Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein Profiling in Hepatocellular Carcinoma by Label-Free Quantitative Proteomics in Two West African Populations

Background Hepatocellular Carcinoma is the third most common cause of cancer related death worldwide, often diagnosed by measuring serum AFP; a poor performance stand-alone biomarker. With the aim of improving on this, our study focuses on plasma proteins identified by Mass Spectrometry in order to investigate and validate differences seen in the respective proteomes of controls and subjects with LC and HCC. Methods Mass Spectrometry analysis using liquid chromatography electro spray ionization quadrupole time-of-flight was conducted on 339 subjects using a pooled expression profiling approach. ELISA assays were performed on four significantly differentially expressed proteins to validate their expression profiles in subjects from the Gambia and a pilot group from Nigeria. Results from this were collated for statistical multiplexing using logistic regression analysis. Results Twenty-six proteins were identified as differentially expressed between the three subject groups. Direct measurements of four; hemopexin, alpha-1-antitrypsin, apolipoprotein A1 and complement component 3 confirmed their change in abundance in LC and HCC versus control patients. These trends were independently replicated in the pilot validation subjects from Nigeria. The statistical multiplexing of these proteins demonstrated performance comparable to or greater than ALT in identifying liver cirrhosis or carcinogenesis. This exercise also proposed preliminary cut offs with achievable sensitivity, specificity and AUC statistics greater than reported AFP averages. Conclusions The validated changes of expression in these proteins have the potential for development into high-performance tests usable in the diagnosis and or monitoring of HCC and LC patients. The identification of sustained expression trends strengthens the suggestion of these four proteins as worthy candidates for further investigation in the context of liver disease. The statistical combinations also provide a novel inroad of analyses able to propose definitive cut-offs and combinations for evaluation of performance.
Quality-driven poisson-guided autoscanning

We present a quality-driven, Poisson-guided autonomous scanning method. Unlike previous scan planning techniques, we do not aim to minimize the number of scans needed to cover the object's surface, but rather to ensure the high quality scanning of the model. This goal is achieved by placing the scanner at strategically selected Next-Best-Views (NBVs) to ensure progressively capturing the geometric details of the object, until both completeness and high fidelity are reached. The technique is based on the analysis of a Poisson field and its geometric relation with an input scan. We generate a confidence map that reflects the quality/fidelity of the estimated Poisson iso-surface. The confidence map guides the generation of a viewing vector field, which is then used for computing a set of NBVs. We applied the algorithm on two different robotic platforms, a PR2 mobile robot and a one-arm industry robot. We demonstrated the advantages of our method through a number of autonomous high quality scannings of complex physical objects, as well as performance comparisons against state-of-the-art methods.
A Novel Probabilistic Key Management Algorithm for Large-Scale MANETs

Mobile ad hoc networks (MANETs) have become very interesting during last years, but the security is the most important problem they suffer from. Asymmetric cryptography is a very useful solution to provide a secure environment in multihop networks where intermediate nodes are able to read, drop or change messages before resending them. However, storing all keys in every node by this approach is inefficient, if practically possible, in large-scale MANETs due to some limitations such as memory or process capability. In this paper, we propose a new probabilistic key management algorithm for large-scale MANETs. To the best of our knowledge, this is the first method which probabilistically uses asymmetric cryptography to manage the keys in MANETs. In this algorithm, we store only a few keys in each node instead of all. We analytically prove that the network will remain connected with a high probability more than 99:99%. Furthermore, we analytically calculate the average path length in the network and show that this parameter will not have a significant increment using our algorithm. All analytical results are also validated by simulation to make them dependable.
EEG-based driving fatigue prediction system using functional-link-based fuzzy neural network

