Miracle-dz/UniFAR-FTUs huggingface dataset:
Aging Uncouples Heritability and Expression-QTL in Caenorhabditis elegans

The number and distribution of gene expression QTL (eQTL) represent the genetic architecture of many complex traits, including common human diseases. We previously reported that the heritable eQTL patterns are highly dynamic with age in an N2 × CB4856 recombinant inbred population of the nematode Caenorhabditis elegans. In particular, we showed that the number of eQTL decreased with age. Here, we investigated the reason for this decrease by combining gene expression profiles at three ages in the wild types N2 and CB4856 with the reported expression profiles of the RIL population. We determined heritability and transgression (when gene expression levels in the RILs are more extreme than the parents) and investigated their relation with eQTL changes with age. Transgressive segregation was widespread but depended on physiological age. The percentage of genes with an eQTL increased with a higher heritability in young worms. However, for old worms this percentage hardly increased. Using a single marker approach, we found that almost 20% of genes with heritability >0.9 had an eQTL in developing worms. Surprisingly, only 10% was found in old worms. Using a multimarker approach, this percentage increased to almost 30% for both age groups. Comparison of the single marker to a multiple marker eQTL mapping indicated that heritable regulation of gene expression becomes more polygenic in aging worms due to multiple loci and possible epistatic interactions. We conclude that linkage studies should account for the relation between increased polygenic regulation and diminished effects at older ages.
Computational Capacity and Energy Consumption of Complex Resistive Switch Networks

Resistive switches are a class of emerging nanoelectronics devices that exhibit a wide variety of switching characteristics closely resembling behaviors of biological synapses. Assembled into random networks, such resistive switches produce emerging behaviors far more complex than that of individual devices. This was previously demonstrated in simulations that exploit information processing within these random networks to solve tasks that require nonlinear computation as well as memory. Physical assemblies of such networks manifest complex spatial structures and basic processing capabilities often related to biologically-inspired computing. We model and simulate random resistive switch networks and analyze their computational capacities. We provide a detailed discussion of the relevant design parameters and establish the link to the physical assemblies by relating the modeling parameters to physical parameters. More globally connected networks and an increased network switching activity are means to increase the computational capacity linearly at the expense of exponentially growing energy consumption. We discuss a new modular approach that exhibits higher computational capacities, and energy consumption growing linearly with the number of networks used. The results show how to optimize the trade-o between computational capacity and energy e ciency and are relevant for the design and fabrication of novel computing architectures that harness random assemblies of emerging nanodevices.
Efficient Design of Bio-Basis Function to Predict Protein Functional Sites Using Kernel-Based Classifiers

In order to apply the powerful kernel-based pattern recognition algorithms such as support vector machines to predict functional sites in proteins, amino acids need encoding prior to input. In this regard, a new string kernel function, termed as the modified bio-basis function, is proposed that maps a nonnumerical sequence space to a numerical feature space. The proposed string kernel function is developed based on the conventional bio-basis function and needs a bio-basis string as a support like conventional kernel function. The concept of zone of influence of a bio-basis string is introduced in the proposed kernel function to take into account the influence of each bio-basis string in nonnumerical sequence space. An efficient method is described to select a set of bio-basis strings for the proposed kernel function, integrating the Fisher ratio and a novel concept of degree of resemblance. The integration enables the method to select a reduced set of relevant and nonredundant bio-basis strings.
Specular multipath exploitation for improved velocity estimation in through-the-wall radar imaging

Through-the-wall radar imaging aims at determining the locations and velocities of obscured targets. The slow velocities of indoor targets are in particular difficult to detect and estimate. It is shown by theoretical considerations and simulation that indirect propagation paths contain significant information on the target movements, which can be utilized for improved sensing. We propose a compressive sensing based method that exploits a multipath model to improve the velocity resolution of the reconstruction. Simulation results demonstrate the effectiveness of the proposed approach.
Multi-agent event recognition in structured scenarios

We present a framework for the automatic recognition of complex multi-agent events in settings where structure is imposed by rules that agents must follow while performing activities. Given semantic spatio-temporal descriptions of what generally happens (i.e., rules, event descriptions, physical constraints), and based on video analysis, we determine the events that occurred. Knowledge about spatio-temporal structure is encoded using first-order logic using an approach based on Allen's Interval Logic, and robustness to low-level observation uncertainty is provided by Markov Logic Networks (MLN). Our main contribution is that we integrate interval-based temporal reasoning with probabilistic logical inference, relying on an efficient bottom-up grounding scheme to avoid combinatorial explosion. Applied to one-on-one basketball, our framework detects and tracks players, their hands and feet, and the ball, generates event observations from the resulting trajectories, and performs probabilistic logical inference to determine the most consistent sequence of events. We demonstrate our approach on 1hr (100,000 frames) of outdoor videos.
The design and analysis of a computational model of cooperative coevolution

