Miracle-dz/UniFAR-FTUs huggingface dataset:
Guest Editorial - Multirow Detector and Cone-Beam Spiral/Helical CT

S PIRAL/HELICAL multirow detector computed tomog-raphy (MDCT), also referred to as multislice spiral CT (MSCT), has attracted a major interest since its recent introduction [18], [33]. Compared with single-row-detector systems, MDCT scanners allow faster data collection and thinner slices that support more demanding clinical applications and present new research opportunities. This special issue consists of 12 original papers from this rapidly expanding field. Although MDCT generally refers to commercial CT scanners used by radiologists, the work in this issue is also applicable to emission CT as well as nonmedical applications of X-ray CT, including nondestructive testing and microtomography. In this editorial, we provide a summary of the special issue, an overview of the field, and suggestions for future work. The first eight papers represent recent work on X-ray CT image reconstruction in multirow detector or cone-beam geometry. The first two papers by Schaller et al.report an adap-tive axial interpolation theory for a MDCT scanner with experimental verification [13], [29]. Adaptive axial interpolation provides increased flexibility for image reconstruction in clinical practice. By keeping slice thickness and image noise independent of pitch, the MDCT parameter selection is simplified. The paper by Proksa et al. introduces the-PI-method for spiral/helical cone-beam CT (CBCT) [27]. This method assumes a detector array shaped by a helix, permits variable pitch, and is amendable to both exact and approximate reconstruction. The paper by Hsieh addresses the degradation in image quality of spiral/helical MDCT when the gantry is tilted to produce oblique sections [17]. He formulated a model as an analytical basis and developed several compensation schemes. The paper by Bruder et al. describes two different approximate single-slice algorithms for CBCT: multirow Fourier reconstruction (MFR) and advanced single-slice rebinning (ASSR), in the framework of the generalized parallel projection using-filtering [3]. Their studies contain data valuable for the design of spiral/helical CT scanners with medium cone-beam angles. The paper by Kachel-rieß et al. proposes MDCT algorithms for cardiac imaging [19]. Based on ECG signals, the two dedicated cardiac reconstruction algorithms improve image quality compared with the standard reconstruction algorithms. The paper by Kudo et al. [21] deals with the long-object problem; that is, to reconstruct a region of interest (ROI) of a long object from data collected along a helical path that extends only marginally above and below the ROI. Their quasi-exact reconstruction algorithms require no Publisher Item Identifier S 0278-0062(00)09415-5. additional circular scans and are in the filtered …
Human language acquisition methods in a machine learning task

The goal of this study is to develop a psycho-computational model of human phoneme acquisition that includes the knowledge of linguistic universals [1, 2, 3] to \teach" Arti(cid:12)cial Neural Nets incrementally. Long Short-Term Memory (LSTM) arti(cid:12)cial neural networks are capable to outperform previous recurrent networks on many tasks ranging from grammar recognition to speech [4] and robot control [5]. Together with our psycho-computational model they are supposed to recognize phonetic features in a way similar to humans learning to understand their (cid:12)rst language.
Assessing Genomic Selection Prediction Accuracy in a Dynamic Barley Breeding Population

Prediction accuracy of genomic selection (GS) has been previously evaluated through simulation and cross‐validation; however, validation based on progeny performance in a plant breeding program has not been investigated thoroughly. We evaluated several prediction models in a dynamic barley breeding population comprised of 647 six‐row lines using four traits differing in genetic architecture and 1536 single nucleotide polymorphism (SNP) markers. The breeding lines were divided into six sets designated as one parent set and five consecutive progeny sets comprised of representative samples of breeding lines over a 5‐yr period. We used these data sets to investigate the effect of model and training population composition on prediction accuracy over time. We found little difference in prediction accuracy among the models confirming prior studies that found the simplest model, random regression best linear unbiased prediction (RR‐BLUP), to be accurate across a range of situations. In general, we found that using the parent set was sufficient to predict progeny sets with little to no gain in accuracy from generating larger training populations by combining the parent set with subsequent progeny sets. The prediction accuracy ranged from 0.03 to 0.99 across the four traits and five progeny sets. We explored characteristics of the training and validation populations (marker allele frequency, population structure, and linkage disequilibrium, LD) as well as characteristics of the trait (genetic architecture and heritability, H2). Fixation of markers associated with a trait over time was most clearly associated with reduced prediction accuracy for the mycotoxin trait DON. Higher trait H2 in the training population and simpler trait architecture were associated with greater prediction accuracy.
Prediction of material strength and fracture of glass using the SPHINX smooth particle hydrodynamics code

