Miracle-dz/UniFAR-FTUs huggingface dataset:
Noisy Image Segmentation by Modified Snake Model

A novel segmentation scheme for noisy image is proposed. According to the analysis of wavelet denoising method and multiscale geometric analysis techniques, an improved wavelet denoising algorithm combined with multiscale geometric analysis is presented in this paper first. Due to the isotropic nature of wavelet transform, 2D image details are not well represented in wavelet transform, which results in over smoothing. In this new denoising method, a noisy image is processed by the wavelet denoising method first, and then edges' information which has been wrongly discarded, is picked up from the residue image by multiscale geometric analysis. The final denoising image is a combination of the wavelet denoising result and the edges' information. Furthermore, incorporating prior knowledge on the contours' shape and shape similarity metric based on Fourier descriptors of snakes, a parameter-varying snake model is introduced. It addresses the problem of varying parameters during snake method. Extensive experimental results illustrate the excellent performance.
Cardiovascular disease, SCI and exercise: unique risks and focused countermeasures

Purpose. To summarise the spinal cord injury (SCI) specific profile of three cardiovascular disease risk factors (CVD): fasting dyslipidaemia, postprandial lipidaemia and vascular inflammation and to summarise exercise prescriptions that may attenuate each. Method. NA. Results. NA. Conclusions. At least three CVD risk factors have unique profiles in the SCI population. Fasting dyslipidaemia is characterised in the SCI population by depressed HDL cholesterol and normal or low total cholesterol. In the post-prandial state, persons with SCI exhibit an exaggerated triglyceride rise and delayed clearance compared to non-disabled persons. Finally, vascular inflammation, as indexed by C-reactive protein, is markedly elevated in SCI. Exercise may improve each, although the specific prescriptions differs. Fasting dyslipidaemia responds to 8 weeks of moderate intensity aerobic exercise performed 5 days weekly for 30 min daily. Post-prandial lipaemia treatment requires daily moderate or vigorous aerobic exercise, as the effect dissipates day by day. The daily exercise duration is proportional to fitness level, with total caloric expenditure emphasised rather than time. Finally, attenuating vascular inflammation in non-disabled persons requires moderate or vigorous exercise performed for ≥12 months, 5 days weekly for ≥45 min; with aerobic exercise plus resistance training more effective than aerobic exercise alone.
Infant recognition memory: the effects of length of familiarization and type of discrimination task.

FAGAN, JOSEPH F. , III. Infant Recognition Memory: The Effects of Length of Familiarization and Type of Discrimination Task. CmLD DEVELOPMENT, 1974, 45, 351-356. Infant recognition memory, defined by novelty preferences, was found to vary over 4 discrimination tasks as a function of length of familiarization for 5--6-month-old infants. Briefer amounts of familiarization were effective for eliciting recognition than had previously been demonstrated. Order of task difficulty corresponded to age-related differences in ease of discrimination.
Learning and Forgetting: Modeling Optimal Product Sampling Over Time

Firms use samples to increase the sales of almost all consumable goods, including food, health, and cleaning products. Despite its importance, sampling remains one of the most under-researched areas. There are no theoretical quantitative models of sampling behavior other than the pioneering work of Jain et al. (1995), who modeled sampling as an important factor in the diffusion of new products.In this paper we characterize sampling as having two effects. The first is the change in the probability of a consumer purchasing a product immediately after having sampled the product. The second is an increase in the consumer's cumulative goodwill formation, which results from sampling the product. This distinction differentiates our model from other models of goodwill, in which firm sales are only a function of the existing goodwill level.We determine the optimal dynamic sampling effort of a firm and examine the factors that affect the sampling decision. We find that although the sampling effort will decline over a product's life cycle, it may continue in mature products. Another finding is that when we have a positive change in the factors that increase sampling productivity, steady-state goodwill stock and sales will increase, but equilibrium sampling can either increase or decrease. The change in the sampling level is indeterminate because, while increased sampling productivity means that firms have incentives to increase sampling, the increase in the equilibrium goodwill level indirectly reduces the marginal productivity of sampling, thus reducing the incentives to sample. We discuss managerial implications, and how the model can be used to address various circumstances.
Hardware-enhanced access control for cloud computing

