Miracle-dz/UniFAR-FTUs huggingface dataset:
Face Recognition Algorithm Based on Multi-layer Weighted LBP

Since the basic LBP operator rigidly field point value of 1 and 0, there exist some error that different face images with the same LBP code value, a face recognition algorithm based on hierarchical weighted local binary pattern (MW-LBP) is proposed. Firstly, the original face image is divided into small blocks, comparing the neighbor pixel values and the center pixel values at each block, using multi-layer weighted LBP feature algorithm to extract histogram of each block respectively, and then keep the partitioned histogram cascade at all levels to form the hierarchy histogram, the every level histogram series into a general histogram vector. Finally, the classification is performed using a nearest neighbor classifier with Chi square as a dissimilarity measure. The proposed algorithm experiments on ORL and YALE face database, experimental results show that MW-LBP algorithm can obtain a better recognition rate and has good recognition effect when comparing with original LBP algorithm.
A primal-dual approximation algorithm for generalized steiner network problems

We present the first polynomial-time approximation algorithm for finding a minimum-cost subgraph having at least a specified number of edges in each cut. This class of problems includes, among others, the generalized Steiner network problem, also called the survivable network design problem. Ifk is the maximum cut requirement of the problem, our solution comes within a factor of 2k of optimal. Our algorithm is primal-dual and shows the importance of this technique in designing approximation algorithms.
Matrix Metalloproteinase-10/TIMP-2 Structure and Analyses Define Conserved Core Interactions and Diverse Exosite Interactions in MMP/TIMP Complexes

Matrix metalloproteinases (MMPs) play central roles in vertebrate tissue development, remodeling, and repair. The endogenous tissue inhibitors of metalloproteinases (TIMPs) regulate proteolytic activity by binding tightly to the MMP active site. While each of the four TIMPs can inhibit most MMPs, binding data reveal tremendous heterogeneity in affinities of different TIMP/MMP pairs, and the structural features that differentiate stronger from weaker complexes are poorly understood. Here we report the crystal structure of the comparatively weakly bound human MMP-10/TIMP-2 complex at 2.1 Å resolution. Comparison with previously reported structures of MMP-3/TIMP-1, MT1-MMP/TIMP-2, MMP-13/TIMP-2, and MMP-10/TIMP-1 complexes offers insights into the structural basis of binding selectivity. Our analyses identify a group of highly conserved contacts at the heart of MMP/TIMP complexes that define the conserved mechanism of inhibition, as well as a second category of diverse adventitious contacts at the periphery of the interfaces. The AB loop of the TIMP N-terminal domain and the contact loops of the TIMP C-terminal domain form highly variable peripheral contacts that can be considered as separate exosite interactions. In some complexes these exosite contacts are extensive, while in other complexes the AB loop or C-terminal domain contacts are greatly reduced and appear to contribute little to complex stability. Our data suggest that exosite interactions can enhance MMP/TIMP binding, although in the relatively weakly bound MMP-10/TIMP-2 complex they are not well optimized to do so. Formation of highly variable exosite interactions may provide a general mechanism by which TIMPs are fine-tuned for distinct regulatory roles in biology.
Firm Valuation and Default Probability Through Exotic (Barrier) Options

Real option theory allows using financial option models to value investments and firms. Traditional models present a substantial problem to positively correlate underlying asset volatility with firm value. Therefore, an alternative approach is used based on a particular type of exotic option, the barrier one. This dynamic model helps estimating default probability incorporating the negative impact that excessive risk has on firm value. The article presents a hypothetical example illustrating similarities and differences between the proposed model and the traditional version. Finally, this model is applied on two of the leading companies in the Argentinean capital market which are characterized by different levels of leverage demonstrating the robustness of the results. The predicted default probability increases with the rise in assets volatility and the time horizon of debt maturity.
Characterization and Estimation of Circuit Reliability Degradation under NBTI using On-Line IDDQ Measurement

Negative bias temperature instability (NBTI) in MOSFETs is one of the major reliability challenges in nano-scale technology. This paper presents an efficient technique to characterize and estimate the lifetime circuit reliability under NBTI degradation. Unlike conventional approaches, where a representative fMAX (maximum operating frequency) measurement from timing critical circuitry is used, we propose to utilize the standby circuit leakage IDDQ as a metric to detect and characterize temporal NBTI degradation in digital circuits. Compared to the fMAX based approach, the proposed IDDQ based technique benefits from lower test cost and improved capability of estimating reliability of complex circuitries such as ALUs and SRAM arrays. We have derived an analytical expression for circuit IDDQ from the analytical PMOS Vt degradation model (DeltaVt prop t1/6 ). The proposed model is verified with measurement data obtained from a test chip fabricated in 130 nm technology. Furthermore, we examine the possible applications of our proposed IDDQ based NBTI characterization. We show that the temporal degradation in static noise margin (SNM) of SRAM array and fMAX of random logic circuits are highly correlated to the IDDQ measurement, and this relationship can be used to predict long term circuit reliability.
Dynamics of Low-Threshold Spike Activation in Relay Neurons of the Cat Lateral Geniculate Nucleus

