Miracle-dz/UniFAR-FTUs huggingface dataset:
Gabor-based needle detection and tracking in three-dimensional ultrasound data volumes

During needle interventions for e.g. regional anaesthesia or biopsy, it is very important to visualize the needle and its tip with respect to important structures in the body. In this work, we propose a novel image-based needle detection technique in a 3D ultrasound volume dataset, which can improve the intervention. We present a novel application of the 3D Gabor transformation, which exploits needle-like structures with appropriate designs. Furthermore, we introduce a needle tracking algorithm based on Gradient Descent and show that it limits the computational complexity and detection error. Finally, we visualize the needle on 2D cross-sections of the volume in order to be presented to the physician. Evaluation of our system in challenging cases shows a high detection score (up to 100% but needs larger sets) and accurate visualization.
Inhibition of Spinal Interlukin-33/ST2 Signaling and Downstream ERK and JNK Pathways in Electroacupuncture Analgesia in Formalin Mice

Although acupuncture is widely used to manage pain, it remains highly controversial, largely due to the lack of a clear mechanism for its benefits. Here, we investigated the role of IL-33, a novel interleukin (IL)-1 family member, and its receptor ST2 in the analgesic effects of electroacupuncture (EA) on formalin-induced inflammatory pain. The results showed that 1) EA stimulation of ipsilateral Zusanli (ST 36) and Yanglingquan (GB 34) acupoints for 30 min remarkably suppressed the two phases of formalin-induced spontaneous pain; 2) subcutaneous or intrathecal administration of recombinant IL-33 (rIL-33) significantly inhibited the analgesic effect of EA, whereas the ST2 antibody potentiated EA analgesia in formalin mice; 3) EA treatment decreased the up-regulation of IL-33 and ST2 protein following formalin injection; and 4) the suppression of the formalin-induced expression of spinal phosphorylated ERK and JNK induced by EA treatment was significantly attenuated following subcutaneous rIL-33 delivery, and was further decreased by the ST2 antibody. These data suggest that EA alleviates formalin-induced inflammatory pain, at least partially, by inhibiting of spinal IL-33/ST2 signaling and the downstream ERK and JNK pathways.
Static and Dynamic Scoring by Web Page Grouping

Web Search System exists to retrieve necessary information on the WWW space. However, these are not accuracy enough. Then, we propose the technique for using Web Page Grouping together with the link structure analysis, and aim at the improvement in accuracy. Our proposal is composed of four techniques. The first technique is Web Page Grouping to enhance it related to adjacent the link structure. The second technique is static scoring by link structure analysis using Web Page Grouping. The third technique is dynamic scoring which tempers the link structure analysis of the retrieval result set with Web Page Grouping. And, the fourth technique is a ranking technique for annexing a static score and a dynamic score. This paper describes these techniques, and reports on the experiment results.
Pitch Estimation in Noisy Speech Using Accumulated Peak Spectrum and Sparse Estimation Technique

Pitch estimation from acoustic signals is a fundamental problem in many areas of speech research. For noise-corrupted speech, reliable pitch estimation is difficult. This paper presents a study of pitch estimation in noisy speech based on robust temporal-spectral representation and sparse reconstruction. We propose to accumulate spectral peaks over consecutive time frames. Since harmonic structure of speech changes much more slowly than noise spectrum, spectral peaks related to pitch harmonics would stand out over the noise through the accumulation. Experimental results show that the accumulated peak spectrum is indeed a robust representation of pitch harmonics. Subsequently, the accumulated peak spectrum is expressed as a sparse linear combination of a large set of clean peak spectrum exemplars. Gaussian mixture density is used to model noise spectrum peaks. The weights of the linear combination are estimated so as to maximize the likelihood of the accumulated peak spectrum under sparsity constraint. Robust pitch estimation is done based on the sparse weights and the corresponding peak spectrum exemplars. The use of Gaussian mixture model leads to non-convexity of the objective function for sparse weight estimation. By approximation and reformulation, two convex optimization approaches are developed to estimate the weights. Extensive experimental studies are carried out to evaluate performance of the proposed pitch estimation algorithms on a wide variety of noise conditions. It is clearly shown that the proposed methods significantly and consistently outperform the conventional methods, particularly at very low signal-to-noise ratios (e.g., SNR <; -5 dB).
Personalized Retrieval and Browsing of Classical Music and Supporting Multimedia Material

