Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Filtering of Nociceptive Information by Dynamic Activation of Endogenous Pain Modulatory Systems

Endogenous pain control mechanisms have long been known to produce analgesia during “flight or fight” situations and to contribute to cognitively driven pain modulation, such as placebo analgesia. Afferent nociceptive information can also directly activate supraspinal descending modulatory systems, suggesting that these mechanisms may participate in feedback loops that dynamically alter the processing of nociceptive information. The functional significance of these feedback loops, however, remains unclear. The phenomenon of offset analgesia—disproportionately large decreases in pain ratings evoked by small decreases in stimulus intensity—suggests that dynamic activation of endogenous pain inhibition may contribute to the temporal filtering of nociceptive information. The neural mechanisms that mediate this phenomenon remain currently unknown. Using functional magnetic resonance imaging, we show that several regions of the midbrain and brainstem are differentially activated during offset analgesia. These activations are consistent with the location of areas such as the periaqueductal gray (PAG), rostral ventral medulla, and locus ceruleus that have substantial roles in descending inhibition of pain. This transient analgesia contributes to the temporal filtering of nociceptive information by producing a perceptual amplification of the magnitude and duration of decreases in noxious stimulus intensity. Together with the involvement of PAG and associated brainstem mechanisms in cognitively generated analgesia, the present observations suggest that the fundamental role of endogenous pain modulatory mechanisms is to dynamically shape the processing of nociceptive signals to best fit with the ever-changing demands of the environment.
Collective Action and the Group Size Paradox

According to the Olson paradox, larger groups may be less successful than smaller groups in furthering their interests. We address the issue in a model with three distinctive features: explicit intergroup interaction, collective prizes with a varying mix of public and private characteristics, and nonlinear lobbying costs. The interplay of these features leads to new results. When the cost of lobbying has the elasticity of a quadratic function, or higher, larger groups are more effective no matter how private the prize. With smaller elasticities, a threshold degree of publicness is enough to overturn the Olson argument, and this threshold tends to zero as the elasticity approaches the value for a quadratic function. We also demonstrate that these results are true, irrespective of whether we examine group sizes over the cross-section in some given equilibrium or changes in the size of a given group over different equilibria.
Inflammation, Cerebral Vasospasm, and Evolving Theories of Delayed Cerebral Ischemia

Cerebral vasospasm (CVS) is a potentially lethal complication of aneurysmal subarachnoid hemorrhage (aSAH). Recently, the symptomatic presentation of CVS has been termed delayed cerebral ischemia (DCI), occurring as early as 3-4 days after the sentinel bleed. For the past 5-6 decades, scientific research has promulgated the theory that cerebral vasospasm plays a primary role in the pathology of DCI and subsequently delayed ischemic neurological decline (DIND). Approximately 70% of patients develop CVS after aSAH with 50% long-term morbidity rates. The exact etiology of CVS is unknown; however, a well-described theory involves an antecedent inflammatory cascade with alterations of intracellular calcium dynamics and nitric oxide fluxes, though the intricacies of this inflammatory theory are currently unknown. Consequently, there have been few advances in the clinical treatment of this patient cohort, and morbidity remains high. Identification of intermediaries in the inflammatory cascade can provide insight into newer clinical interventions in the prevention and management of cerebral vasospasm and will hopefully prevent neurological decline. In this review, we discuss current theories implicating the inflammatory cascade in the development of CVS and potential treatment targets.
Existing data sources for clinical epidemiology: the Danish Quality Database of Mammography Screening