The design of many military devices involves numerical predictions of the material strength and fracture of brittle materials. The materials of interest include ceramics, that are used in armor packages; glass that is used in truck and jeep windshields and in helicopters; and rock and concrete that are used in underground bunkers. As part of a program to develop advanced hydrocode design tools, the authors have implemented a brittle fracture model for glass into the SPHINX smooth particle hydrodynamics code. The authors have evaluated this model and the code by predicting data from one-dimensional flyer plate impacts into glass, and data from tungsten rods impacting glass. Since fractured glass properties, which are needed in the model, are not available, the authors did sensitivity studies of these properties, as well as sensitivity studies to determine the number of particles needed in the calculations. The numerical results are in good agreement with the data.
Wind-wave amplification mechanisms: possible models for steep wave events in finite depth

Abstract. We extend the Miles mechanism of wind-wave generation to finite depth. A β-Miles linear growth rate depending on the depth and wind velocity is derived and allows the study of linear growth rates of surface waves from weak to moderate winds in finite depth h. The evolution of β is plotted, for several values of the dispersion parameter kh with k the wave number. For constant depths we find that no matter what the values of wind velocities are, at small enough wave age the β-Miles linear growth rates are in the known deep-water limit. However winds of moderate intensities prevent the waves from growing beyond a critical wave age, which is also constrained by the water depth and is less than the wave age limit of deep water. Depending on wave age and wind velocity, the Jeffreys and Miles mechanisms are compared to determine which of them dominates. A wind-forced nonlinear Schrodinger equation is derived and the Akhmediev, Peregrine and Kuznetsov–Ma breather solutions for weak wind inputs in finite depth h are obtained.
The Interactive Effect of Birth Weight and Parental Investment on Child Test Scores

This paper explores how observed and unobserved parental investments compensate for low birth weight. Controlling for family fixed effects, which encompass unobserved parental investment, we find birth weight positively correlates with math and reading scores and these estimates are considerably larger in magnitude than estimates derived from models that do not control for family fixed effects. Additionally, we examine how three specific parental investments - kindergarten entrance age, maternal labor supply, and family size - interact with birth weight in models of child test scores. Of these investments, only smaller family size conveys particular advantage to low birth weight children.
Correction: Chang, C.W., et al. Development of a Three Dimensional Neural Sensing Device by a Stacking Method. Sensors 2010, 10, 4238–4252

This study reports a stacking method for assembling a 3-D microprobe array. To date, various 3D array structures have been reported with complex assembly steps, vertical interconnection for 3-D signal transmission which may suffer from low structure strength and large implantable opening. By applying the proposed stacking method, the previous problems were no longer existed. Also, ASIC chips could be substituted for the spacers in the stacked arrays achieving system integration, design flexibility and volume usage efficiency. To avoid overflow of the adhesive fluid during assembly, an anti-overflow design which made use of capillary action force was applied in the stacking method as well. Presented stacking procedure consumes only 35 minutes in average for a 4 × 4 3D microprobe array without requiring specially made assembly tools. The advantages of the proposed stacking method for 3D array assembly include simplified assembly process, high structure strength, smaller opening area and integration ability with active circuits. This stacking assembly technique allows an alternative method to create 3-D structures from planar components.
Smart Driver Monitoring: When Signal Processing Meets Human Factors: In the driver's seat

This article provides an interdisciplinary perspective on driver monitoring systems by discussing state-of-the-art signal processing solutions in the context of road safety issues identified in human factors research. Recently, the human factors community has made significant progress in understanding driver behaviors and assessed the efficacy of various interventions for unsafe driving practices. In parallel, the signal processing community has had significant advancements in developing signal acquisition and processing methods for driver monitoring systems. This article aims to bridge these efforts and help initiate new collaborations across the two fields. Toward this end, we discuss how vehicle measures, facial/body expressions, and physiological signals can assist in improving driving safety through adaptive interactions with the driver, based on the driver's state and driving environment. Moreover, by highlighting the current human factors research in road safety, we provide insights for building feedback and mitigation technologies, which can act both in real time and postdrive. We provide insights into areas with great potential to improve driver monitoring systems, which have not yet been extensively studied in the literature, such as affect recognition and data fusion. Finally, a high-level discussion is given on the challenges and possible future directions for driver monitoring systems.
An MMSE Based Signal to Leakage Plus Noise Ratio Precoding Scheme with Other Cell Interference

In this paper, we consider the problem of interference suppression by means of precoding method in multiuser MIMO (MU-MIMO) downlink communications. Compared with the conventional algorithms such as Block Diagonalization in the Presence of Other Cell Interference (BD-POCI) and Block Diagonalization with Signal to Leakage plus Noise Ratio (BD-SLNR) solutions, the proposed scheme keeps the merit of SLNR and Block Diagonalization (BD) while overcoming their shortcomings in error rate performance. Simulation results demonstrate that the proposed scheme achieves better error rate performance while maintaining much the same achievable sum-rate.
Correlation of magnetization transfer ration with clinical disability in multiple sclerosis