The low-threshold spike (LTS), generated by the transient Ca2+ current IT, plays a pivotal role in thalamic relay cell responsiveness and thus in the nature of the thalamic relay. By injecting depolarizing current ramps at various rates to manipulate the slope of membrane depolarization (dV/dt), we found that an LTS occurred only if dV/dt exceeded a minimum value of ∼5–12 mV/sec. We injected current ramps of variable dV/dt into relay cells that were sufficiently hyperpolarized to de-inactivateIT completely. Higher values of dV/dt activated an LTS. However, lower values of dV/dt eventually led to tonic firing without ever activating an LTS; apparently, the inactivation ofIT proceeded beforeIT could be recruited. Because the maximum rate of rise of the LTS decreased with slower activating ramps of injected current, we conclude that slower ramps allow increasing inactivation of IT before the threshold for its activation gating is reached, and when the injected ramps have a sufficiently low dV/dt, the inactivation is severe enough to prevent activation of an LTS. In the presence of Cs+, we found that even the lowest dV/dt that we applied led to LTS activation, apparently because Cs+ reduced the K+ “leak” conductance and increased neuronal input resistance. Nonetheless, under normal conditions, our data suggest that there is neither significant window current (related to the overlap of the inactivation and activation curves forIT ), rhythmogenic properties, nor bistability properties for these neurons. Our theoretical results using a minimal model of LTS excitability in these neurons are consistent with the experimental observations and support our conclusions. We suggest that inputs activating very slow EPSPs (i.e., via metabotropic receptors) may be able to inactivateIT without generating sizableIT and a spurious burst of action potentials to cortex.
Implementing a Prototype of the Security Framework for Distributed Brokering Systems

1.0 Introduction Security is an important element of any system design. Entities supported by a system need to securely interact with each other. The problem gets even more complex in the context of a distributed system, where the underling infrastructure of messaging nodes, which we call “brokers,” needs to carry these interactions securely. Furthermore, it is entirely possible that some brokers communicate with each other over communication channels that do not encrypt network traffic. Thus, even if two entities are connected to brokers, and they have secure communications channels with their brokers, when their communications traverse over insecure channels, the security of their interactions is compromised.
The design and implementation of panar maps in CGAL

Planar maps are fundamental structures in computational geometry. They are used to represent the subdivision of the plane into regions and have numerous applications. We describe the planar map package of CGAL--a Computational Geometry Algorithms Library. We discuss its modular design and implementation. In particular we introduce the two main classes of the design--planar maps and topological maps that enable the convenient separation between geometry and topology. The modular design is implemented using a generic programming approach. By switching a template parameter--the geometric traits class, one can use the same code for planar maps of different objects such as line segments or circular arcs. More flexibility is achieved by choosing a point location algorithm out of three implemented algorithms or plugging in an algorithm implemented by the user. The user of the planar maps package can benefit both from its flexibility and robustness. We present several examples of geometric traits classes and point location algorithms which demonstrate the possibility to adapt the general package to specific needs.
Overweight is associated with lower resting state functional connectivity in females after eliminating genetic effects: A twin study

Obesity is related to altered functional connectivity of resting state brain networks that are involved in reward and motivation. It is unknown to what extent these associations reflect genetic confounding and whether the obesity‐related connectivity changes are associated with differences in dietary intake. In this study, resting state functional MRI was performed after an overnight fast in 16 female monozygotic twin pairs (aged 48.8 ± 9.8 years) with a mean BMI discordance of 3.96 ± 2.1 kg/m2 (range 0.7–8.2). Functional connectivity of the salience, basal ganglia, default mode and anterior cingulate–orbitofrontal cortex networks was examined by independent component analysis. Dietary intake was assessed using 3‐day 24‐hour recalls. Results revealed that within the basal ganglia network, heavier versus leaner co‐twins have decreased functional connectivity strength in bilateral putamen (P < 0.05, FWE‐corrected). There were no differences in connectivity in the other networks examined. In the overall group, lower functional connectivity strength in the left putamen was correlated with higher intake of total fat (P < 0.01). It was concluded that, after eliminating genetic effects, overweight is associated with lower resting state functional connectivity in bilateral putamen in the basal ganglia network. The association between lower putamen connectivity and higher fat intake suggests an important role of the putamen in appetitive mechanisms. The cross‐sectional nature of our study cannot discriminate cause and consequence, but the findings are compatible with an effect of lower putamen connectivity on increased BMI and associated higher fat intake. Hum Brain Mapp 38:5069–5081, 2017. © 2017 Wiley Periodicals, Inc.
New motor design with conductive micro fiber shaft grounding ring prevents bearing failure in PWE inverter driven motors

