Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrapulmonary lipopolysaccharide exposure upregulates cytokine expression in the neonatal brainstem

Perinatal inflammation and neonatal sepsis trigger lung and brain injury. We hypothesized that endotoxin exposure in the immature lung upregulates proinflammatory cytokine expression in the brainstem and impairs respiratory control. Lipopolysaccharide (LPS) or saline was administered intratracheally to vagal intact or denervated rat pups. LPS increased brainstem IL‐1β and vagotomy blunted this response. There was an attenuated ventilatory response to hypoxia and increased brainstem IL‐1β expression after LPS.
Joint production control and product quality decision making in a failure prone manufacturing system

This paper considers joint production control and product quality specification decision making in a failure prone manufacturing system. This is with the knowledge that tight process specifications, while leading to a product of more reliable quality and higher market value, are at the same time associated with higher levels of non conforming parts, a higher rate of parts rejection and thus a lowering of overall plant productivity. The decision making is further complicated by the lack of reliability of the production process which imposes that an adequate, also to be designed, level of inventory of finished parts be maintained. The overall optimal decision policy is defined here as one that maximizes the long term average per unit time profit of a combined measure of quality and quantity dependent sales revenue, minus inventory and backlog costs, in the presence of random plant failures and random repair durations. Policy optimization is achieved via a simulation and experimental design based methodology for Markovian and more general cases.
Failing hospitals: mission statements to drive service improvement?

PURPOSE This paper aims to consider whether the hospital mission statement can be used as a management tool to improve service provision in failing hospitals. DESIGN/METHODOLOGY/APPROACH A literature search into the potential value and harm of hospital mission statements was done, followed by a survey of initial attitudes within a failing hospital. Do they indicate likely success of the tool? FINDINGS Mission statement is a potentially valuable leadership tool in the hospital environment. The success of its implementation is broadly dependent on its being developed with the support of stakeholders and its real application to all management decisions and questions of asset allocation. The potential danger lies in the fact that it can be seen as an expensive expression of politically correct platitudes which leads to cynical alienation of stakeholders. RESEARCH LIMITATIONS/IMPLICATIONS This was a small study within a single UK failing hospital, and extending its range will help to clarify whether its findings are typical of attitudes within such institutions. PRACTICAL IMPLICATIONS The likely success of the hospital mission statement as a management tool within a failing hospital is significantly limited by initial attitudes and preconceptions. Our research suggests that implementation is likely to be detrimental without preparatory involvement of the local community and hospital staff at all levels. SOCIAL IMPLICATIONS Hospital management cannot be divorced from the local community where patient confidence must be maintained. ORIGINALITY/VALUE This paper complements previous research, which has looked at mission statement acceptance among the upper echelons of hospital management.
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers

We derive here new generalization bounds, based on Rademacher Complexity theory, for model selection and error estimation of linear (kernel) classifiers, which exploit the availability of unlabeled samples. In particular, two results are obtained: the first one shows that, using the unlabeled samples, the confidence term of the conventional bound can be reduced by a factor of three; the second one shows that the unlabeled samples can be used to obtain much tighter bounds, by building localized versions of the hypothesis class containing the optimal classifier.
Patterns of Disease Progression in Hypertrophic Cardiomyopathy: An Individualized Approach to Clinical Staging

After the recent celebrations of the 50th anniversary of the modern description of hypertrophic cardiomyopathy (HCM) by Teare and Lord Brock, the time is ripe to reflect on what remains to be discovered.1–3 With the full realization that a massive amount of information relating to the disease has already been uncovered, and paying tribute to all those involved in this process, it is essential to concentrate on the gaps in our knowledge that require concerted efforts to advance the field, particularly in relation to patient management, which continues to be perceived as less than optimal.3 We believe that this is largely due to the partial disconnect between basic research, and an incomplete understanding of the fundamental mechanisms molding a continuously, often insidiously changing phenotype. A thorough comprehension of these processes requires a translational approach based on long-term clinical observation of large HCM cohorts, coupled with basic scientific research, and represents an essential step toward the development of innovative therapies which need to be both disease- and patient-specific.2,3 Traditionally, the focus of HCM literature has been polarized on 2 aspects of indisputable clinical relevance: the pathogenesis, clinical consequences, and management of dynamic left ventricular (LV) outflow obstruction,1 and the issue of arrhythmic risk stratification and prevention of sudden cardiac death (SCD). 4,5 By comparison, limited attention has been devoted to the life-long process of LV remodeling and progressive dysfunction that occur in a substantial proportion of HCM patients and culminates in the rare but dramatic clinical evolution termed as end-stage or burned-out phase.6–9 Consequently, the stages that precede this severe condition are still relatively unknown, representing an important target for research.3 Indeed, because of the slowly evolving nature of HCM, timely identification of patients at risk …
Content Delivery in Fog-Aided Small-Cell Systems with Offline and Online Caching: An Information - Theoretic Analysis