This study presents a fuzzy prediction system for the forecasting and estimation of driving fatigue, which utilizes a functional-link-based fuzzy neural network (FLFNN) to predict the drowsiness (DS) level in car driving task. The cognitive state in car driving task is one of key issue in cognitive neuroscience because fatigue driving usually causes enormous losses nowadays. The damage can be extremely decreased by the assistant of various artificial systems. Many Electroencephalography (EEG)-based interfaces have been widely developed recently due to its convenient measurement and real-time response. However, the improvement of recognition accuracy is still confined to some specific problems (e.g., individual difference). In order to solve this issue, the proposed methodology in this paper utilizes a nonlinear fuzzy neural network structure to increase the adaptability in the real-world environment. Therefore, this study is further to analysis the brain activities in car driving, which is constructed in a simulated three-dimensional virtual-reality (VR) environment. Finally, through the development of brain cognitive model in car driving task, this system can predict the cognitive state effectively before drivers' action and then provide correct feedback to users. This study also compared the result with the-state-of-art systems, including Linear Regression (LR), Multi-Layer Perceptron Neural Network (MLPNN) and Support Vector Regression (SVR). Results of this study demonstrate the effectiveness of the proposed FLFNN model.
Multimodal recognition of personality traits in human-computer collaborative tasks

The user's personality in Human-Computer Interaction (HCI) plays an important role for the overall success of the interaction. The present study focuses on automatically recognizing the Big Five personality traits from 2-5 min long videos, in which the computer interacts using different levels of collaboration, in order to elicit the manifestation of these personality traits. Emotional Stability and Extraversion are the easiest traits to automatically detect under the different collaborative settings: all the settings for Emotional Stability and intermediate and fully-non collaborative settings for Extraversion. Interestingly, Agreeableness and Conscientiousness can be detected only under a moderately non-collaborative setting. Finally, our task does not seem to activate the full range of dispositions for Creativity.
The link between exercise and titin passive stiffness

What is the topic of this review? This review focuses on how in vivo and molecular measurements of cardiac passive stiffness can predict exercise tolerance and how exercise training can reduce cardiac passive stiffness. What advances does it highlight? This review highlights advances in understanding the relationship between molecular (titin‐based) and in vivo (left ventricular) passive stiffness, how passive stiffness modifies exercise tolerance, and how exercise training may be therapeutic for cardiac diseases with increased passive stiffness.
Zero Moment Point based pace reference generation for quadruped robots via preview control

Legged robots have significant advantages over other types of mobile robots when task at hand requires the robot to overcome obstacles. This paper presents a reference trajectory generation method for a quadruped robot for pace gait on a flat surface. The approach is based on the Zero Moment Point (ZMP) stability criterion and the Linear Inverted Pendulum Model (LIPM). ZMP reference trajectories for pace is proposed, from which reference trajectories for the Robot Center of Mass (CoM) references are obtained by applying preview control. The position of leg joints are computed using inverse kinematics according to CoM reference trajectory. Proposed reference trajectory generation synthesis is tested through full-dynamics 3D simulation. A 16-degrees-of-freedom (DOF) quadruped robot model is used in the simulations. Simulation results show the success of the reference generation technique for the pace gait.
Space and Time Localization in Wireless Sensor Networks 12

Wireless sensor networks (WSN) are an increasingly attractive means to bridge the gap between the physical and virtual world. WSNs are envisioned to be used to fulfill complex monitoring tasks. Space and time play a crucial role in wireless sensor networks, since sensor nodes are used to collaboratively monitor physical phenomena and their spacetime properties. A number of techniques and distributed algorithms for location estimation and time synchronization have been developed specifically for sensor networks. There are many similarities in space and time domains. This affects the location estimation and time synchronization, ranging from applications and requirements to basic approaches and concrete algorithmic techniques. An improved approach for space and time localization in WSNs is given in this paper. The main aspect of the algorithm is the use of a mobile beacon for both localization and synchronization. A mobile beacon is a node that moves around the sensor’s field and it is aware of its time and position, equipped with a Global Positioning System (GPS) receiver. The synchronization component uses the packets required by the positioning component to improve its performance. The positioning component benefits from the communication, required by the synchronization component to decrease errors. A set of experiments and simulations are presented to evaluate the performance of the algorithm in order to reduce communication and processing resources and save energy and network resources.
Option-Pricing in Incomplete Markets: The Hedging Portfolio plus a Risk Premium-Based Recursive Approach

