Miracle-dz/UniFAR-FTUs huggingface dataset:
Tool Support for Software Architecture Design with Genetic Algorithms

Automated support for software architecture design is discussed. The proposed approach is based on a tool applying genetic algorithms for producing potential architecture proposals. The tool requires a basic functional decomposition of the system and the specification of the quality requirements as input, relying on a repository of standard solutions like patterns and architectural styles. The underlying techniques and the design of the tool are discussed, and the usage of the tool is illustrated by an example.
Emerging Transcriptional Mechanisms in the Regulation of Epithelial to Mesenchymal Transition and Cellular Plasticity in the Kidney

Notwithstanding controversies over the role of epithelial to mesenchymal transition in the pathogenesis of renal disease, the last decade has witnessed a revolution in our understanding of the regulation of renal cell plasticity. Significant parallels undoubtedly exist between ontogenic processes and the initiation and propagation of damage in the diseased kidney as evidenced by the reactivation of developmental programmes of gene expression, in particular with respect to TGFβ superfamily signaling. Indeed, multiple signaling pathways converge on a complex transcriptional regulatory nexus that additionally involves epigenetic activator and repressor mechanisms and microRNA regulatory networks that control renal cell plasticity. It is becoming increasingly apparent that differentiated cells can acquire an undifferentiated state akin to “stemness” which is leading us towards new models of complex cell behaviors and interactions. Here we discuss the latest findings that delineate new and novel interactions between this transcriptional regulatory network and highlight a hitherto poorly recognized role for the Polycomb Repressive Complex (PRC2) in the regulation of renal cell plasticity. A comprehensive understanding of how external stimuli interact with the epigenetic control of gene expression, in normal and diseased contexts, establishes a new therapeutic paradigm to promote the resolution of renal injury and regression of fibrosis.
How do “robopets” impact the health and well‐being of residents in care homes? A systematic review of qualitative and quantitative evidence

Abstract Background Robopets are small animal‐like robots which have the appearance and behavioural characteristics of pets. Objective To bring together the evidence of the experiences of staff, residents and family members of interacting with robopets and the effects of robopets on the health and well‐being of older people living in care homes. Design Systematic review of qualitative and quantitative research. Data sources We searched 13 electronic databases from inception to July 2018 and undertook forward and backward citation chasing. Methods Eligible studies reported the views and experiences of robopets from residents, family members and staff (qualitative studies using recognised methods of qualitative data collection and analysis) and the effects of robopets on the health and well‐being of care home residents (randomised controlled trials, randomised crossover trials and cluster randomised trials). Study selection was undertaken independently by two reviewers. We used the Wallace criteria and the Cochrane Risk of Bias tool to assess the quality of the evidence. We developed a logic model with stakeholders and used this as a framework to guide data extraction and synthesis. Where appropriate, we used meta‐analysis to combine effect estimates from quantitative studies. Results Nineteen studies (10 qualitative, 2 mixed methods and 7 randomised trials) met the inclusion criteria. Interactions with robopets were described as having a positive impact on aspects of well‐being including loneliness, depression and quality of life by residents and staff, although there was no corresponding statistically significant evidence from meta‐analysis for these outcomes. Meta‐analysis showed evidence of a reduction in agitation with the robopet “Paro” compared to control (−0.32 [95% CI −0.61 to −0.04, p = 0.03]). Not everyone had a positive experience of robopets. Conclusions Engagement with robopets appears to have beneficial effects on the health and well‐being of older adults living in care homes, but not all chose to engage. Whether the benefits can be sustained are yet to be investigated. Implications for practice Robopets have the potential to benefit people living in care homes, through increasing engagement and interaction. With the robopet acting as a catalyst, this engagement and interaction may afford comfort and help reduce agitation and loneliness.
Risk Factors for Primary Arteriovenous Fistula Dysfunction in Hemodialysis Patients: A Retrospective Survival Analysis in Multiple Medical Centers