The storage of frequently requested multimedia content at small-cell base stations (BSs) can reduce the load of macro-BSs without relying on high-speed backhaul links. In this work, the optimal operation of a system consisting of a cache-aided small-cell BS and a macro-BS is investigated for both offline and online caching settings. In particular, a binary fading one-sided interference channel is considered in which the small-cell BS, whose transmission is interfered by the macro-BS, has a limited-capacity cache. The delivery time per bit (DTB) is adopted as a measure of the coding latency, that is, the duration of the transmission block, required for reliable delivery. For offline caching, assuming a static set of popular contents, the minimum achievable DTB is characterized through information-theoretic achievability and converse arguments as a function of the cache capacity and of the capacity of the backhaul link connecting cloud and small-cell BS. For online caching, under a time-varying set of popular contents, the long-term (average) DTB is evaluated for both proactive and reactive caching policies. Furthermore, a converse argument is developed to characterize the minimum achievable long-term DTB for online caching in terms of the minimum achievable DTB for offline caching. The performance of both online and offline caching is finally compared using numerical results.
INTEGRATED RANGE CAMERA CALIBRATION USING IMAGE SEQUENCES FROM HAND-HELD OPERATION

This article concentrates on the integrated self-calibration of both the interior orientation and the distance measurement system of a time-of-flight range camera that employs amplitude-modulated, continuous-wave, near-infrared light (photonic mixer device PMD). In contrast to other approaches that conduct specialized experiments for the investigation of individual, potential distortion factors, in the presented approach all calculations are based on the same data set being captured under near-real-world conditions, guided by hand and without auxiliary devices serving as high-order reference. Flat, circular targets stuck on a planar whiteboard and with known positions are automatically tracked throughout the amplitude layer of long image sequences. These image observations are introduced into a bundle block adjustment, which on the one hand results in the determination of the camera’s interior orientation and its temporal variation. On the other hand, the reconstructed exterior orientations and the known planarity of the imaged board allow for the derivation of reference values of the actual distance observations. These deviations are checked on relations with the reference distance itself, the observed signal amplitude, the integration time, the angle of incidence, and with both the position in the field of view and in object space. Eased by the automatic reconstruction of the camera’s trajectory and attitude, several thousand frames are processed, leading to comprehensive statistics.
Surface plasmon polaritons in metal stripes and wires

Surface plasmon polaritons (SPPs) are collective electron oscillations coupled to a light field which are propagating along the interface of a metal and a dielectric. As a surface wave, SPP modes feature properties essentially different from light–field modes in all dielectric structures. These properties could allow the realization of novel photonic devices that overcome certain limitations of conventional devices. Specifically, the realization of two–dimensional optics and light–field transport in sub–wavelength SPP waveguides seems feasible. In this review we discuss recent experimental advances regarding SPP waveguides, i.e. laterally confined metal thin films that guide SPPs. Electron–beam lithography is applied to tailor these films with widths ranging from a few micrometres (stripes) to nanoscopic values (wires). We investigate SPP properties such as propagation length, mode field profile and reflection or scattering at interfaces. Various techniques for SPP excitation and detection are discussed.
A Fractal Model of Mountains with Rivers

This paper addresses the long-standing problem of generating fractal mountains with rivers, and presents a partial solution that incorporates a squig-curve model of a river’s course into the midpoint-displacement model for mountains. The method is based on the observation that both models can be expressed by similar context-sensitive rewriting mechanisms. As a result, a mountain landscape with a river can be generated using a single integrated process.
How Do We Recognize a Song in One Second? : The Importance of Salience and Sound in Music Perception