Variable frequency drives induce shaft voltages onto the shaft of the driven motor because of the extremely high speed switching of the insulated gate bipolar transistors which produce the pulse width modulation used to control AC motors in commercial industrial and even consumer applications. This voltage induced on the shaft becomes great enough to overcome the dielectric of the oil film in the bearing causing bearing discharges known as electrical discharge machining effect. Unless mitigation for these shaft voltages is employed in the motor, the motor's bearings may become damaged from the electrical bearing currents which cause pitting and excessive bearing noise, fluting and finally motor failure. The cause of this problem and commonly applied mitigation methods are discussed as well as a new and highly effective conductive micro fiber shaft grounding ring technology which resolves these problems.
Rapid Genotyping of MSTN Gene Polymorphism Using High-resolution Melting for Association Study in Rabbits

The myostatin (MSTN) gene, as a negative regulator of skeletal muscle growth, has been proposed to be associated with production traits in farm animals. In the present study, a T/C variant at −125 bp (relative to ATG start codon) of 5’regulatory region of rabbit MSTN was identified by direct sequencing. Two hundred and twenty two rabbits, which were randomly sampled from 3 breeds (Ira rabbits, Champagne rabbits and Tianfu black rabbits), were genotyped by high-resolution melting (HRM). Comparing the genotyping results of 47 samples with direct sequencing, the HRM showed high sensitivity (0.96) and high specificity (0.98). In the three rabbit breeds, the allele C was the predominant allele. The polymorphic site showed high heterozygosity (He = 0.48) and high effective number of alleles (Ne = 1.91). The genetic diversity was reasonably informative (0.25<PIC<0.50). The association analysis showed that the genotype TC had significant effect on the 84-d-weight of rabbits compared with genotype CC (p = 0.047). In contrast, the genotypes had no significant effect on other production traits. These results showed that HRM could be effectively used for genotyping analysis of MSTN gene. The T/C variant in 5’regulatory region of MSTN might be one of the candidate SNP loci affecting the trait of 84-d-weight.
Loss and Double-edge-triggered Detector for Robust Small-footprint Keyword Spotting

Keyword spotting (KWS) system constitutes a critical component of human-computer interfaces, which detects the specific keyword from a continuous stream of audio. The goal of KWS is providing a high detection accuracy at a low false alarm rate while having small memory and computation requirements. The DNN-based KWS system faces a large class imbalance during training because the amount of data available for the keyword is usually much less than the background speech, which overwhelms training and leads to a degenerate model. In this paper, we explore the focal loss for the training of a small-footprint KWS system. It can automatically down-weight the contribution of easy samples during training and focus the model on hard samples, which naturally solves the class imbalance and allows us to efficiently utilize all data available. Furthermore, many keywords of Chinese conversational assistants are repeated words due to the idiomatic usage, such as ‘XIAO DU XIAO DU’. We propose a double-edge-triggered detecting method for the repeated keyword, which significantly reduces the false alarm rate relative to the single threshold method. Systematic experiments demonstrate significant further improvements compared to the baseline system.
Sudden Asphyxia Due to a Laryngeal Lipoma Following Esophageal Endosonography

Esophageal endosonography is nowwidely used, especially for staging esophageal carcinoma. It is currently recognised a routine procedure with few complications. We report the case of a 74-year-old patient who experienced a novel complication of esophageal endosonography. During endoscopic monitoring of an esophageal adenocarcinoma (32–36 cm from the incisors) treated by chemoradiotherapy, an odd bulge was discovered in the upper part of the esophagus (20–25 cm from incisors). Biopsy findings remained negative. Endosonography showed an hyperechogenic and homogeneous lesion, on the left part of the esophagus, 17–21 cm from the incisors, i. e. , in a different location from that previously measured (Figure 1). The diameter and thickness of the lesion were, respectively 26mm and 11mm. The submucosa and muscularis mucosa were intact and no evidence of recurrence of carcinoma in the lower part of the esophagus was noted.
Driving User Interfaces from FaCT

We describe a mechanism that can be used to drive interfaces from a description logic (DL) model of the domain. A simple layer with limited expressivity sits on top of the DL, with the interface behaviour described using a collection of application specific assertions. DL reasoning is then employed to ensure that the choices offered to the user in expression construction are “reasonable” as well as valid.
Forecasting Electricity Spot Prices Accounting for Wind Power Predictions