Background: Arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis (HD). However, primary AVF dysfunction represents a major barrier to the long-term success of HD therapy. This study aims to analyze the variables that influence the incidence of first AVF failure in HD patients. Methods: From January 2012 to October 2016, a total of 100 HD subjects from 43 medical centers were enrolled for a retrospective survival analysis of AVF dysfunction. To diminish the potential influence of surgeon experiences, the same operation group in Second Xiangya Hospital performed all studied AVF placements. This study focuses on a Chinese population of idiopathic glomerular disease to avoid the secondary influence of other systemic diseases, including diabetes, hypertension, and autoimmune disorder. AVF dysfunction was defined as lower blood flow during dialysis (≤200 mL/min) or insufficiency of HD treatment caused by reduced blood flow. Results: Among all enrolled subjects, the incidence of AVF dysfunction due to impatency was 27% (n = 27) with a cumulative survival of 84.0, 73.1, and 71.6% in 6, 12, and 24 months of post-placement. AVF survival analysis revealed a higher incidence of AVF failure in females (p= 0.025) and elderly (p = 0.031) patients. Importantly, AVF dysfunction markedly increased in subjects with higher levels of platelets (PLTs; p = 0.024), severe anemia (p = 0.014), and extended temporary catheter retention (p = 0.020). Further multivariate Cox regression analysis confirmed these variables as independent risk factors for first AVF dysfunction. Meanwhile, no significant difference could be observed according to the levels of body mass index, serum albumin, serum calcium, serum phosphorus, prothrombin time, and activated partial thromboplastin time. Lastly, anti-coagulant treatments seemed to barely influence the outcomes of AVF survival in this study. Conclusion: These findings suggest that primary AVF dysfunction in HD patients is associated with gender, ageing, PLT counting number, hemoglobin level, and retention time of temporary catheter.
An Adapted Optical Flow Algorithm for Robust Quantification of Cardiac Wall Motion From Standard Cine-MR Examinations

This paper presents a method for local myocardial motion estimation from a conventional steady-state free precession cine-MRI sequence using a modified phase-based optical flow (OF) technique. Initially, the technique was tested on synthetic images to evaluate its robustness with regards to Rician noise and to brightness variations. The method was then applied to cardiac images acquired on 11 healthy subjects. Myocardial velocity is measured in centimeter per second in each studied pixel and visualized as colored vectors superimposed on MRI images. The estimated phase-based OF results were compared with a reference OF method and gave similar results on synthetic images, i.e., without a significant difference of the mean angular error. Applied on cine-MRI of normal hearts, the calculated velocities from short-axis images concord with values obtained in the literature. The advantage of the presented method is its robustness with respect to Rician noise and to brightness changes often observed in cine-MRI sequences, and especially with the through-plane movement of the heart. Motion assessment using our method on cine-MR images gives promising results on motion estimation on a pixel-by-pixel basis, leading to a regional measurement of the time-velocity course of myocardial displacement in different segments of the heart wall.
A new variable---length genome genetic algorithm for data clustering in semeiotics

This paper focuses on the introduction of a new evolutionary algorithm for data clustering, the Self-sizing Genome Genetic Algorithm. It is akin to a messy Genetic Algorithm and does not use a priori information about the number of clusters. A new recombination operator, gene-pooling, is introduced, while fitness is based on simultaneously maximizing intra-cluster homogeneity and inter-cluster separability. This algorithm is applied to clustering in dermatological semeiotics. Moreover, a Pathology Addressing Index is defined to quantify utility of found clusters in unambiguously addressing towards pathologies. Comparison with other clustering tools is performed.
Analyzing Adaptive Cache Replacement Strategies

