Miracle-dz/UniFAR-FTUs huggingface dataset:
Control of chaos in agent based manufacturing systems

Distributed control architectures in manufacturing are becoming increasingly popular because their modularity makes them easy to install, configure, and modify. These benefits do not come for free. For creating the manufacturing systems of the future, engineers need to dare a leap in their ways of thinking. Considering a simple multi-agent model in use in a distributed flexible manufacturing project, it is shown that the dynamics of such multi-agent systems without appropriate design control can be chaotic, strongly dependent on some parameters inherent to the agentpsilas negotiation rules. In order to develop appropriate multi-agent models, the dynamic behavior of the system is investigated using concepts and methods of the theory of nonlinear dynamical systems, and suggests some ways to control chaotic behaviours.
Computing the hierarchy of conjunctions of concept names and their negations in a Description Logic knowledge base using Formal Concept Analysis ?

In a series of previous work, we have presented how attribute exploration can be used in the bottom-up construction of DL knowledge bases to compute a concept lattice that is isomorphic to the subsumption hierarchy of all conjunctions of concept names occurring in a knowledge base, and the negations of these concept names. This work is a continuation in the line of the previous work, that makes a step towards more efficient computation of the mentioned hierarchy. Its specific accomplishment is reducing the number of questions asked to the expert and the number of objects produced during the computation of this hierarchy, thus speeding up the computation. Despite its simple nature, the approach speeds up the computation of this hierarchy drastically.
Lexical and Sublexical Units in Speech Perception

Saffran, Newport, and Aslin (1996a) found that human infants are sensitive to statistical regularities corresponding to lexical units when hearing an artificial spoken language. Two sorts of segmentation strategies have been proposed to account for this early word-segmentation ability: bracketing strategies, in which infants are assumed to insert boundaries into continuous speech, and clustering strategies, in which infants are assumed to group certain speech sequences together into units (Swingley, 2005). In the present study, we test the predictions of two computational models instantiating each of these strategies i.e., Serial Recurrent Networks: Elman, 1990; and Parser: Perruchet & Vinter, 1998 in an experiment where we compare the lexical and sublexical recognition performance of adults after hearing 2 or 10 min of an artificial spoken language. The results are consistent with Parser's predictions and the clustering approach, showing that performance on words is better than performance on part-words only after 10 min. This result suggests that word segmentation abilities are not merely due to stronger associations between sublexical units but to the emergence of stronger lexical representations during the development of speech perception processes.
In vivo Firre and Dxz4 deletion elucidates roles for autosomal gene regulation

Recent evidence has determined that the conserved X chromosome “mega-structures” controlled by the Firre and Dxz4 alleles are not required for X chromosome inactivation (XCI) in cell lines. Here we determined the in vivo contribution of these alleles by generating mice carrying a single or double deletion of Firre and Dxz4. We found that these mutants are viable, fertile and show no defect in random or imprinted XCI. However, the lack of these elements results in many dysregulated genes on autosomes in an organ-specific manner. By comparing the dysregulated genes between the single and double deletion, we identified superloop, megadomain, and Firre locus dependent gene sets. The largest transcriptional effect was observed in all strains lacking the Firre locus, indicating that this locus is the main driver for these autosomal expression signatures. Collectively, these findings suggest that these X-linked loci are involved in autosomal gene regulation rather than XCI biology.
Structure and Intractability of Optimal Multi-Robot Path Planning on Graphs

In this paper, we study the structure and computational complexity of optimal multi-robot path planning problems on graphs. Our results encompass three formulations of the discrete multi-robot path planning problem, including a variant that allows synchronous rotations of robots along fully occupied, disjoint cycles on the graph. Allowing rotation of robots provides a more natural model for multi-robot path planning because robots can communicate.Our optimality objectives are to minimize the total arrival time, the makespan (last arrival time), and the total distance. On the structure side, we show that, in general, these objectives demonstrate a pairwise Pareto optimal structure and cannot be simultaneously optimized. On the computational complexity side, we extend previous work and show that, regardless of the underlying multi-robot path planning problem, these objectives are all intractable to compute. In particular, our NP-hardness proof for the time optimal versions, based on a minimal and direct reduction from the 3-satisfiability problem, shows that these problems remain NP-hard even when there are only two groups of robots (i.e. robots within each group are interchangeable).
An FNN-Based adaptive iterative learning control for a class of nonlinear discrete-time systems