We performed spin echo magnetic resonance imaging with and without application of an off‐resonance saturation pulse in 43 patients with multiple selerosis (MS), 10 age‐matched controls, and 4 elderly asymptomatic patients with the radiological diagnosis of small‐vessel disease. Magnetization transfer (MT) ration images were obtained from these. All MS subgroups (primary progressive, secondary progressive, benign, early relapsing–remitting) showed significantly lower average lesion MT ratios than small‐vessel disease patients. Secondary progressive MS patients showed significantly lower lesion MT ratios than those with benign disease, and there was an inverse correlaion of disability with average lesion MT ratio. The degree of reduction of MT ratios is an indicator of the extent of tissue destruction. Thus, reduced MT ratios in MS may provide an indication of the degree of demyelination and axonal loss, both of which are likely to cause functional deficits in MS. We conclude that MT measurement is (1) a robust quantitative method that may increase the pathological specificity of magnetic resonance imaging, (2) has the potential to differentiate demyelination in MS from less destructive pathological changes, and (3) may be useful in monitoring modifications in tissue structure brought about by treatment.
On MDS extensions of generalized Reed-Solomon codes

An (n, k, d) linear code over F= GF (q) is said to be {\em maximum distance separable} (MDS) if d = n - k + 1 . It is shown that an (n, k, n - k + 1) generalized Reed-Solomon code such that 2\leq k \leq n - \lfloor (q - 1)/2 \rfloor (k \neq 3 {\rm if} q is even) can be extended by one digit while preserving the MDS property if and only if the resulting extended code is also a generalized Reed-Solomon code. It follows that a generalized Reed-Solomon code with k in the above range can be {\em uniquely} extended to a maximal MDS code of length q + 1 , and that generalized Reed-Solomon codes of length q + 1 and dimension 2\leq k \leq \lfloor q/2 \rfloor + 2 (k \neq 3 {\rm if} q is even) do not have MDS extensions. Hence, in cases where the (q + 1, k) MDS code is essentially unique, (n, k) MDS codes with n > q + 1 do not exist.
A Utility-Based Approach to Intention Recognition

Based on the assumption that a rational agent will adopt a plan that maximizes the expected utility, we present a utility-based approach to plan recognition problem in this paper. The approach explicitly takes the observed agent’s preferences into consideration, and computes the estimated expected utilities of plans to disambiguate competing hypotheses. Online plan recognition is realized by incrementally using plan knowledge and observations to change state probabilities. We also discuss the work and compare it with other probabilistic models in the paper.
Impact of Natural Genetic Variation on Gene Expression Dynamics

DNA sequence variation causes changes in gene expression, which in turn has profound effects on cellular states. These variations affect tissue development and may ultimately lead to pathological phenotypes. A genetic locus containing a sequence variation that affects gene expression is called an “expression quantitative trait locus” (eQTL). Whereas the impact of cellular context on expression levels in general is well established, a lot less is known about the cell-state specificity of eQTL. Previous studies differed with respect to how “dynamic eQTL” were defined. Here, we propose a unified framework distinguishing static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. Further, we introduce a new approach to simultaneously infer eQTL from different cell types. By using murine mRNA expression data from four stages of hematopoiesis and 14 related cellular traits, we demonstrate that static, conditional and dynamic eQTL, although derived from the same expression data, represent functionally distinct types of eQTL. While static eQTL affect generic cellular processes, non-static eQTL are more often involved in hematopoiesis and immune response. Our analysis revealed substantial effects of individual genetic variation on cell type-specific expression regulation. Among a total number of 3,941 eQTL we detected 2,729 static eQTL, 1,187 eQTL were conditionally active in one or several cell types, and 70 eQTL affected expression changes during cell type transitions. We also found evidence for feedback control mechanisms reverting the effect of an eQTL specifically in certain cell types. Loci correlated with hematological traits were enriched for conditional eQTL, thus, demonstrating the importance of conditional eQTL for understanding molecular mechanisms underlying physiological trait variation. The classification proposed here has the potential to streamline and unify future analysis of conditional and dynamic eQTL as well as many other kinds of QTL data.
Profile Implementation and Service Adaptation within an InfoStation-Based mLearning Environment

This paper deals with the creation and utilization of user profiles and user service profiles within an InfoStation based mobile eLearning (mLearning) environment. The underlying technologies are paid particular attention. A description of a generic mLearning service along with sample interactions among the involved system entities is provided. Various profile attributes affecting the service adaptation for different operating environments are also examined.
NW-based Particle Swarm Optimization