This paper reports on three demonstrators developed to provide personalized, enhanced experiences of classical music, within the EU FP7 project "Performances as Highly Enriched aNd Interactive Concert eXperiences" (PHENICX): (i) an interface for accessing supplemental multimodal material about music items, (ii) a recommender system for visualizations of classical music, and (iii) a recommender system for tagging. The personalization in all three demos is achieved through modeling users' personality and musical sophistication. The links to the web interfaces are provided as well as the outcomes of quantitative and qualitative evaluations.
An Efficient Automatic Iris Image Acquisition and Preprocessing System

An efficient technique on iris image acquisition, iris de-nosing, iris localization, and quality assessment is proposed in this paper. An automatic focusing system based on a decision function is introduced into the iris acquisition device to achieve the feedback control, which can capture the high resolution iris image with real-time. Iris localization differs from previous iris localization schemes in that it combines iris acquisition with edge detection technique. On the basis of coarse detection, the iris accurate detection is based on the combination of wavelet-based least square method, Laplacian of Gaussian function and an improved Hough transform. Experimental results show that the technique has a good performance, which can not only capture high quality iris image, but also improve the speed and accuracy or iris localization
Akt1 Intramitochondrial Cycling Is a Crucial Step in the Redox Modulation of Cell Cycle Progression

Akt is a serine/threonine kinase involved in cell proliferation, apoptosis, and glucose metabolism. Akt is differentially activated by growth factors and oxidative stress by sequential phosphorylation of Ser473 by mTORC2 and Thr308 by PDK1. On these bases, we investigated the mechanistic connection of H2O2 yield, mitochondrial activation of Akt1 and cell cycle progression in NIH/3T3 cell line with confocal microscopy, in vivo imaging, and directed mutagenesis. We demonstrate that modulation by H2O2 entails the entrance of cytosolic P-Akt1 Ser473 to mitochondria, where it is further phosphorylated at Thr308 by constitutive PDK1. Phosphorylation of Thr308 in mitochondria determines Akt1 passage to nuclei and triggers genomic post-translational mechanisms for cell proliferation. At high H2O2, Akt1-PDK1 association is disrupted and P-Akt1 Ser473 accumulates in mitochondria in detriment to nuclear translocation; accordingly, Akt1 T308A is retained in mitochondria. Low Akt1 activity increases cytochrome c release to cytosol leading to apoptosis. As assessed by mass spectra, differential H2O2 effects on Akt1-PDK interaction depend on the selective oxidation of Cys310 to sulfenic or cysteic acids. These results indicate that Akt1 intramitochondrial-cycling is central for redox modulation of cell fate.
Closed-form BER results for MRC diversity with channel estimation errors in Ricean fading channels

Closed-form expressions for computing the average bit error rate (BER) for a class of modulation schemes in Ricean flat fading channels with maximal-ratio combining diversity and channel estimation errors are derived. The results are valid for a general multichannel model that includes Ricean and Rayleigh fading channels. The results are applicable to channel estimators that are jointly Gaussian with the channel gain. The BER performance of pilot symbol-assisted modulation is studied. The effects of estimation error on the diversity system are examined as well as the influences of varying the Ricean factor K and changing the number of diversity branches L.
Expression of molecular markers in mediastinal nodes from resected stage I non-small-cell lung cancer (NSCLC): prognostic impact and potential role as markers of occult micrometastases.