In this paper, a fuzzy neural network is applied to design a discrete adaptive iterative learning controller for a class of nonlinear discrete-time systems. The fuzzy neural network is used as a function approximator to compensate the unknown certainty equivalent controller. The problem of function approximation error is solved by a technique of time-varying boundary layer. This boundary layer is then utilized to construct an auxiliary error function for the design of adaptive laws. In order to achieve a desired learning performance, the FNN parameter and the width of boundary layer will be tuned during the iteration processes. Based on a Lyapunov-like analysis, we show that all adjustable parameters as well as the internal signals remain bounded for all iterations and the output tracking error will asymptotically converge to a residual set whose size depends on the width of boundary layer as iteration goes to infinity.
Abnormalities in Monocyte Recruitment and Cytokine Expression in Monocyte Chemoattractant Protein 1–deficient Mice

Monocyte chemoattractant protein 1 (MCP-1) is a CC chemokine that attracts monocytes, memory T lymphocytes, and natural killer cells. Because other chemokines have similar target cell specificities and because CCR2, a cloned MCP-1 receptor, binds other ligands, it has been uncertain whether MCP-1 plays a unique role in recruiting mononuclear cells in vivo. To address this question, we disrupted SCYA2 (the gene encoding MCP-1) and tested MCP-1–deficient mice in models of inflammation. Despite normal numbers of circulating leukocytes and resident macrophages, MCP-1−/− mice were specifically unable to recruit monocytes 72 h after intraperitoneal thioglycollate administration. Similarly, accumulation of F4/80+ monocytes in delayed-type hypersensitivity lesions was impaired, although the swelling response was normal. Development of secondary pulmonary granulomata in response to Schistosoma mansoni eggs was blunted in MCP-1−/− mice, as was expression of IL-4, IL-5, and interferon γ in splenocytes. In contrast, MCP-1−/− mice were indistinguishable from wild-type mice in their ability to clear Mycobacterium tuberculosis. Our data indicate that MCP-1 is uniquely essential for monocyte recruitment in several inflammatory models in vivo and influences expression of cytokines related to T helper responses.
Towards Planning for Elastic

This paper investigates the problem of path planning for a thin elastic plate. The underlying geometric model for the plate is provided by a B ezier representation. The geometric model is augmented by a realistic mechanical model. The latter permits the computation of the shape of the plate with respect to a set of grasping constraints by minimizing the elastic energy of the deformation. We use a probabilistic roadmap planner to compute paths for the plate and we present a number of experimental results to illustrate our approach. Our work is a rst step towards considering the physical properties of objects when planning.
Arming parvoviruses with CpG motifs to improve their oncosuppressive capacity

Oncolytic viruses represent novel tools for cancer treatment. Besides specifically killing cancer cells (oncolysis), these agents also provide danger signals, prompting the immune system to eliminate virus‐infected tumours. As a consequence of oncolytic events, the innate and adaptive immune systems gain access to tumour antigens, which result in cross‐priming and vaccination effects. Here the aim was to see whether we could enhance this adjuvant capacity by incorporating immunostimulatory CpG motifs into the single‐stranded genome of an oncolytic parvovirus (H‐1PV). We engineered 2 CpG‐enriched H‐1PV variants (JabCG1 and JabCG2), preserving both the replication competence and the oncolytic features of the parental virus. In keeping with their increased CpG content, the JabCG1 and JabCG2 genomes proved in vitro to be more potent triggers of TLR‐9‐mediated signalling than wild‐type H‐1PV DNA. Antitumour activity was evaluated in a rat model of MH3924A hepatoma lung metastases, where an infection with parental or modified viruses served as an ex vivo adjuvant to a subcutaneously administered autologous cell vaccine. In this setup, which excludes direct oncolytic effects on metastases, the JabCG2 vector displayed enhanced immunogenicity, inducing markers of cellular immunity (IFNγ) and dendritic cell activation (CD80, CD86) in mediastinal (tumour‐draining) lymph nodes. This led to a significantly reduced metastatic rate (50%) as compared to other vaccination schedules (H‐1PV‐, JabCG1‐, JabGC‐ or mock‐treated cells). The data provide proof of principle that increasing the number of immunostimulatory CpG motifs within oncolytic viruses makes it possible to improve their overall anticancer effect by inducing antitumour vaccination. © 2008 Wiley‐Liss, Inc.
Learning to estimate reverberation time in noisy and reverberant rooms