As evolutionary algorithms are applied to the solution of increasingly complex systems, explicit notions of modularity must be introduced to provide reasonable opportunities for solutions to evolve in the form of interacting coadapted subcomponents. The difficulty comes in finding computational extensions to our current evolutionary paradigms in which such subcomponents "emerge" rather than being hand designed. At issue is how to identify and represent such subcomponents, provide an environment in which they can interact and coadapt, and apportion credit to them for their contributions to the problem-solving activity such that their evolution proceeds without human involvement. We begin by describing a computational model of cooperative coevolution that includes the explicit notion of modularity needed to provide reasonable opportunities for solutions to evolve in the form of interacting coadapted subcomponents. In this novel approach, subcomponents are represented as genetically isolated species and evolved in parallel. Individuals from each species temporarily enter into collaborations with members of the other species and are rewarded based on the success of the collaborations in solving objective functions. Next, we perform a sensitivity analysis on a number of characteristics of decomposable problems likely to have an impact on the effectiveness of the coevolutionary model. Through focused experimentation using tunable test problems chosen specifically to measure the effect of these characteristics, we provide insight into their influence and how any exposed difficulties may be overcome. This is followed by a study of the basic problem-decomposition capability of the model. We show, within the context of a relatively simple environment, that evolutionary pressure can provide the needed stimulus for the emergence of an appropriate number of subcomponents that cover multiple niches, are evolved to an appropriate level of generality, and can adapt to a changing environment. We also perform two case studies in emergent decomposition on complex problems from the domains of artificial neural networks and concept learning. These case studies validate the ability of the model to handle problems only decomposable into subtasks with complex and difficult to understand interdependencies.
Procedural approach to mitigating concurrently applied clinical practice guidelines

There is a pressing need in clinical practice to mitigate (identify and address) adverse interactions that occur when a comorbid patient is managed according to multiple concurrently applied disease-specific clinical practice guidelines (CPGs). We describe an automatic algorithm for mitigating undesirable interactions for pairs of CPGs. The algorithm constructs logical models of processed CPGs and employs constraint logic programming to solve them. It handles two important issues frequently occurring in CPGs – iterative actions forming a cycle and numerical measurements. Dealing with these two issues in practice relies on a physician’s knowledge and the manual analysis of CPGs. Yet for guidelines to be considered stand-alone and an easy to use clinical decision support tool this process needs to be automated. In this paper we present our algorithm that aims to build such a tool by mitigating multiple CPGs while handling cycles and numerical measurements. The application of the mitigation algorithm is illustrated with a clinical case study involving a comorbid patient suffering from atrial fibrillation in the setting of WolffParkinsons-White syndrome.
Fast Recognition of Natural Feature Identifiers by a Mobile Phone

One of the most important and widely used technique for automatic identification is the use of visual codes. Identifiers encoded in various symbols and patterns make electronic reading possible, that greatly helps and speeds up processing, e.g., at cashier lines, warehouse transactions, high speed processing places, production lines. The common codes designed using geometric patterns usually identify types or entities. However, patterns can be produced that, by their nature, are unique and thus can be used to validate originality or authenticity. In this paper, we focus on the automatic localization and recognition of a kind of natural feature identifier (NFI). We present an image processing algorithm that successfully locates NFI code region in an image taken by a mobile camera and extracts features of the NFI glitters that can be the basis for recognition. We also show preliminary experimental results.
A Study of the Impact of Persuasive Argumentation in Political Debates

Persuasive communication is the process of shaping, reinforcing and changing others' responses. In political debates, speakers express their views towards the debated topics by choosing both the content of their discourse and the argumentation process. In this work we study the use of semantic frames for modelling argumentation in speakers' discourse. We investigate the impact of a speaker's argumentation style and their effect in influencing an audience in supporting their candidature. We model the influence index of each candidate based on their relative standings in the polls released prior to the debate and present a system which ranks speakers in terms of their relative influence using a combination of content and persuasive argumentation features. Our results show that although content alone is predictive of a speaker's influence rank, persuasive argumentation also affects such indices.
BUT system for NIST 2008 speaker recognition evaluation