This project started with the observation that we manage to recognize a song by listening to only a second of it. What perceptual and musical features make this possible, and can such features be used in music analysis and music information retrieval? These questions can be broken down to two main problems: a) segregation of sensory input and b) recognition of musical features. The segregation of musical information from a complex soundscape is discussed with reference to theories of auditory scene analysis and music perception. A problem is that there is still no good way to make computers separate sound streams in a way similar to human perception. When it comes to the recognition process, the thesis focuses on what musical features make a song more or less recognizable. It is argued that a song is recognized quicker if there is some salient, or perceptually significant, feature present. Then it is shown how salience points can be analysed with reference to traditional musical parameters such as melody, harmony, rhythm and dynamics. This discussion leads to an acknowledgment of the significance of sound in music perception. Next, different methods of analysing, visualizing and synthesizing sound, or more specifically instrument timbre, is shown. Finally, theories of artificial neural networks are outlined, with an example of training a feedforward network with timbre. The success of this simulation is taken as an indication that connectionist models may resemble human perception. Throughout the thesis, several examples are shown of how the graphical programming environment MAX/MSP can be used experimentally in music analysis. The thesis concludes that investigating short term music excerpts might be interesting in music analysis. Due to the limitations of our short term memory, such short passages may reveal noteworthy aspects of music perception. It is also suggested that music theory could benefit from studying salience points and paying more attention to the sound of music.
Do Lambs Perceive Regular Human Stroking as Pleasant? Behavior and Heart Rate Variability Analyses

Stroking by humans is beneficial to the human-animal relationship and improves welfare in many species that express intraspecific allogrooming, but very few studies have looked at species like sheep that do not express such contact except around parturition. This study investigated the way lambs perceive regular human tactile contact using behavioral and physiological responses. Twenty-four lambs were reared and bucket-fed in groups of four. All were stroked daily by their familiar caregiver. At 8 weeks of age, the lambs were individually tested in their home pen but in a 1×1m open-barred pen after a 15h period of habituation to physical separation from peers while remaining in visual and auditory contact. Half of the lambs received stroking by their caregiver for 8min and half were exposed to their caregiver’s immobile presence. Heart rate and heart rate variability were recorded and analyzed by 2-min slots over the same interval based on three measures: mean heart rate value (HR), root mean square of successive differences (RMSSD) and standard deviation of all intervals measured between consecutive sinus beats (SDNN). Behavioral responses (ear postures of the lamb and time spent in contact with the familiar caregiver, on the knees of the familiar caregiver, and moving) were recorded throughout the test. Lamb HR decreased continuously while in the presence of their caregiver. Lambs being stroked showed slower HR and higher RMSSD which reflected positive emotional states compared to lambs left unstroked. All behavioral variables were highly correlated with the main component axis of the PCA analyses: the more the animals stayed in contact with their caregiver, the less they moved and the more their ears were hanging. This first component clearly differentiates lambs being stroked or not. Behavioral and physiological observations support the hypothesis that gentle physical contact with the caregiver is perceived positively by lambs.
Finding Characteristic Substrings from Compressed Texts

Text mining from large scaled data is of great importance in computer science. In this paper, we consider fundamental problems on text mining from compressed strings, i.e., computing a longest repeating substring, longest non-overlapping repeating substring, most frequent substring, and most frequent non-overlapping substring from a given compressed string. Also, we tackle the following novel problem: given a compressed text and compressed pattern, compute the representative of the equivalence class of the pattern w.r. t. the text. We present algorithms that solve the above problems in time polynomial in the size of input compressed strings. The compression scheme we consider is straight line program (SLP) which has exponential compression, and therefore our algorithms are more efficient than any existing algorithms that require decompression of given SLPs.
Binary progenitor models for long-duration gamma-ray bursts

While it is well established that long-duration gamma-ray bursts (LGRBs) are intrinsically rare events, requiring a special evolutionary channel, the nature of the most important channels still has to be established. Here, we review some of the main binary models that have been proposed, specifically tidal spin-up models and binary mergers of various types, and then present a new model involving the recently discovered mechanism of explosive common-envelope ejection. The latter model naturally explains why LGRB-related supernovae have not observed helium and may also explain a constant-density medium around LGRBs, as has been deduced in some cases. LGRB rates as well as their metallicity dependence is also discussed for the various models.
Rediscovering SAS/IML ® Software: Modern Data Analysis for the Practicing Statistician

For over 25 years, the SAS/IML programming language has been used by researchers and SAS® programmers who write custom algorithms and analyses. Recently, SAS/IML® Studio arrived with flashy statistical graphics, new language features, and the ability to call the open-source statistical language, R. If you are a longtime SAS/IML programmer, it is time to take a new look at the SAS/IML language. Recent languagefeatures and functions make it easier than ever to program modern data-analytic techniques. If you have not used SAS/IML software before, it is time to discover how its high-level matrix-vector programming language can increase your productivity. This paper presents short programs that implement modern data analyses in SAS/IML software. These analyses include exploring outliers with regression diagnostics, robust estimation of multivariate statistics, bootstrap permutation tests, and calling functions in R that compute certain exact statistics.
Large-scale Online Kernel Learning with Random Feature Reparameterization