BACKGROUND Occult lymph node (LN) metastases are clinically relevant and confer a worse prognosis in non-small-cell lung cancer (NSCLC) patients. Current staging methods are unable to identify patients with poor outcome. Their detection requires both a more sensitive and specific technique. We aimed to assess the role of messenger RNA expression in pathologically negative LNs (pN0) of stage I NSCLC patients as markers of occult micrometastases and to correlate the results with local or distant tumor recurrence and survival. PATIENTS AND METHODS Potential molecular markers were evaluated in 344 LNs and 38 tumors by quantitative real-time RT-PCR. Only CEACAM5 and PLUNC showed high expression in lung tumor tissue and null expression in RNA from benign LNs. RESULTS Thirteen per cent of the LNs were positive for CEACAM5 and 16% for PLUNC. Eight of 38 NSCLC patients had positive expression in pN2 nodes by CEACAM5 and/or PLUNC and disease-free survival (P=0.028) and overall survival time was significantly worse in these patients compared with those with negative expression (P=0.0083). CONCLUSIONS Quantitative real-time RT-PCR of CEACAM5 and PLUNC can estimate the presence of micrometastatic cells in LNs with greater precision than current staging method used for assessing tumor recurrence risk.
SDN enabled resiliency in LTE assisted small cell mmWave backhaul networks

As mobile data usage increases dramatically, new architectures and technologies for wireless communication are required. Next generation of mobile networks are expected to be augmented by a massive amount of small cells that will be densely deployed. In order to connect the small cells, new high capacity wireless backhauling technologies are required. A promising solution is to use frequencies in the mmWave band, which allows for much greater capacity due to the massive amount of free spectrum. However, the special characteristics of the mmWave bands such as high path loss and atmospheric absorption lead to unstable links. In this paper, we investigate using Software Defined Networking principles for the operation and control of wireless backhaul networks. We demonstrate how SDN resiliency mechanisms can be used to mitigate disruptive connectivity due to mmWave links frequently failing. For assisting the small cell backhauling, we propose to also use the LTE uplinks of the small cells as backup links, should the mmWave mesh forwarding link break. Our experiments using a network emulator show that using SDN-based local repair mechanisms can significantly reduce the packet loss rate inside the mmWave backhaul mesh, which can be further reduced with an LTE assisted Failover.
266 S . NirmalRaj 3 SPIHT _ Algorithm ( ) Step-1 : Initialization

Image compression is a necessary technique for image transmission in a channel. In this paper, it is aimed to improve efficiency and dealing with the performance evaluation of image compression techniques for various kinds of images from uncontrolled environments. The key special of this paper is, the image compression is carried out using wavelet based image compression and decomposition techniques. SPIHT based image compression is proposed for achieving better image compression in high compression ratio. The simulation results of SPIHT technique are compared with the various existing compression techniques such as VD, DCT and DWT. All the technique used in this paper are implemented in MATLAB software and the performance is compared using the parameters such as PSNR, MSE, CR and compressed size.
Strategies for the Evaluation of Ontology Learning

An important aspect of ontology learning is a proper evaluation. Generally, one can distinguish between two scenarios: (i) quality assurance during an ontology engineering project in which also ontology learning techniques may be used and (ii) evaluating and comparing ontology learning algorithms in the laboratory during their development. This paper gives an overview of different evaluation approaches and matches them against the requirements of the scenarios. It will be shown that different evaluation approaches have to be applied depending on the scenario. Special attention will be paid to the second scenario and the gold standard based evaluation of ontology learning for which concrete measures for the lexical and taxonomic layer will be presented.
Costimulation of AMPA and Metabotropic Glutamate Receptors Underlies Phospholipase C Activation by Glutamate in Hippocampus