The reverberation time, T 60 , is an important indicator of the reverberation strength in a room and has many applications in speech processing, such as dereverberation. However, the T 60 must be blindly estimated if only reverberant speech is available. In this paper, we provide a learning based approach for T 60 estimation. We treat the T 60 estimation as a classiﬁcation problem by dividing the T 60 range into countable bins (e.g. 19 bins covering 0 . 1 s to 1 s with a bin width of 0 . 05 s) and the estimation becomes predicting which bin the true T 60 falls into for a given speech. We use deep neural networks (DNN) to learn such a mapping from speech to the T 60 . The DNN is trained on a large amount of reverberant and noisy speech signals generated from various simulated rooms with known reverberations. After training, we observe that the DNN can learn highly sensible features for the T 60 estimation task. Experimental results on the data from both simulated rooms and real rooms conﬁrmed the effectiveness of the DNN learning based approach. In all the test cases, the DNN method signiﬁcantly outperforms the state-of-the-art SDD T 60 estimation method.
A “ Good Death ” : Critical Care Nurses ’ Suggestions for Improving End-of-life Care

• BACKGROUND Providing appropriate end-of-life care has become a primary concern of nurses and the public. The highly technological critical care environment may not facilitate such care. • OBJECTIVE To collect suggestions from critical care nurses for improving end-of-life care in intensive care units. • METHODS A geographically dispersed, random sample of 1409 members of the American Association of Critical-Care Nurses was sent a 72-item survey on perceptions of end-of life care. The survey included a request for suggestions on ways to improve end-of life care. • RESULTS Of the 861 critical care nurses who responded to the survey, 485 offered 530 suggestions for improving end-of-life care. Providing a “good death” was the major theme; specific suggestions included ways to help ensure death with dignity and peace. Barriers to providing good deaths included nursing time constraints, staffing patterns, communication challenges, and treatment decisions that were based on physicians’ rather than patients’ needs. Suggestions for providing a good death included facilitating dying with dignity; not allowing patients to be alone while dying; managing patients’ pain and discomfort; knowing, and then following, patients’ wishes for end-of-life care; promoting earlier cessation of treatment or not initiating aggressive treatment at all; and communicating effectively as a healthcare team. Educational initiatives for professionals and the public were also suggested. • CONCLUSIONS Implementation of specific suggestions provided by experienced critical care nurses might increase the quality of end-of-life care, facilitating a good death for intensive care patients. (American Journal of Critical Care. 2006;15:38-46) PROVIDING A “GOOD DEATH”: CRITICAL CARE NURSES’ SUGGESTIONS FOR IMPROVING END-OF-LIFE CARE
Fundamentals of modern spectral analysis