A typical online kernel learning method faces two fundamental issues: the complexity in dealing with a huge number of observed data points (a.k.a the curse of kernelization) and the difficulty in learning kernel parameters, which often assumed to be fixed. Random Fourier feature is a recent and effective approach to address the former by approximating the shift-invariant kernel function via Bocher’s theorem, and allows the model to be maintained directly in the random feature space with a fixed dimension, hence the model size remains constant w.r. t. data size. We further introduce in this paper the reparameterized random feature (RRF), a random feature framework for large-scale online kernel learning to address both aforementioned challenges. Our initial intuition comes from the so-called ‘reparameterization trick’ [Kingma and Welling, 2014] to lift the source of randomness of Fourier components to another space which can be independently sampled, so that stochastic gradient of the kernel parameters can be analytically derived. We develop a well-founded underlying theory for our method, including a general way to reparameterize the kernel, and a new tighter error bound on the approximation quality. This view further inspires a direct application of stochastic gradient descent for updating our model under an online learning setting. We then conducted extensive experiments on several large-scale datasets where we demonstrate that our work achieves state-of-the-art performance in both learning efficacy and efficiency.
Temporally Anchored Relation Extraction

Although much work on relation extraction has aimed at obtaining static facts, many of the target relations are actually fluents, as their validity is naturally anchored to a certain time period. This paper proposes a methodological approach to temporally anchored relation extraction. Our proposal performs distant supervised learning to extract a set of relations from a natural language corpus, and anchors each of them to an interval of temporal validity, aggregating evidence from documents supporting the relation. We use a rich graph-based document-level representation to generate novel features for this task. Results show that our implementation for temporal anchoring is able to achieve a 69% of the upper bound performance imposed by the relation extraction step. Compared to the state of the art, the overall system achieves the highest precision reported.
Maternal depressive symptoms during and after pregnancy are associated with attention-deficit/hyperactivity disorder symptoms in their 3- to 6-year-old children

Maternal depressive symptoms during pregnancy have been associated with child behavioural symptoms of attention-deficit/hyperactivity disorder (ADHD) in early childhood. However, it remains unclear if depressive symptoms throughout pregnancy are more harmful to the child than depressive symptoms only during certain times, and if maternal depressive symptoms after pregnancy add to or mediate any prenatal effects. 1,779 mother-child dyads participated in the Prediction and Prevention of Pre-eclampsia and Intrauterine Growth Restriction (PREDO) study. Mothers filled in the Center of Epidemiological Studies Depression Scale biweekly from 12+0–13+6 to 38+0–39+6 weeks+days of gestation or delivery, and the Beck Depression Inventory-II and the Conners’ Hyperactivity Index at the child’s age of 3 to 6 years (mean 3.8 years, standard deviation [SD] 0.5). Maternal depressive symptoms were highly stable throughout pregnancy, and children of mothers with consistently high depressive symptoms showed higher average levels (mean difference = 0.46 SD units, 95% Confidence Interval [CI] 0.36, 0.56, p < 0.001 compared to the low group), and proportion (32.1% vs. 14.7%) and odds (odds ratio = 2.80, 95% CI 2.20, 3.57, p < 0.001) of clinically significant ADHD symptoms. These associations were not explained by the effects of maternal depressive symptoms after pregnancy, which both added to and partially mediated the prenatal effects. Maternal depressive symptoms throughout pregnancy are associated with increased ADHD symptomatology in young children. Maternal depressive symptoms after pregnancy add to, but only partially mediate, the prenatal effects. Preventive interventions suited for the pregnancy period may benefit both maternal and offspring mental health.
The Aging Population and Its Needs

Now that science and technology have contributed to our living longer, the world's population is aging dramatically. The oldest people - those over age 80 - represent the greatest challenge in providing health-related services and identifying ways to assist them in maintaining independence. The US National Institutes on Health's National Center on medical rehabilitation research studies mechanisms, modalities, and devices that improve, restore, or replace lost, underdeveloped, or deteriorating function. The NCMRR's model for viewing research and development in technology and aging comprises five levels of intervention (or research and development) with each level focusing on a different area. The cellular level focuses on pathophysiology, the organ level on impairment, the action level on functional limitation, the task-role level on disability, and the social-limitations level on barriers resulting from attitudes and policies.