This paper presents BUT system submitted to NIST 2008 SRE. It includes two subsystems based on Joint Factor Analysis (JFA) GMM/UBM and one based on SVM-GMM. The systems were developed on NIST SRE2006 data, and the results arepresented on NIST SRE 2008 evaluation data. We concentrate on the influence of side information in the calibration. Index Terms: speaker recognition, joint factor analysis, NIST SRE 2008.
A Review of Fragile Image Watermarks

1. ABSTRACT Many image watermarks have been proposed to protect intellectual property in an age where digital images may be easily modified and perfectly reproduced. In a fragile marking system, a signal (watermark) is embedded within an image such that subsequent alterations to the watermarked image can be detected with high probability. The insertion of the watermark is perceptually invisible under normal human observation. These types of marks have found applicability in image authentication systems. In this paper we discuss fragile marking systems and their desirable features, common methods of attack, and survey some recent marking systems.
Searching for points-to analysis

The complexity of points-to analysis is well understood, but the approximations used to carry out points-to analysis efficiently are less well understood. In this paper we characterize points-to analysis as a reachability problem on a program's state space. Reachability analysis can be performed approximately but more efficiently for a program to which certain basic program transformations have been applied. We show the source of approximation and efficiency in several existing points-to analysis algorithms in terms of these generic program transformations.
A Posteriori Error Estimates of Mixed Methods for Two Phase Flow Problems

Two phase flow problem in a porous medium is governed by a system of nonlinear equations. One is an elliptic equation for the pressure and the other is a parabolic equation for the concentration of one of the fluids. Since the pressure appears in the concentration only through its velocity field, we choose a mixed finite element to approximate the pressure equation and use the standard Galerkin method to treat the concentration equation. We shall obtain an explicit a posteriori error estimator in L2(L2) for the semi-discrete scheme of the nonlinear coupled system.
Stereoselective Bupropion Hydroxylation as an In Vivo Phenotypic Probe for Cytochrome P4502B6 (CYP2B6) Activity

The clearance of racemic bupropion, metabolized selectively by CYP2B6 in vitro, has been used clinically to phenotype CYP2B6 activity, polymorphisms, and drug interactions but has known limitations. Bupropion hydroxylation by CYP2B6 is stereoselective. This investigation assessed the stereoselectivity of bupropion pharmacokinetics and the influence of CYP2B6 induction. Ten healthy volunteers received immediate‐release bupropion before and after 7 days of rifampin. Plasma and urine bupropion and hydroxybupropion were analyzed using a stereoselective assay. Plasma area under the curve (AUC0‐∞) and maximum concentrations were 3‐fold greater for R‐ than S‐bupropion. Bupropion apparent oral clearance was 3‐ and 2‐fold greater for S‐ than R‐ and R,S‐bupropion, respectively Hydroxybupropion plasma AUC0‐∞ and elimination half‐life were significantly less for (S,S)‐ than (R,R)‐ and the racemate. (S,S)‐hydroxybupropion was formation rate limited, whereas (R,R)‐hydroxybupropion and the racemate were elimination rate limited. Rifampin doubled both R‐ and S‐bupropion clearance and caused 4‐fold increases in both (R,R)‐ and (S,S)‐hydroxybupropion formation clearances. Increases in the plasma hydroxybupropion/bupropion AUC0‐∞ ratio were greater for (S,S)‐ than (R,R)‐hydroxybupropion. Simplified plasma and urine metrics of stereoselective bupropion metabolism and clearance were identified. Because metabolite formation clearance is the best in vivo metric of enzyme activity and due, therefore, to faster S‐bupropion elimination and formation rate‐limited (S,S)‐hydroxybupropion kinetics, stereoselective S‐bupropion hydroxylation and (S,S)‐hydroxybupropion formation clearance may be a useful and improved phenotypic probe for CYP2B6.
A Bootstrapping Approach to Information Extraction Domain Porting

This paper presents a seed-driven, bootstrapping approach to domain porting that could be used to customize a generic information extraction (IE) capability for a specific domain. The approach taken is based on the existence of a robust, domain-independent IE engine that can continue to be enhanced, independent of any particular domain. This approach combines the strengths of parsing-based symbolic rule learning and the high performance linear string-based Hidden Markov Model (HMM) to automatically derive a customized IE system with balanced precision and recall. The key idea is to apply precision-oriented symbolic rules learned in the first stage to a large corpus in order to construct an automatically tagged training corpus. This training corpus is then used to train an HMM to boost the recall. The experiments conducted in named entity (NE) tagging and relationship extraction show a performance close to the performance of supervised learning systems.
A survey of race bug detection techniques for multithreaded programmes