Future trustworthy computer systems should provide built-in support for at least the cornerstone security properties of confidentiality, integrity and availability. Access control can help significantly towards achieving this. However, in today's computing landscape, traditional access control implemented only in software may be either insufficient or non-optimal. We discuss some of these situations. Furthermore, fine-grained access control and usage control mechanisms implemented in software are themselves subject to attack, and may impose heavy performance overheads. Can new hardware architecture improve the security achievable by software mechanisms for access control and usage control? If so, what types of hardware support are most useful while retaining the flexibility of software protection mechanisms? What can software do, to help hardware achieve the best results? With the trend towards Cloud Computing, we discuss how new hardware architectural features for cloud servers can help protect the confidentiality and integrity of a cloud customer's code and data in his leased Virtual Machines -- even when the powerful underlying hypervisor may be compromised. This uses a new, non-bypassable form of hardware access control. Without requiring new hardware, we can also leverage the hardware trend towards manycore chips, and the already available hardware virtualization features, to enhance Cloud Security -- but with a few restrictions and some new software support. In general, we would like to motivate collaborations between the software security and the hardware architecture communities to explore software-hardware co-design for security. What comes beyond access control in cloud computing and mobile computing ecosystems? The goal is to design future trustworthy systems that provide security protections, at the levels needed, when needed, even with malware in the system.
No Association Between SOD2 or NQO1 Genotypes and Risk of Bladder Cancer

SOD2 encodes manganese superoxide dismutase, a mitochondrial enzyme that protects the cell against damage from superoxide free radicals. A common polymorphism, a T → C transition (Val16Ala), was shown to alter the manganese superoxide dismutase mitochondrial targeting sequence ([1][1]). Animal
Myocardial ischemia detection with ECG analysis, using Wavelet Transform and Support Vector Machines

In this paper, we propose a novel method for the detection of myocardial ischemic events from electrocardiogram (ECG) signal, using the Discrete Wavelet Transform (DWT) technique and Support Vector Machines (SVM). The ST-T Segment is obtained based on the detection of R peak location based on the well-known Pan-Tompkins method. Then ratio of energy in the DWT approximation coefficients rather than detail coefficients calculated as the features. SVM is used to build classifiers for ischemic and normal ECG signals. The proposed method achieved correct rate of 98.2%, sensitivity of 98.43% and specificity of 99.45%.
Utilizing Patterns in Developing Design Theories

Aside from specialized design artifacts solving 'wicked' problems in a distinct organizational context, prescriptive (design) theories for solving classes of problems are well-accepted results of design science research. Especially for intricate problem fields, as IT service management or EA management, the development of such theories remains a highly complex research endeavor spanning a longer period of time. In this paper, we discuss how patterns, i.e. practice-proven, reusable solutions to recurring problems, can be used to ease the establishment of a design theory, and to provide practice-relevant intermediary results prior to theory completion.
Cause and countermeasure of overtermination for PCN-based flow termination

Pre-congestion notification (PCN) is a packet marking mechanism for DiffServ domains to facilitate feedback-based admission control (AC) and flow termination (FT) for the Internet to achieve quality of service (QoS) for inelastic flows. While AC blocks new flows to prevent an overly high load on network links, FT removes traffic to protect the QoS of the remaining PCN-flows when traffic is rerouted due to any kind of failure. We focus on one of the PCN methods, the controlled load (CL) method, whose edge behavior is now under standardization in the PCN working group of the Internet Engineering Task Force. The CL method terminates a sufficient number of flows in one shot and seems to have accurate control. However, we discovered that more termination than was needed, which we call overtermination, occurred in the CL method. We found that the CL method causes overtermination when not all the ingress-egress pairs have the same round-trip time. A countermeasure is presented to remove this overtermination.
Evaluating the role of genome downsizing and size thresholds from genome size distributions in angiosperms.

PREMISE OF THE STUDY Whole-genome duplications (WGDs) can rapidly increase genome size in angiosperms. Yet their mean genome size is not correlated with ploidy. We compared three hypotheses to explain the constancy of genome size means across ploidies. The genome downsizing hypothesis suggests that genome size will decrease by a given percentage after a WGD. The genome size threshold hypothesis assumes that taxa with large genomes or large monoploid numbers will fail to undergo or survive WGDs. Finally, the genome downsizing and threshold hypothesis suggests that both genome downsizing and thresholds affect the relationship between genome size means and ploidy. METHODS We performed nonparametric bootstrap simulations to compare observed angiosperm genome size means among species or genera against simulated genome sizes under the three different hypotheses. We evaluated the hypotheses using a decision theory approach and estimated the expected percentage of genome downsizing. KEY RESULTS The threshold hypothesis improves the approximations between mean genome size and simulated genome size. At the species level, the genome downsizing with thresholds hypothesis best explains the genome size means with a 15% genome downsizing percentage. In the genus level simulations, the monoploid number threshold hypothesis best explains the data. CONCLUSIONS Thresholds of genome size and monoploid number added to genome downsizing at species level simulations explain the observed means of angiosperm genome sizes, and monoploid number is important for determining the genome size mean at the genus level.
Mothers' experiences of having a premature infant due to pre‐eclampsia