Topology structure is a key issue for the performance of particle swarm optimization (PSO). A good structure selection may provide more chance to escape from a local optimum. In this paper, a new topology structure – Newman and Watts model is introduced to increase the performance particle swarm optimization and is called NWPSO. In NWPSO, the topology is changed according to Newman and Watts rules within the whole evolutionary process. Simulation results show the proposed algorithm is superior to other three variants of PSO.
A Model of Discourse Predictions in Human Sentence Processing

This paper introduces a psycholinguistic model of sentence processing which combines a Hidden Markov Model noun phrase chunker with a co-reference classifier. Both models are fully incremental and generative, giving probabilities of lexical elements conditional upon linguistic structure. This allows us to compute the information theoretic measure of surprisal, which is known to correlate with human processing effort. We evaluate our surprisal predictions on the Dundee corpus of eye-movement data show that our model achieve a better fit with human reading times than a syntax-only model which does not have access to co-reference information.
The Voice of Anger: Oscillatory EEG Responses to Emotional Prosody

Emotionally relevant stimuli and in particular anger are, due to their evolutionary relevance, often processed automatically and able to modulate attention independent of conscious access. Here, we tested whether attention allocation is enhanced when auditory stimuli are uttered by an angry voice. We recorded EEG and presented healthy individuals with a passive condition where unfamiliar names as well as the subject’s own name were spoken both with an angry and neutral prosody. The active condition instead, required participants to actively count one of the presented (angry) names. Results revealed that in the passive condition the angry prosody only elicited slightly stronger delta synchronization as compared to a neutral voice. In the active condition the attended (angry) target was related to enhanced delta/theta synchronization as well as alpha desynchronization suggesting enhanced allocation of attention and utilization of working memory resources. Altogether, the current results are in line with previous findings and highlight that attention orientation can be systematically related to specific oscillatory brain responses. Potential applications include assessment of non-communicative clinical groups such as post-comatose patients.
You Can Pick Your Friends, But You Need to Watch Them: Loan Screening and Enforcement in a Referrals Field Experiment

We examine a randomized trial that allows separate identification of peer screening and enforcement of credit contracts. A South African microlender offered half its clients a bonus for referring a friend who repaid a loan. For the remaining clients, the bonus was conditional on loan approval. After approval, the repayment incentive was removed from half the referrers in the first group and added for half those in the second. We find large enforcement effects, a $12 (100 Rand) incentive reduced default by 10 percentage points from a base of 20%. In contrast, we find no evidence of screening.
Motion segmentation using Markov random field model for accurate moving object segmentation

In this paper, we propose a new motion segmentation algorithm for accurate moving object segmentation. The proposed algorithm is elaborately designed based on Markov random field (MRF) model. When motion decision is performed on each pixel in images by detection theory, false decisions such as miss and false alarm are occurred. These false decisions cause segmentation errors of moving objects. We remove these errors by using MRF model based on Bayes rule. By means of experiments on test sequences, we demonstrate that our algorithm achieves good performance with respect to the segmentation accuracy.
Molecular Dynamics Analysis for Deformation and Fracture Behavior of Copper Thin Films

Molecular dynamics simulation is conducted to investigate the effect of notch depth on the deformation and fracture behavior of a single crystal copper which is expected to a conductive material of micro-devices. In the stress – strain relationship, a normal stress increases with increasing applied strain. Then, the normal stress decreases rapidly. When the stress decreases, the dislocation emits from a notch root and the stacking fault is formed on the {111} plane, which is slip plane of the fcc crystalline structure. The maximum stress decreases with notch depth. The non-damaging defect size is quite small. The shear stress in the slip direction at dislocation emission is constant irrespective of the notch depth. The criterion of the dislocation emission is given by the critical value of the resolved shear stress in the sliding direction.
Security considerations for equipment controllers and SDN

The role of the equipment controller is changing in the Software-Defined Network (SDN). In the past, the role of the controller was focused on providing alarms and status information. In the Software-Defined Network, the controller provides the alarming and status with real-time information about the available capacity and historical information about energy use. With the larger role of equipment controllers and the reality of security threats, it is important to leverage defense-in-depth, protocol security, and a reduction in the controller's attack surface to keep the SDN secure and operating properly. This paper examines these aspects of controller security in an SDN environment.
Explaining the outcome of knowledge-based systems : A discussion-based approach

Many inferences made in everyday life are only valid in the absence of explicit counter information. This has led to the development of nonmonotonic logics. The kind of reasoning performed by these logics can be difficult to explain to the average end-user of a knowledge based system that implements them. Although the system can still give advice, it is hard for the user to assess the rationale be- hind this advice. In this paper we propose an argumentation approach that enables the advice to be assessed through an interactive dialogue with the system much like the discussion one might have with a col- league. The aim of thie dialogue is for the system to convince the user that the advice is well-founded.