As multithreaded programmes become popular to fully utilize multicore CPUs, many race bug detection techniques have been developed to find concurrency errors in multithreaded programmes effectively. Because these techniques have different views on target programme execution and detect race bugs of various types, it is difficult to characterize, compare and improve race bug detection techniques. This paper presents a formal execution model, which can uniformly represent various views of race bug detection techniques on target programme execution. Then, this paper classifies 43 race bug detection techniques according to their target race bugs. We classify race bugs on whether or not a bug violates operation block specification and/or data association specification. This survey provides researchers with a clear top‐down view of various race bug detection techniques. In addition, the concrete examples of various race bugs in this survey can help field engineers avoid race bugs in their multithreaded programmes. Copyright © 2014 John Wiley & Sons, Ltd.
Robust Power Control for Cognitive Radio in Spectrum Underlay Networks

Power control is a key technique in spectrum underlay cognitive network to guarantee the interference temperature limit of the primary users (PUs) and the quality of service of the secondary users (SUs). In this paper, a robust power control scheme via link gain pricing with H ∞ estimator is proposed. The scheme guarantees the interference temperature of the PUs through operating in the network-centric manner, and keeps the fairness between the SUs through link gain pricing. Furthermore, the H ∞ filter is also used in the proposed scheme to estimate the channel variation, and thus the power control scheme is robust to the severe channel fading. Plenty of simulations are taken, and prove its superior robust performance against the channel fading, and its effectiveness in guaranteeing the interference temperature limit of the PUs.
Cost comparison of insulin glargine with insulin detemir in a basal-bolus regime with mealtime insulin aspart in type 2 diabetes in Germany

Objective: To compare the treatment costs of insulin glargine (IG; Lantus®) to detemir (ID; Levemir®), both combined with bolus insulin aspart (NovoRapid®) in type 2 diabetes (T2D) in Germany. Methods: Cost comparison was based on data of a 1-year randomised controlled trial [1]. IG was administered once daily and ID once (57% of patients) or twice daily (43%) according to treatment response. At the end of the trial, mean daily basal insulin doses were 0.59 U/kg (IG) and 0.82 U/kg (ID). Aspart doses were 0.32 U/kg (IG) and 0.36 U/kg (ID). Costs were calculated from the German statutory health insurance (SHI) perspective using official 2008 prices. Sensitivity analyses were performed to test robustness of the results. Results: Annual basal and bolus insulin costs per patient were € 1,473 (IG) and € 1,940 (ID). The cost of lancets and blood glucose test strips were € 1,125 (IG) and € 1,286 (ID). Annual costs for needles were € 393 (IG) and € 449 (ID). The total annual cost per patient of administering IG was € 2,991 compared with € 3,675 for ID, translating into a 19% annual cost difference of € 684/patient. Base case results were robust to varying assumptions for insulin dose, insulin price, change in weight and proportion of ID once daily administrations. Conclusion: IG and ID basal-bolus regimes have comparative safety and efficacy, based on the Hollander study, IG however may represent a significantly more cost saving option for T2D patients in Germany requiring basal-bolus insulin analogue therapy with potential annual cost savings of € 684/patient compared to ID.
On the Acoustics of Emotion in Audio: What Speech, Music, and Sound have in Common

Without doubt, there is emotional information in almost any kind of sound received by humans every day: be it the affective state of a person transmitted by means of speech; the emotion intended by a composer while writing a musical piece, or conveyed by a musician while performing it; or the affective state connected to an acoustic event occurring in the environment, in the soundtrack of a movie, or in a radio play. In the field of affective computing, there is currently some loosely connected research concerning either of these phenomena, but a holistic computational model of affect in sound is still lacking. In turn, for tomorrow’s pervasive technical systems, including affective companions and robots, it is expected to be highly beneficial to understand the affective dimensions of “the sound that something makes,” in order to evaluate the system’s auditory environment and its own audio output. This article aims at a first step toward a holistic computational model: starting from standard acoustic feature extraction schemes in the domains of speech, music, and sound analysis, we interpret the worth of individual features across these three domains, considering four audio databases with observer annotations in the arousal and valence dimensions. In the results, we find that by selection of appropriate descriptors, cross-domain arousal, and valence regression is feasible achieving significant correlations with the observer annotations of up to 0.78 for arousal (training on sound and testing on enacted speech) and 0.60 for valence (training on enacted speech and testing on music). The high degree of cross-domain consistency in encoding the two main dimensions of affect may be attributable to the co-evolution of speech and music from multimodal affect bursts, including the integration of nature sounds for expressive effects.