The Danish Quality Database of Mammography Screening (DKMS) was established in 2007, when screening was implemented on a nationwide basis and offered biennially to all Danish women aged 50–69 years. The primary aims of the database are to monitor and evaluate the quality of the screening program and – after years of follow-up – to evaluate the effect of nationwide screening on breast cancer-specific mortality. Here, we describe the database and present results for quality assurance from the first round of national screening. The steering committee for the DKMS defined eleven organizational and clinical quality indicators and standards to monitor the Danish breast cancer screening program. We calculated the relevant proportions and ratios with 95% confidence intervals for each quality indicator. All indicators were assessed on a national and regional level. Of 670,039 women invited for mammography, 518,823 (77.4%) participated. Seventy-one percent of the women received the result of their mammography examination within 10 days of screening, and 3% of the participants were recalled for further investigation. Among all detected cancers, 86% were invasive cancers, and the proportion of women with node negative cancer was 67%. There were 36% women with small cancers, and the ratio of surgery for benign lesions to malignant lesions was 1:6.3. A total of 80% of women with invasive cancers were treated with breast conserving therapy. Screening interval and interval cancers were not relevant in the first round, and data regarding radiation dose were not available at the time of evaluation. Overall, the quality indicators showed satisfactory quality in the first round of national breast cancer screening in Denmark. The DKMS is a potentially valuable tool for improving quality and conducting research in the field of breast cancer screening.
An Unconstrained Architecture for High-Order ΣΔ Force-Feedback Inertial Sensors

ΣΔ force-feedback loops have to deal with a stability problem due to phase-lag occurring in the sensor. To tackle this problem traditionally a compensator is introduced in the loop. We show that this traditional approach imposes a constraint on the realizable NTF such that the NTF cannot be optimized. Next we propose a novel unconstrained architecture that allows to implement any NTF. This way greatly improved quantisation noise shaping can be obtained. In a typical design example the improvement was 12 dB.
Adaptive Query Optimization Method for Multiple Continuous Queries

Continuous query is widely recognized as a scheme for processing queries over data streams, and efficient methods for processing multiple continuous queries are needed. Our research group has proposed a multiple query optimization method for continuous queries. In our method, the system forms clusters of queries with similar execution patterns, and derives query plans sharing the result of common operators. Our previous experiments have shown that a parameter value in the clustering phase controls divisions of clusters and has a great impact on query processing effi- ciency. However, the optimal parameter value must be decided by trial and error. This paper extends our previous work. The proposed method automatically estimates the optimal value and iteratively adjusts it even if properties of underlying data streams dramatically change.
Interference as a Tool for Designing and Evaluating Multi-Robot Controllers

Designing and implementing cooperative group behaviors for robots is considered something of a black art involving an extensive amount of reprogramming and parameter adjustment. What seems to be lacking is a pragmatic, practical, general-purpose tool that would both guide the design and structure the evaluation of controllers for distributed real-world multi-robot tasks. In this paper, we propose the use of interference. between robots as one such simple tool for designing and evaluating multi-robot controllers. We explore how key issues in multi-robot control can be addressed using interference, a directly measurable property of a multi-robot system. We discuss how behavior arbitration schemes, i.e., the choice of controllers, can be made and adjusted using interference. As an experimental example, we demonstrate three different implementations of a collection clean-up (foraging) task using four physical mobile robots, and present analyses of the experimental data gathered from trials of all three implementations.
NAS Parallel Benchmarks I/O Version 2.4. 2.4

We describe a benchmark problem, based on the Block-Tridiagonal (BT) problem of the NAS Parallel Benchmarks (NPB), which is used to test the output capabilities of high-performance computing systems, especially parallel systems. We also present a source code implementation of the benchmark, called NPBIO2.4-MPI, based on the MPI implementation of NPB, using a variety of ways to write the computed solutions to file.
Exploring the Role of Brain Oscillations in Speech Perception in Noise: Intelligibility of Isochronously Retimed Speech