Adaptive Replacement Cache (ARC) and CLOCK with Adaptive Replacement (CAR) are state-of-the- art "adaptive" cache replacement algorithms invented to improve on the shortcomings of classical cache replacement policies such as LRU, LFU and CLOCK. By separating out items that have been accessed only once and items that have been accessed more frequently, both ARC and CAR are able to control the harmful effect of single-access items flooding the cache and pushing out more frequently accessed items. Both ARC and CAR have been shown to outperform their classical and popular counterparts in practice. Both algorithms are complex, yet popular. Even though they can be treated as online algorithms with an "adaptive" twist, a theoretical proof of the competitiveness of ARC and CAR remained unsolved for over a decade. We show that the competitiveness ratio of CAR (and ARC) has a lower bound of N + 1 (where N is the size of the cache) and an upper bound of 18N (4N for ARC). If the size of cache offered to ARC or CAR is larger than the one provided to OPT, then we show improved competitiveness ratios. The important implication of the above results are that no "pathological" worst-case request sequences exist that could deteriorate the performance of ARC and CAR by more than a constant factor as compared to LRU.
EFFECTS OF POPULATION SIZE AND MUTATION RATE ON THE EVOLUTION OF MUTATIONAL ROBUSTNESS

Abstract It is often assumed that the efficiency of selection for mutational robustness would be proportional to mutation rate and population size, thus being inefficient in small populations. However, Krakauer and Plotkin (2002) hypothesized that selection in small populations would favor robustness mechanisms, such as redundancy, that mask the effect of deleterious mutations. In large populations, by contrast, selection is more effective at removing deleterious mutants and fitness would be improved by eliminating mechanisms that mask the effect of deleterious mutations and thus impede their removal. Here, we test whether these predictions are supported in experiments with evolving populations of digital organisms. Digital organisms are self-replicating programs that inhabit a virtual world inside a computer. Like their organic counterparts, digital organisms mutate, compete, evolve, and adapt by natural selection to their environment. In this study, 160 populations evolved at different combinations of mutation rate and population size. After 104 generations, we measured the mutational robustness of the most abundant genotype in each population. Mutational robustness tended to increase with mutation rate and to decline with population size, although the dependence with population size was in part mediated by a negative relationship between fitness and robustness. These results are independent of whether genomes were constrained to their original length or allowed to change in size.
Evaluation of Embodied Conversational Agents

This paper proposes a platform which allows to evaluate the performance of an Embodied Conversational Agent and the influence of several distinct multimedia elements. Using the “Imitation Game” as described by Alan Turing as a starting point and motivation, the platform allows the configuration of several features which influence the Virtual environment. Through a series of user tests and surveys that are performed using the platform, it is possible to gather data that helps measure the efficiency of each feature.
Recognizing Banknote Fitness with a Visible Light One Dimensional Line Image Sensor

In general, dirty banknotes that have creases or soiled surfaces should be replaced by new banknotes, whereas clean banknotes should be recirculated. Therefore, the accurate classification of banknote fitness when sorting paper currency is an important and challenging task. Most previous research has focused on sensors that used visible, infrared, and ultraviolet light. Furthermore, there was little previous research on the fitness classification for Indian paper currency. Therefore, we propose a new method for classifying the fitness of Indian banknotes, with a one-dimensional line image sensor that uses only visible light. The fitness of banknotes is usually determined by various factors such as soiling, creases, and tears, etc. although we just consider banknote soiling in our research. This research is novel in the following four ways: first, there has been little research conducted on fitness classification for the Indian Rupee using visible-light images. Second, the classification is conducted based on the features extracted from the regions of interest (ROIs), which contain little texture. Third, 1-level discrete wavelet transformation (DWT) is used to extract the features for discriminating between fit and unfit banknotes. Fourth, the optimal DWT features that represent the fitness and unfitness of banknotes are selected based on linear regression analysis with ground-truth data measured by densitometer. In addition, the selected features are used as the inputs to a support vector machine (SVM) for the final classification of banknote fitness. Experimental results showed that our method outperforms other methods.
The Impact of Gender on Interferon-Associated Depression and Anxiety.