Consider a non-spanned security C_{T} in an incomplete market. We study the risk/return trade-offs generated if this security is sold for an arbitrage-free price Câ‚€ and then hedged. We consider recursive "one-period optimal" self-financing hedging strategies, a simple but tractable criterion. For continuous trading, diffusion processes, the one-period minimum variance portfolio is optimal. Let Câ‚€(0) be its price. Self-financing implies that the residual risk is equal to the sum of the one-period orthogonal hedging errors, âˆ‘_{tâ‰¤T}Y_{t}(0)e^{r(T-t)}. To compensate the residual risk, a risk premium y_{t}I”t is associated with every Y_{t}. Now let Câ‚€(y) be the price of the hedging portfolio, and âˆ‘_{tâ‰¤T}(Y_{t}(y)+y_{t}I”t)e^{r(T-t)} is the total residual risk. Although not the same, the one-period hedging errors Y_{t}(0) and Y_{t}(y) are orthogonal to the trading assets, and are perfectly correlated. This implies that the spanned option payoff does not depend on y. Let Câ‚€=Câ‚€(y). A main result follows. Any arbitrage-free price, Câ‚€, is just the price of a hedging portfolio (such as in a complete market), Câ‚€(0), plus a premium, Câ‚€-Câ‚€(0). That is, Câ‚€(0) is the price of the option's payoff which can be spanned, and Câ‚€-Câ‚€(0) is the premium associated with the option's payoff which cannot be spanned (and yields a contingent risk premium of âˆ‘y_{t}I”te^{r(T-t)} at maturity). We study other applications of option-pricing theory as well
Childhood Sexual Abuse and the Development of Recurrent Major Depression in Chinese Women

Background Our prior study in Han Chinese women has shown that women with a history of childhood sexual abuse (CSA) are at increased risk for developing major depression (MD). Would this relationship be found in our whole data set? Method Three levels of CSA (non-genital, genital, and intercourse) were assessed by self-report in two groups of Han Chinese women: 6017 clinically ascertained with recurrent MD and 5983 matched controls. Diagnostic and other risk factor information was assessed at personal interview. Odds ratios (ORs) were calculated by logistic regression. Results We confirmed earlier results by replicating prior analyses in 3,950 new recurrent MD cases. There were no significant differences between the two data sets. Any form of CSA was significantly associated with recurrent MD (OR 4.06, 95% confidence interval (CI) [3.19–5.24]). This association strengthened with increasing CSA severity: non-genital (OR 2.21, 95% CI 1.58–3.15), genital (OR 5.24, 95% CI 3.52–8.15) and intercourse (OR 10.65, 95% CI 5.56–23.71). Among the depressed women, those with CSA had an earlier age of onset, longer depressive episodes. Recurrent MD patients those with CSA had an increased risk for dysthymia (OR 1.60, 95%CI 1.11–2.27) and phobia (OR 1.41, 95%CI 1.09–1.80). Any form of CSA was significantly associated with suicidal ideation or attempt (OR 1.50, 95% CI 1.20–1.89) and feelings of worthlessness or guilt (OR 1.41, 95% CI 1.02–2.02). Intercourse (OR 3.47, 95%CI 1.66–8.22), use of force and threats (OR 1.95, 95%CI 1.05–3.82) and how strongly the victims were affected at the time (OR 1.39, 95%CI 1.20–1.64) were significantly associated with recurrent MD. Conclusions In Chinese women CSA is strongly associated with recurrent MD and this association increases with greater severity of CSA. Depressed women with CSA have some specific clinical traits. Some features of CSA were associated with greater likelihood of developing recurrent MD.
A Time-Window Based Request Distribution Strategy for Media Server Cluster