Glutamate, a major neurotransmitter in the brain, activates ionotropic and metabotropic glutamate receptors (iGluRs and mGluRs, respectively). The two types of glutamate receptors interact with each other, as exemplified by the modulation of iGluRs by mGluRs. However, the other way of interaction (i.e., modulation of mGluRs by iGluRs) has not received much attention. In this study, we found that group I mGluR-specific agonist (RS)-3,5-dihydroxyphenylglycine (DHPG) alone is not sufficient to activate phospholipase C (PLC) in rat hippocampus, while glutamate robustly activates PLC. These results suggested that additional mechanisms provided by iGluRs are involved in group I mGluR-mediated PLC activation. A series of experiments demonstrated that glutamate-induced PLC activation is mediated by mGluR5 and is facilitated by local Ca2+ signals that are induced by AMPA-mediated depolarization and L-type Ca2+ channel activation. Finally, we found that PLC and L-type Ca2+ channels are involved in hippocampal mGluR-dependent long-term depression (mGluR-LTD) induced by paired-pulse low-frequency stimulation, but not in DHPG-induced chemical LTD. Together, we propose that AMPA receptors initiate Ca2+ influx via the L-type Ca2+ channels that facilitate mGluR5-PLC signaling cascades, which underlie mGluR-LTD in rat hippocampus.
CLSI: A Flexible Approximation Scheme from Clustered Term-Document Matrices

Abstract We investigate a methodology for matrix approximation andIR. A central feature of these techniques is an initial clus-tering phase on the columns of the term-document matrix,followed by partial SVD on the columns constituting eachcluster. The extracted information is used to build effectivelow rank approximations to the original matrix as well as forIR. The algorithms can be expressed by means of rank reduc-tion formulas. Experiments indicate that these methods canachieve good overall performance for matrix approximationand IR and compete well with existing schemes. Keywords : Low rank approximations, Clustering, LSI. 1 Introduction and motivation The purpose of this paper is to outline aspects of a frame-work for matrix approximation and its application in LSI 1 . This framework is designed in the context of the vector spacemodel [18], where a collection of n documents is repre-sented by a term-document matrix (abbreviated as tdm) ofncolumns and mrows, where mis the number of terms (orphrases) used to index the collection. Each element ﬁ
Effect of intersubband scattering on weak localization in two-dimensional systems

The theory of weak localization is generalized for multilevel 2D systems taking into account intersubband scattering. It is shown that weak intersubband scattering which is negligible in a classical transport, affects strongly the weak-localization correction to conductivity. The anomalous magnetoresistance is calculated in the whole range of classically low magnetic fields. This correction to conductivity is shown to depend strongly on the ratios of occupied level concentrations. It is demonstrated that at relatively low population of the excited subband, it is necessary to use the present theory because the high-field limit asimptotics is shown to be achieved only in classical magnetic fields.
Robust stability and a criss‐cross algorithm for pseudospectra

x = Ax is robustly stable when all eigenvalues of complex matrices within a given distance of the square matrix A lie in the left half-plane. The ‘pseudospectral abscissa’, which is the largest real part of such an eigenvalue, measures the robust stability of A . W epresent an algorithm for computing the pseudospectral abscissa, prove global and local quadratic convergence, and discuss numerical implementation. As with analogous methods for calculating H∞ norms, our algorithm depends on computing the eigenvalues of associated Hamiltonian matrices.
Extracting Temporal Information from Open Domain Text: A Comparative Exploration

The utility of data-driven techniques in the end-to-end problem of temporal information extraction is unclear. Recognition of temporal expressions yields readily to machine learning, but normalization seems to call for a rule-based approach. We explore two aspects of the (potential) utility of data-driven methods in the temporal information extraction task. First, we look at whether improving recognition beyond the rule base used by a normalizer has an eect on normalization performance, comparing normalizer performance when fed by several recognition systems. We also perform an error analysis of our normalizer’s performance to uncover aspects of the normalization task that might be amenable to data-driven techniques.
Speaker Recognition Based on Weighted Mel-cepstrum

The key point of speaker recognition is to extract the unique, effective, stable and reliable features that can stand for the personality of the speaker from the speech signal. This paper applied the psychologically weighted technology in mel-cepstrum analysis and adopted the Signal-to-Mask Ratios (SMRS) obtained from the psychoacoustic model as the weighting function to obtain the weighted mel-cepstrum coefficients (WMCEP) as features in speaker recognition. Experiments showed that the WMCEP not only described the speaker’s formant much better than MFCC and MCEP, but also had robustness to some extent for speaker recognition.
Experimental comparison of AdaBoost algorithms applied on leg detection with different range sensor setups