AIM Pre-eclampsia can lead to premature birth aimed at saving mothers' and infants' lives. Few studies have addressed how women with serious pre-eclampsia experience to become mothers to a premature infant. The aim was to describe the phenomenon of mothers' experience of being a seriously ill with pre-eclampsia and on the same time becoming a mother of a premature infant. METHODOLOGY A reflective phenomenological lifeworld approach with a descriptive design was used. Nine mothers were interviewed twice, between 6 and 24 days and 16 days to 16 weeks postpartum. The analysis was done by finding meaning units, and then contemporary clusters were made. The process was a movement between figure and background. The essence with its constituents emerged. FINDINGS Being a seriously ill mother of a premature infant requires journeying through physical and psychological suffering to be able to care and take responsibility for their beloved infants. The essence had the following constituents: conflicting feelings concerning giving birth, reflecting upon the borderline of life, longing for the infant, becoming a mother in actuality and experiencing physical exhaustion. CONCLUSION Because of the acuteness of the birth and the infant's prematurity, the mothers had various feelings about becoming mothers. Existential issues concerning life and death were triggered regarding their own lives and those of their infants. Their experiences are different from those of other NICU mothers; their illnesses initially restricted them from being with their infants. The mothers found that their own health and the fact that their infants were premature and had to stay in the NICU restricted them from being able to take care of their infants. While they recovered, they strived to act as they felt a real mother should.
An Effective PSO-Based Hybrid Algorithm for Multiobjective Permutation Flow Shop Scheduling

This paper proposes a hybrid algorithm based on particle swarm optimization (PSO) for a multiobjective permutation flow shop scheduling problem, which is a typical NP-hard combinatorial optimization problem with strong engineering backgrounds. Not only does the proposed multiobjective algorithm (named MOPSO) apply the parallel evolution mechanism of PSO characterized by individual improvement, population cooperation, and competition to effectively perform exploration but it also utilizes several adaptive local search methods to perform exploitation. First, to make PSO suitable for solving scheduling problems, a ranked-order value (ROV) rule based on a random key technique to convert the continuous position values of particles to job permutations is presented. Second, a multiobjective local search based on the Nawaz-Enscore-Ham heuristic is applied to good solutions with a specified probability to enhance the exploitation ability. Third, to enrich the searching behavior and to avoid premature convergence, a multiobjective local search based on simulated annealing with multiple different neighborhoods is designed, and an adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood will be used. Due to the fusion of multiple different searching operations, good solutions approximating the real Pareto front can be obtained. In addition, MOPSO adopts a random weighted linear sum function to aggregate multiple objectives to a single one for solution evaluation and for guiding the evolution process in the multiobjective sense. Due to the randomness of weights, searching direction can be enriched, and solutions with good diversity can be obtained. Simulation results and comparisons based on a variety of instances demonstrate the effectiveness, efficiency, and robustness of the proposed hybrid algorithm.
Performance Breakdown Effects Dissociate from Error Detection Effects in Typing

Mistakes in skilled performance are often observed to be slower than correct actions. This error slowing has been associated with cognitive control processes involved in performance monitoring and error detection. A limited literature on skilled actions, however, suggests that preerror actions may also be slower than accurate actions. This contrasts with findings from unskilled, discrete trial tasks, where preerror performance is usually faster than accurate performance. We tested 3 predictions about error-related behavioural changes in continuous typing performance. We asked participants to type 100 sentences without visual feedback. We found that (a) performance before errors was no different in speed than that before correct key-presses, (b) error and posterror key-presses were slower than matched correct key-presses, and (c) errors were preceded by greater variability in speed than were matched correct key-presses. Our results suggest that errors are preceded by a behavioural signature, which may indicate breakdown of fluid cognition, and that the effects of error detection on performance (error and posterror slowing) can be dissociated from breakdown effects (preerror increase in variability).
Owen Lattimore, Asia, and Comparative History

Perhaps best known today as a pioneering scholar of Inner Asia and a victim of the McCarthy witch hunts of the 1950s, Owen Lattimore was more basically, like his friend Arnold Toynbee, a major player in the vogue of comparative history that captured wide public attention in the second quarter of the twentieth century. His lifelong intellectual project was to develop a “scientific” model of the way human societies form, evolve, grow, decline, mutate, and interact with one another along “frontiers. ” In the process of working out this model, Lattimore appropriated for his own purposes, and often later discarded, some of the analytic devices most popular in his day, including ecological determinism, biological racism, economic geography and location theory, and Marxist modes of production. At every stage in his thinking, he sought to confound complacent teleologies, both those of Western “progress” and those of Chinese “civilization” of its pastoralist neighbors.
Piece-wise vector fitting method with binary frequency domain partitioning algorithm for automatic modeling