Characteristics of streaming media service is different from the traditional networked services, e.g. web service, which makes designing the request distribution strategy face additional challenges. By exploiting the high skewness in streaming media access pattern, a variable-duration time-window based scheduling strategy is proposed for clustered streaming media systems, and it can help to improve cache utility while keeping workload balance among the servers in the cluster. The throughput requirement of storage subsystem is relieved, and the system utility is improved. Finally, computer simulations are performed to investigate its performance, and the results show that our proposed algorithm is better than traditional scheduling strategies.
A BAT in the Lab: Experimental Results of New Link State Routing Protocol

In this paper, we analyze the common problems of link state routing protocols along with an evolution of the Optimized Link State Routing protocol, called BATMAN. BATMAN uses a weighted and auto-selective flooding which makes it a sort of gossip protocol. The flooding uses short control packets to make every mesh node aware of network topology. However, there is no topology information dissemination and no multi-point relaying node selection. While routing loops are easily avoided by this modified flooding mechanism, there are still problems about self-interference. Due the broadcast nature of the radio channel, traffic data can disturb control data, and localized control data may cause interference on neighboring control data as well. In fact there is a complicated interaction between MAC and routing protocols. We demonstrate this fact by extensive field tests carried out by our measurement framework based on statistical tests. In particular, we show that the so called hop horizon of multi-hop network can be due to the routing and MAC protocols. These analysis can be a useful hint for the design of more intelligent routing, or rather a less cross-layer dependence of protocols.
Improving the spectral efficiency of FDM-CPM systems through packing and multiuser processing

In frequency division multiplexed (FDM) systems, spectral efficiency can be increased by reducing the spacing between two adjacent channels, thus increasing the relevant interference and possibly accounting for it at the receiver. In this paper, we consider a FDM system where each user employs a continuous phase modulation (CPM), serially concatenated with an outer code through an interleaver, and iterative detection/decoding. We show that, by taking into account the increased interference using properly designed multiuser detection and synchronization schemes, it is possible to implement transmission schemes with unprecedented spectral efficiency at a price of a limited complexity increase with respect to a classical single-user receiver which neglects the interference.
Features for Named Entity Recognition in Czech Language

This paper deals with Named Entity Recognition (NER). Our work focuses on the application for the Czech News Agency (CTK). We propose and implement a Czech NER system that facilitates the data searching from the CTK text news databases. The choice of the feature set is crucial for the NER task. The main contribution of this work is thus to propose and evaluate some different features for the named entity recognition and to create an “optimal” set of features. We use Conditional Random Fields (CRFs) as a classifier. Our system is tested on a Czech NER corpus with nine main named entity classes. We reached 58% of the F-measure with the best feature set which is sufficient for our target application.
Playing Mozart by Analogy: Learning Multi-level Timing and Dynamics Strategies

The article describes basic research in the area of machine learning and musical expression. A first step towards automatic induction of multi-level models of expressive performance (currently only tempo and dynamics) from real performances by skilled pianists is presented. The goal is to learn to apply sensible tempo and dynamics “shapes” at various levels of the hierarchical musical phrase structure. We propose a general method for decomposing given expression curves into elementary shapes at different levels, and for separating phrase-level expression patterns from local, note-level ones. We then present a hybrid learning system that learns to predict, via two different learning algorithms, both note-level and phrase-level expressive patterns, and combines these predictions into complex composite expression curves for new pieces. Experimental results indicate that the approach is generally viable; however, we also discuss a number of severe limitations that still need to be overcome in order to arrive at truly musical machine-generated performances.
Research on Verb Subcategorization-Based Syntactic Parsing Postprocess for Chinese Language

In this paper, we propose a simple approach to use verb sub categorization-based pattern matching method to rerank the output of a baseline parsing system. A baseline parser first provides a set of n-best candidate parsing trees. Then we extract various features of verb sub categorization from train corpora. And use those features of verb sub categorization extracted from train corpus to rerank the n-best list via pattern matching approach. The result shows our approach reaches a good performance.
Optimized power allocation approach in relay-based cognitive radio network

