat in the high and the low temperature phase, respectively.
Predicting Students' Outcome by Interaction Monitoring

In this paper we propose to predict the students’ outcome by analyzing the interactions that happen in class during the course. PresenceClick lets teachers and students register their interactions during learning sessions in an agile way to give feedback in return about the students’ learning progress by means of visualizations. Some of the registered interactions are the students who are attending class and a subset of the students’ emotions felt during learning sessions. We have found correlations among attendance, emotions and performance in the final exam. This paper presents the study carried out to build a prediction model for the students’ mark in the final exam based on these interactions. The purpose is to advice teachers about students in risk to fail.
On the convergence of FOCUSS algorithm for sparse representation

FOCal Underdetermined System Solver (FOCUSS) is a powerful tool for sparse representation and underdetermined inverse problems, which is extremely easy to implement. In this paper, we provide a comprehensive convergence analysis on the FOCUSS algorithm towards establishing a systematic convergence theory for it. First, we give a rigorous derivation for this algorithm exploiting the auxiliary function. Then, we prove its convergence. In particular, we systematically analyze its convergence rate for different sparsity parameter p and demonstrate its convergence rate by numerical experiments.
Real-Time Identification of Users under the New Structure of Skype

It is of great significance for network operators and researchers to accurately identify the Skype flows. However, traditional port-based or payload- based flow classification methods can not be used on Skype because of its encryption technology. In this paper, we analyse the network structure and operating mechanism of Skype, and find that they have great changes after being acquired by Microsoft. Based on the analysis result, we extract the characteristics that can be used to identify Skype online users and design a method to detect the Skype users in real time. We implement our method with an open source software and evaluate it under the actual network environment. The results show that our method can identify Skype users quickly and accurately on the Gbps link.