Population studies indicate women have higher prevalences of depression and anxiety than men. Interferon (IFN) is a biologic agent that can induce or exacerbate depression and/or anxiety. Whether women are more likely to experience these side effects of IFN during treatment remains to be determined. The aim of this study was to document levels of depression and anxiety in female and male patients before and during IFN-based treatment. This was a prospective open-label study in which depression was measured by Beck Depression Inventory (BDI) and anxiety by Hospital Anxiety and Depression Scale (HADS). Before treatment, the prevalence of depression was higher in females (3/13 [23%]) than males (1/25 [4%]), but the difference did not reach statistical significance (P = 0.12). Initial BDI scores were also higher in females but not significantly (P = 0.07). During treatment, BDI scores increased to a similar extent in both genders. A similar percentage of nondepressed patients at baseline developed depression (females: 50% versus males: 35%, P = 0.45). Before treatment, anxiety was significantly more common in females (7/13 [54%]) than males (3/25 [12%]) (P = 0.016) and median HADS scores were higher in females (P = 0.03). During treatment, increases in HADS scores were similar in the 2 genders. A similar percentage of patients without anxiety at baseline developed anxiety on treatment (females: 50% versus males: 23%, P = 0.31). The frequency and extent of IFN-induced/exacerbated depression and anxiety are not gender dependent.
Complementary Metamaterial Sensor for Nondestructive Evaluation of Dielectric Substrates

In this paper, complementary metamaterial sensor is designed for nondestructive evaluation of dielectric substrates. The design concept is based on electromagnetic stored energy in the complementary circular spiral resonator (CCSR), which is concentrated in small volume near the host substrate at resonance. This energy can be employed to detect various electromagnetic properties of materials under test (MUT). Effective electric permittivity and magnetic permeability of the proposed sensor is extracted from scattering parameters. Sensitivity analysis is performed by varying the permittivity of MUT. After sensitivity analysis, a sensor is fabricated using standard PCB fabrication technique, and resonance frequency of the sensor due to interaction with different MUT is measured using vector network analyzer (AV3672series). The transcendental equation is derived for the fabricated sensor to calculate relative permittivity for unknown MUTs. This method is very simple and requires calculating only the resonant frequency, which reduces the cost and computation time.
Sphingosine-1-phosphate receptor 1 activation in astrocytes contributes to neuropathic pain

Significance Understanding the cellular and molecular pathways of neuropathic pain is fundamental to discovering classes of non-opioid analgesics. Emerging evidence implicates altered sphingolipid metabolism and elevated levels of sphingosine-1-phosphate (S1P) in the spinal cord in the persistence of neuropathic pain, but its neuropharmacology and pathways engaged are poorly understood. Using a multidisciplinary approach, we provide evidence that S1P drives neuropathic pain by selectively activating the S1P receptor subtype 1 (S1PR1) in astrocytes. Inhibiting S1PR1 attenuated and reversed neuropathic pain by engaging the potent anti-inflammatory cytokine, interleukin 10. Our findings represent a paradigm shift in the understanding of S1PR1 signaling that has implications on the future development of S1PR1 antagonists as a promising class of nonnarcotic analgesics. Neuropathic pain afflicts millions of individuals and represents a major health problem for which there is limited effective and safe therapy. Emerging literature links altered sphingolipid metabolism to nociceptive processing. However, the neuropharmacology of sphingolipid signaling in the central nervous system in the context of chronic pain remains largely unexplored and controversial. We now provide evidence that sphingosine-1-phosphate (S1P) generated in the dorsal horn of the spinal cord in response to nerve injury drives neuropathic pain by selectively activating the S1P receptor subtype 1 (S1PR1) in astrocytes. Accordingly, genetic and pharmacological inhibition of S1PR1 with multiple antagonists in distinct chemical classes, but not agonists, attenuated and even reversed neuropathic pain in rodents of both sexes and in two models of traumatic nerve injury. These S1PR1 antagonists retained their ability to inhibit neuropathic pain during sustained drug administration, and their effects were independent of endogenous opioid circuits. Moreover, mice with astrocyte-specific knockout of S1pr1 did not develop neuropathic pain following nerve injury, thereby identifying astrocytes as the primary cellular substrate of S1PR1 activity. On a molecular level, the beneficial reductions in neuropathic pain resulting from S1PR1 inhibition were driven by interleukin 10 (IL-10), a potent neuroprotective and anti-inflammatory cytokine. Collectively, our results provide fundamental neurobiological insights that identify the cellular and molecular mechanisms engaged by the S1PR1 axis in neuropathic pain and establish S1PR1 as a target for therapeutic intervention with S1PR1 antagonists as a class of nonnarcotic analgesics.
Technology trends survey: Measurement support in software engineering environments