A two-step methodology for forecasting of electricity spot prices is introduced, with focus on the impact of predicted system load and wind power generation. The nonlinear and nonstationary influence of these explanatory variables is accommodated in a first step based on a nonparametric and time-varying regression model. In a second step, time-series models, i.e., ARMA and Holt-Winters, are applied to account for residual autocorrelation and seasonal dynamics. Empirical results are presented for out-of-sample forecasts of day-ahead prices in the Western Danish price area of Nord Pool's Elspot, during a two year period covering 2010-2011. These results clearly demonstrate the practical benefits of accounting for the complex influence of these explanatory variables.
Bonding strength characterization of eutectic-based WLP using molecular dynamics and wafer level shear testing

This paper investigated the bonding shear strength of eutectic-based wafer-level-packaging (WLP) for piezoresistive MEMS accelerometers. The bonding conditions were experimentally and analytically determined to realize higher shear strength without a diffusion of the solder material atoms to the adhesion layers: the energy dispersive X-ray (EDX) spectrometry and molecular dynamics (MD) simulations clarified the eutectic reaction of solder materials used in this research. Consequently, the bonding load of 9.8 kN and the temperature of 300 °C was found to be one of rational conditions because of a sufficient shear strength to endure the polishing process after the WLP and few diffusion of solder material atoms to the adhesion layer.
Morphologies Observed in Ultraflexible Microemulsions with and without the Presence of a Strong Acid

We show that three different morphologies exist near the two-phase boundary of ternary systems containing a hydrotropic cosolvent. Based on synchrotron small- and wide-angle X-ray scattering combined with molecular dynamics, we rationalize the specific scattering signature of direct, bicontinuous, and reverse mesoscale solubilization. Surprisingly, these mesostructures are resilient toward strong acids, which are required in industrial applications. However, on a macroscopic scale, the phase boundary shifts in salting-in and salting-out in the direct and respectively reverse regime, leading to a crossing of the binodals.
Real-Time Respiratory Motion Analysis Using 4-D Shape Priors

Respiratory motion analysis based on range imaging (RI) has emerged as a popular means of generating respiration surrogates to guide motion management strategies in computer-assisted interventions. However, existing approaches employ heuristics, require substantial manual interaction, or yield highly redundant information. In this paper, we propose a framework that uses preprocedurally obtained 4-D shape priors from patient-specific breathing patterns to drive intraprocedural RI-based real-time respiratory motion analysis. As the first contribution, we present a shape motion model enabling an unsupervised decomposition of respiration induced high-dimensional body surface displacement fields into a low-dimensional representation encoding thoracic and abdominal breathing. Second, we propose a method designed for GPU architectures to quickly and robustly align our models to high-coverage multiview RI body surface data. With our fully automatic method, we obtain respiration surrogates yielding a Pearson correlation coefficient (PCC) of 0.98 with conventional surrogates based on manually selected regions on RI body surface data. Compared to impedance pneumography as a respiration signal that measures the change of lung volume, we obtain a PCC of 0.96. Using off-the-shelf hardware, our framework enables high temporal resolution respiration analysis at 50 Hz.
Proteinase-mediated cell signalling: targeting proteinase-activated receptors (PARs) by kallikreins and more

Abstract Serine proteinases, like trypsin, can play a hormone-like role by triggering signal transduction pathways in target cells. In many respects these hormone-like actions of proteinases can now be understood in terms of the pharmacodynamics of the G protein-coupled ‘receptor’ responsible for the cellular actions of thrombin (proteinase-activated receptor-1, or PAR1). PAR1, like the other three members of this receptor family (PAR2, PAR3 and PAR4), has a unique mechanism of activation involving the proteolytic unmasking of an N-terminally tethered sequence that can activate the receptor. The selective activation of each PAR by short synthetic peptides representing these sequences has demonstrated that PAR1, PAR2 and PAR4 play important roles in regulating physiological responses ranging from vasoregulation and cell growth to inflammation and nociception. We hypothesise that the tissue kallikreins may regulate signal transduction via the PARs. Although PARs can account for many of their biological actions, kallikreins may also cause effects by mechanisms not involving the PARs. For instance, trypsin activates the insulin receptor and thrombin can act via a mechanism involving its non-catalytic domains. Based on the data we summarise, we propose that the kallikreins, like thrombin and trypsin, must now be considered as important ‘hormonal’ regulators of tissue function.
2D-STR: Reducing Spatio-temporal Traffic Datasets by Partitioning and Modelling

Spatio-temporal data generated by sensors in the environment, such as traffic data, is widely used in the transportation domain. However, learning from and analysing such data is increasingly problematic as the volume of data grows. Therefore, methods are required to reduce the quantity of data needed for multiple types of subsequent analysis without losing significant information. In this paper, we present the 2-Dimensional Spatio-Temporal Reduction method (2D-STR), which partitions the spatio-temporal matrix of a dataset into regions of similar instances, and reduces each region to a model of its instances. The method is shown to be effective at reducing the volume of a traffic dataset to <5% of its original volume whilst achieving a normalise root mean squared error of <5% when reproducing the original features of the dataset.