Digital Signal Processing (DSP) has revolutionized spectral analysis. Where the swept spectrum analyzer dominated the market in the past, the Fast Fourier Transform (FFT) based spectrum analyzer is now gaining acceptance as the method of choice. This is due in part to the prevalence of high speed, high dynamic range Analog-to-Digital Converters (ADC) and high speed signal processing devices such as Field Programmable Gate Arrays (FPGA). Because the FFT-based spectrum analyzer is readily implemented with a limited set of generic hardware, it is an attractive technique for Synthetic Instruments (SI), where the goal is to form multiple measurement functions from a limited set of generic hardware modules. In this paper some of the fundamental design parameters and specifications of modern spectrum analyzers such as dynamic range, instantaneous bandwidth, and image rejection are presented. These parameters are explored with a focus on maintaining system performance without sacrificing flexibility.
Timebase dynamic weight for Dijkstra Algorithm implementation in route planning software

This paper reviews Dijkstra Algorithm implementation in finding the shortest path for route planning. The regular shortest path algorithms do not consider the timebase dynamic traffic condition of road network, i.e the hourly changing of traffic density. Timebase dynamic weight for Dijkstra Algorithm compute the most efficient time and minimum fuel consumption based on the real condition of the traffic profiles of the road network. The traffic profiles describe the time needed to pass the road based on time which also differs for workdays and weekend. Simulation shows the implementation of Timebase Dynamic Weight for Dijkstra Algorithm can give better cost efficiency compared with the common Shortest Distance calculation and Traffic Avoidance calculation.
Apolipoprotein E isoform polymorphisms are not associated with insulin resistance: the Framingham Offspring Study.

OBJECTIVE Insulin resistance and the apolipoprotein (apo) allele e4 have both been associated with coronary heart disease (CHD). We examined the relationship between insulin resistance and apo(e) polymorphisms among participants in the Framingham Offspring Study. RESEARCH DESIGN AND METHODS During 1991-1995, subjects underwent a clinical examination and an oral glucose tolerance test with measurement of fasting and 2-h glucose, insulin levels, and fasting lipid levels. We measured insulin resistance using the homeostasis model, in which insulin resistance (HOMA-IR) = fasting insulin x glucose/22.5. Apo(e) isoforms and phenotypes were determined in 1983-1987 using isoelectric focusing of plasma VLDL. Of the 2,120 subjects with complete HOMA-IR and apo(e) data, 204 with type 2 diabetes were excluded. The remainder were classified with features of the insulin resistance syndrome including impaired glucose tolerance (1997 American Diabetes Association criteria), hypertension (criteria from the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure [6th report]), obesity (BMI >85th percentile), high waist-to-hip ratio (>85th percentile), and high triglyceride and low HDL levels (NCEP-2 criteria). We analyzed data with contingency tables and age- and sex-adjusted logistic regression models. RESULTS Among the 1,916 subjects, the mean age was 55 (range 28-83 years) and 51% were women. Median HOMA-IR was 6.4 (interquartile range 5.2-8.2), and allele frequencies were 7.8, 79.9, and 12.4% for apo(e) alleles e2, e3, and e4, respectively There were no differences in proportions of apo(e) isoforms or alleles across increasing quintiles of HOMA-IR. A less dramatic increase in proportions occurred with elevated triglycerides associated with increasing HOMA-IR among those with apo(e) isoforms 2/2 and 2/3 compared with the others (P < or = 0.01 for interaction). Otherwise, apo(e) did not substantially modify associations between insulin resistance and features of the insulin resistance syndrome. CONCLUSIONS There is no association between apo(e) polymorphisms and insulin resistance. These appear to represent 2 completely independent risk factors for CHD.
In Vitro Antioxidant, Antibacterial, and Cytotoxic Activity and In Vivo Effect of Syngonium podophyllum and Eichhornia crassipes Leaf Extracts on Isoniazid Induced Oxidative Stress and Hepatic Markers