The use of empirical data to understand and improve software products and software engineering processes is gaining ever increasing attention. Empirical data from products and processes is needed to help organization understand and improve its way of doing business in the software domain. Additional motivation for collecting and using data is provided by the need to conform to guidelines and standards which mandate measurement, specifically the SEI’s Capability Maturity Model and ISO 9000-3. Some software engineering environments (SEEs) offer automated support for collecting and, in a few cases, using empirical data. Measurement will clearly play a significant role in future SEEs. The paper surveys the trend towards supporting measurement in SEEs and gives details about several existing research and Funding for the Software Technology Transfer Initiative Kaiserslautern was provided in part by the Ministry of Commerce and Transport (BMWV) of the State of the RheinlandPalatinate, Germany. commercial software systems.
Multiple-stage decisions in a marine central-place forager

Air-breathing marine animals face a complex set of physical challenges associated with diving that affect the decisions of how to optimize feeding. Baleen whales (Mysticeti) have evolved bulk-filter feeding mechanisms to efficiently feed on dense prey patches. Baleen whales are central place foragers where oxygen at the surface represents the central place and depth acts as the distance to prey. Although hypothesized that baleen whales will target the densest prey patches anywhere in the water column, how depth and density interact to influence foraging behaviour is poorly understood. We used multi-sensor archival tags and active acoustics to quantify Antarctic humpback whale foraging behaviour relative to prey. Our analyses reveal multi-stage foraging decisions driven by both krill depth and density. During daylight hours when whales did not feed, krill were found in deep high-density patches. As krill migrated vertically into larger and less dense patches near the surface, whales began to forage. During foraging bouts, we found that feeding rates (number of feeding lunges per hour) were greatest when prey was shallowest, and feeding rates decreased with increasing dive depth. This strategy is consistent with previous models of how air-breathing diving animals optimize foraging efficiency. Thus, humpback whales forage mainly when prey is more broadly distributed and shallower, presumably to minimize diving and searching costs and to increase feeding rates overall and thus foraging efficiency. Using direct measurements of feeding behaviour from animal-borne tags and prey availability from echosounders, our study demonstrates a multi-stage foraging process in a central place forager that we suggest acts to optimize overall efficiency by maximizing net energy gain over time. These data reveal a previously unrecognized level of complexity in predator–prey interactions and underscores the need to simultaneously measure prey distribution in marine central place forager studies.
hep-th / 0605151 1 / 4 BPS circular loops , unstable worldsheet instantons and the matrix model

The standard prescription for computing Wilson loops in the AdS/CFT correspondence in the large coupling regime and tree-level involves minimizing the string action. In many cases the action has more than one saddle point as in the simple example studied in this paper, where there are two 1/4 BPS string solutions, one a minimum and the other not. Like in the case of the regular circular loop the perturbative expansion seems to be captured by a free matrix model. This gives enough analytic control to extrapolate from weak to strong coupling and find both saddle points in the asymptotic expansion of the matrix model. The calculation also suggests a new BMN-like limit for nearly BPS Wilson loop operators.