In this paper, we address the issue of power allocation in a relay-based cognitive radio network. The power allocation approach analyzed here maximizes the system throughput while maintaining interference to a tolerable limit. We modify the approach to achieve a higher convergence rate. Later we further propose a near optimal power allocation approach that significantly lowers the computational complexity. A detailed analysis has been done before simulation. The simulated results validate the theoretical analysis.
A novel handover algorithm design in WiMAX networks

In this paper, we propose a handover algorithm for WiMAX networks. The algorithm relies on the computation of the received signal-to-noise ratio (SNR) at a Mobile Station (MS) from neighboring Base Stations (BSs) combined with the capacity estimation of the targeting cell. The proposed handover algorithm is implemented by a joint decision between the MS and BS nodes. The performance of the proposed algorithm is evaluated in terms of call dropping ratio and system throughput. A comparison with the conventional hard handover algorithm is presented.
mGluR Control of Interneuron Output Regulates Feedforward Tonic GABAA Inhibition in the Visual Thalamus

Metabotropic glutamate receptors (mGluRs) play a crucial role in regulation of phasic inhibition within the visual thalamus. Here we demonstrate that mGluR-dependent modulation of interneuron GABA release results in dynamic changes in extrasynaptic GABAA receptor (eGABAAR)-dependent tonic inhibition in thalamocortical (TC) neurons of the rat dorsal lateral geniculate nucleus (dLGN). Application of the group I selective mGluR agonist dihydroxyphenylglycine produces a concentration-dependent enhancement of both IPSC frequency and tonic GABAA current (IGABAtonic) that is due to activation of both mGluR1a and mGluR5 subtypes. In contrast, group II/III mGluR activation decreases both IPSC frequency and IGABAtonic amplitude. Using knock-out mice, we show that the mGluR-dependent modulation of IGABAtonic is dependent upon expression of δ-subunit containing eGABAARs. Furthermore, unlike the dLGN, no mGluR-dependent modulation of IGABAtonic is present in TC neurons of the somatosensory ventrobasal thalamus, which lacks GABAergic interneurons. In the dLGN, enhancement of IPSC frequency and IGABAtonic by group I mGluRs is not action potential dependent, being insensitive to TTX, but is abolished by the L-type Ca2+ channel blocker nimodipine. These results indicate selective mGluR-dependent modulation of dendrodendritic GABA release from F2-type terminals on interneuron dendrites and demonstrate for the first time the presence of eGABAARs on TC neuron dendritic elements that participate in “triadic” circuitry within the dLGN. These findings present a plausible novel mechanism for visual contrast gain at the thalamic level and shed new light upon the potential role of glial ensheathment of synaptic triads within the dLGN.
Fast elimination of redundant linear equations and reconstruction of recombination-free mendelian inheritance on a pedigree

Computational inference of haplotypes from genotypes has attracted a great deal of attention in the computational biology community recently, partially driven by the international HapMap project. In this paper, we study the question of how to efficiently infer haplotypes from genotypes of individuals related by a pedigree, assuming that the hereditary process was free of mutations (i.e. the Mendelian law of inheritance) and recombinants. The problem has recently been formulated as a system of linear equations over the finite field of F(2) and solved in O(m3n3) time by using standard Gaussian elimination, where m is the number of loci (or markers) in a genotype and n the number of individuals in the pedigree. We give a much faster algorithm with running time O(mn2 + n3 log2 n log log n). The key ingredients of our construction are (i) a new system of linear equations based on some spanning tree of the pedigree graph and (ii) an efficient method for eliminating redundant equations in a system of O(mn) linear equations over O(n) variables. Although such a fast elimination method is not known for general systems of linear equations, we take advantage of the underlying pedigree graph structure and recent progress on low-stretch spanning trees.