When tracking people or other moving objects with a mobile robot, detection is the first and most critical step. At first most researchers focused on the tracking algorithms, but recently AdaBoost (supervised machine learning technique) was used for people legs detection in 2D range data. The results are promising, but it is unclear if the obtained classifier could be used on the data from another sensor. As it would be a huge inconvenience having to train a classifier for every sensor (setup), we set out to find if, and when is a classifier trained on one sensor setup transferable to another sensor setup. We tested two sensors in five different setups. In total, we acquired 2455 range scans. Experiments showed that the classifier trained on noisier sensor data performed better at classification of data coming from other sensor setups. Classifiers trained on less noisy data were shown to be overconfident, and performed poorly on noisy data. Furthermore, experiments showed that classifiers learned on ten times smaller datasets performed as good as classifiers trained on larger datasets. Since AdaBoost is a supervised learning technique, obtaining same classifier efficiency with significantly smaller dataset means less hand labeling of the data for the same results.
Money Laundering Crime and Its Situational Prevention in Iranian Law and International Law

Crime prevention is crucial to social life which necessitates the need for conducting criminology studies to identify the causes of the crime. In this paper, we focus on money laundering crime. First we discuss about money laundering crime in Iran’s penal system, and after presenting its criminological characteristics (transnational, organized and victimless), we review Iran’s legal system and the international conventions about this crime. Then, since major situational prevention measures against money laundering are related to the banks and financial institutions, we proposed some measures for financial institutions such as staff training, adjusting banking secrecy laws, monitoring money transfer, reporting large cash transactions, and reporting suspicious transactions. Results showed that Iran’s anti money laundering laws are in accordance with Merida convention, for example, in terms of identification, record-keeping and the reporting, but do not obligate the identification of customer when there is criminal evidence.
Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation

Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions. We apply our algorithm to the challenging 3D segmentation problem of reconstructing neuronal processes from volumetric electron microscopy (EM) and show that we are able to learn a good affinity graph directly from the raw EM images. Further, we show that our affinity graph improves the segmentation accuracy of both simple and sophisticated graph partitioning algorithms. In contrast to previous work, we do not rely on prior knowledge in the form of hand-designed image features or image preprocessing. Thus, we expect our algorithm to generalize effectively to arbitrary image types.
Implementation of slotted-complementary split-ring resonators for Wimax applications in microstrip patch antennas

Reducing mutual coupling between elements of antennas are more attractive in the electromagnetic and antenna community. The complementary split-ringresonators (SRRs) are introduced to reduce the mutual coupling between two microstrip patch antennas that radiate in the same frequency band. The new unit cell consists of two complementary SRR. The adjustment in SRR improves the rejection response in terms of bandwidth and suppression. The filtering characteristics of the band-gap structure are investigated using dispersion analysis. This SRR implementation used for Wimax applications at a resonant frequency of 5.8GHz. The design simulation and optimization is done by ADS(Advanced Design System) software. The simulated return loss of this proposed antenna is 7dB. The proposed resonator structures are broadband and also they can be used to minimize coupling and co-channel interference in antennas.
Phytochemical Analysis of Eight Medicinal Plants from Amravati District ( MS ) India

The preliminary phytochemical analysis of eight medicinal plants from Amravati District (MS) was done. The plants were Abutilon indicum L . (Swart). , Euphrbia hirta L. , Ficus hispida L. f. , Melia azedarch L. , Phyllathus reticulatus Poir. ,Psidium guajava L. , vitex negundo L. , Vitex pinnata L. Qualitative phytochemical analysis of these plants confirms the presence of various phytochemicals like alkaloids, flavonoids, steroids and terpenoid. The presence of these phytochemicals can be correlated with medicinal potential of these plants.