The present study reports the in vitro antioxidant, antibacterial, and cytotoxic potential of Syngonium podophyllum (SP) and Eichhornia crassipes (EC) leaf aqueous extracts as well as their in vivo effect on oxidative stress and hepatic biomarkers in isoniazid induced rats. Phytochemical screening of extracts revealed the presence of flavonoids, terpenoids, reducing sugars, alkaloids, and saponins. Phenolic content in SP and EC extracts was 5.36 ± 0.32 and 10.63 ± 0.13 mg PGE/g, respectively, while flavonoid content was 1.26 ± 0.03 and 0.51 ± 0.03 μg QE/mg, respectively. EC extract exhibited comparatively better antioxidant activity as indicated by reducing power (0.197–0.775), DPPH radical scavenging potential (11%–96%), and metal ion chelating ability (42%–93%). Both the extracts provided 13%–65% protection against lipid peroxidation in rat tissue (liver, kidney, and brain) homogenate. SP and EC extracts exhibited 51% and 43% cytotoxicity against lung cancer (NCI-H322) cell line, respectively. Both extracts demonstrated considerable antibacterial activity against Proteus vulgaris, Salmonella typhi, and Bordetella bronchiseptica. Coadministration of E. crassipes extract with isoniazid in rats accounted for 46% decrease in malondialdehyde content and 21% increase in FRAP value of plasma. It also mitigated the isoniazid induced alterations in serum enzymes (SGOT, SGPT, and ALP), total bilirubin, creatinine, and hemoglobin contents. S. podophyllum extract was found to be hepatotoxic.
The Neural Consequences of Attentional Prioritization of Internal Representations in Visual Working Memory

Although humans can hold multiple items in mind simultaneously, the contents of working memory (WM) can be selectively prioritized to effectively guide behavior in response to rapidly changing exigencies in the environment. Neural evidence for this is seen in studies of dual serial retrocuing of two items held concurrently in visual WM, in which evidence in occipital cortex for the active neural representation of the cued item increases, and evidence for the uncued item decreases, often to levels indistinguishable from empirical baseline. Although this pattern is reminiscent of the effects of selective attention on visual perception, the extent to which more subtle principles of visual attention may also apply to visual working memory remains uncertain. In the present study we explored whether the well-characterized “same-object” benefit in visual target detection, attributed to object-based attention (e.g., Duncan, 1984; Egly, Driver, & Rafal, 1994), may also be observed for information held in visual WM. fMRI data were collected while human subjects (male and female) performed a multi-step serial retrocuing task in which they first viewed two two-dimensional sample stimuli comprised of colored moving dots. After stimulus offset, an initial relevance cue then indicated whether both dimensions of only the first or only the second object, or only the color or only the direction-of-motion of both objects, would be relevant for the remainder of the trial, which then proceeded with the standard dual serial retrocuing procedure. Thus, on “object-relevant” trials, the ensuing priority cues prompted the selection of one from among two features (“color” or “direction”) bound to the same object, whereas on “feature-relevant” trials the priority cues prompted the selection of one from among two features each belonging to a different object. Results of analyses with multivariate inverted encoding models (IEM) revealed a same-object benefit on object-relevant trials: Whereas, on feature-relevant trials, the first priority cue triggered a strengthening of the neural representation of the cued feature and a concomitant weakening-to-baseline of the uncued feature; on object-relevant trials the cued item remained active but did not increase in strength, and the uncued item weakened, but remained significantly elevated throughout the delay period. Of additional interest, on both types of trials the second priority cue prompted an active recoding of the uncued item into a different neural representation, perhaps to minimize its ability to interfere with recall of the cued item. Finally, although stimulus-specific representation in parietal and frontal cortex was weak and uneven, these regions closely tracked the higher-order information of which stimulus category was relevant for behavior at all points during the trial, indicating an important role in controlling the prioritization of information in visual working memory.
The CES-D Scale

The CES-D scale is a short self-report scale designed to measure depressive symptomatology in the general population. The items of the scale are symptoms associated with depression which have been used in previously validated longer scales. The new scale was tested in household interview surveys and in psychiatric settings. It was found to have very high internal consistency and adequate test- retest repeatability. Validity was established by pat terns of correlations with other self-report measures, by correlations with clinical ratings of depression, and by relationships with other variables which support its construct validity. Reliability, validity, and factor structure were similar across a wide variety of demographic characteristics in the general population samples tested. The scale should be a useful tool for epidemiologic studies of de pression.