Previously, piecewise vector fitting (PWVF) method for building macro-model from a highly resonant frequency response in a broad range of frequencies has been proposed. The method accurately identifies macro-model by dividing its frequency response into sections, but no specific method for the frequency-domain partitioning has been proposed. To make the entire identification process automatic, a binary algorithm of frequency domain partitioning which is used to dividing the whole frequency band into sections is added into the PWVF. An illustrative example involving a micro-strip line filter is presented for validation of the proposed method.
SENSITIVITY ANALYSIS WITH FINITE-ELEMENT METHOD FOR MICROWAVE DESIGN AND OPTIMIZATION

The thesis proposes a novel method for the computation of the design sensitivity of microwave network parameters. The approach is based on the finite-element method. When combined with the iterative update method (the Broyden method) during the gradient-based optimization process, the approach requires practically no overhead for the computation of the response Jacobian, thus accelerating the optimization. The efficiency and accuracy of the gradient-based optimization and the tolerance analysis greatly depend on the computation of the design sensitivity. However, common commercial full-wave electromagnetic solvers do not provide sensitivity information. With them, the design sensitivities are computed from the response themselves using finite-difference or higher-order approximations at the response level. Consequently, for each design parameter of interest, at least one additional full-wave analysis is performed. The proposed self-adjoint sensitivity analysis (SASA) is so far the most efficient way to extract the sensitivity information for the network parameters with the finite-element method. As an improvement of the adjoint-variable method (A VM), it eliminates the additional system analyses. With one single
Electronic Data Processing in Pediatric EEG Research

Introduction As in so many other fields of biomedical research, the use of a computer has recently become a popular topic of discussion among pediatric electroencephalographers. The progress made during the past two decades in mathematical-statistical analysis of signals and in communication theory on the one side, and advances in electronics and computer techniques on the other side, has opened many ways to perform EEG analysis. Today we dispose of a large variety of more or less sophisticated analytical and quantifying methods together with a large market of electronic systems, which may help to overcome the problems of transforming the data presented by the conventional inkwritten EEG record into a form more apt to an objective treatment. The installation of computing centers in many universities and hospitals and, more recently, the availability of relatively low cost small laboratory computers has brought a new situation. Before this, planning the application of the one or the other analytical method, evaluating the suitable special electronic device, waiting for the financial grant and afterwards for the delivery of the instruments consumed all together an enormous amount of time such that either the method or the hardware or both were likely to be outdated before the work could ever begin.
N-3 PUFAs induce inflammatory tolerance by formation of KEAP1-containing SQSTM1/p62-bodies and activation of NFE2L2

ABSTRACT Inflammation is crucial in the defense against infections but must be tightly controlled to limit detrimental hyperactivation. Our diet influences inflammatory processes and omega-3 polyunsaturated fatty acids (n-3 PUFAs) have known anti-inflammatory effects. The balance of pro- and anti-inflammatory processes is coordinated by macrophages and macroautophagy/autophagy has recently emerged as a cellular process that dampens inflammation. Here we report that the n-3 PUFA docosahexaenoic acid (DHA) transiently induces cytosolic speckles of the autophagic receptor SQSTM1/p62 (sequestosome 1) (described as SQSTM1/p62-bodies) in macrophages. We suggest that the formation of SQSTM1/p62-bodies represents a fast mechanism of NFE2L2/Nrf2 (nuclear factor, erythroid 2 like 2) activation by recruitment of KEAP1 (kelch like ECH associated protein 1). Further, the autophagy receptor TAX1BP1 (Tax1 binding protein 1) and ubiquitin-editing enzyme TNFAIP3/A20 (TNF α induced protein 3) could be identified in DHA-induced SQSTM1/p62-bodies. Simultaneously, DHA strongly dampened the induction of pro-inflammatory genes including CXCL10 (C-X-C motif chemokine ligand 10) and we suggest that formation of SQSTM1/p62-bodies and activation of NFE2L2 leads to tolerance towards selective inflammatory stimuli. Finally, reduced CXCL10 levels were related to the improved clinical outcome in n-3 PUFA-supplemented heart-transplant patients and we propose CXCL10 as a robust marker for the clinical benefits mobilized by n-3 PUFA supplementation.