A growing body of evidence shows that brain oscillations track speech. This mechanism is thought to maximize processing efficiency by allocating resources to important speech information, effectively parsing speech into units of appropriate granularity for further decoding. However, some aspects of this mechanism remain unclear. First, while periodicity is an intrinsic property of this physiological mechanism, speech is only quasi-periodic, so it is not clear whether periodicity would present an advantage in processing. Second, it is still a matter of debate which aspect of speech triggers or maintains cortical entrainment, from bottom-up cues such as fluctuations of the amplitude envelope of speech to higher level linguistic cues such as syntactic structure. We present data from a behavioral experiment assessing the effect of isochronous retiming of speech on speech perception in noise. Two types of anchor points were defined for retiming speech, namely syllable onsets and amplitude envelope peaks. For each anchor point type, retiming was implemented at two hierarchical levels, a slow time scale around 2.5 Hz and a fast time scale around 4 Hz. Results show that while any temporal distortion resulted in reduced speech intelligibility, isochronous speech anchored to P-centers (approximated by stressed syllable vowel onsets) was significantly more intelligible than a matched anisochronous retiming, suggesting a facilitative role of periodicity defined on linguistically motivated units in processing speech in noise.
Comparison of composite Bragg theory and quad‐polarization radar backscatter from RADARSAT‐2: With applications to wave breaking and high wind retrieval

[1] Depolarized (de-pol) radar backscatter is now produced by many spaceborne satellites. Analysis of RADARSAT-2 (R2) quad-polarization (quad-pol) data with collocated in situ ocean wind measurements reveals that the de-pol radar backscatter does not saturate in high winds. This is a significant development for radar wind sensing, because wind retrieval with copolarized (co-pol) backscatter suffers from problems of incidence-and-azimuth-angle-dependent signal saturation and dampening in high winds. We present a study comparing satellite quad-pol measurements with the composite surface Bragg (CB) theory of radar backscattering from the ocean surface. The co-pol data are in good agreement with the CB theory. De-pol data are more sensitive to wind speed compared to theoretical prediction, thus retrieval of high winds is more accurate using the de-pol return. The cubic wind speed dependence of de-pol returns in high winds reflects the significant breaking wave contributions. The relationship can be used to obtain wave-breaking properties from space.
Towards a Cloud-Based Framework for Urban Computing, The Trajectory Analysis Case

In this paper we present a Cloud-based framework for urban computing that can be tailored to be used in different scenarios of urban planning and management that can occur in smart cities. The focus in the paper is on the management of large-scale socio-geographic data obtained through the trajectories followed by mobile devices. Our goal is to mine human activities and routines from this socio-geographic data in order to catch user's behaviour. To this aim, we introduce a methodology for trajectory pattern mining consisting in (a) finding frequent regions, more densely passed through ones, and (b) extracting trajectory patterns from those regions. Experimental evaluation shows that due to complexity and large data involved in the application scenario, the trajectory pattern mining process can take advantage from a parallel execution environment offered by a Cloud architecture.
Multistability and Perceptual Inference

Ambiguous images present a challenge to the visual system: How can uncertainty about the causes of visual inputs be represented when there are multiple equally plausible causes? A Bayesian ideal observer should represent uncertainty in the form of a posterior probability distribution over causes. However, in many real-world situations, computing this distribution is intractable and requires some form of approximation. We argue that the visual system approximates the posterior over underlying causes with a set of samples and that this approximation strategy produces perceptual multistability—stochastic alternation between percepts in consciousness. Under our analysis, multistability arises from a dynamic sample-generating process that explores the posterior through stochastic diffusion, implementing a rational form of approximate Bayesian inference known as Markov chain Monte Carlo (MCMC). We examine in detail the most extensively studied form of multistability, binocular rivalry, showing how a variety of experimental phenomena—gamma-like stochastic switching, patchy percepts, fusion, and traveling waves—can be understood in terms of MCMC sampling over simple graphical models of the underlying perceptual tasks. We conjecture that the stochastic nature of spiking neurons may lend itself to implementing sample-based posterior approximations in the brain.
Argumentation models for cyber attribution

A major challenge in cyber-threat analysis is combining information from different sources to find the person or the group responsible for the cyber-attack. It is one of the most important technical and policy challenges in cybersecurity. The lack of ground truth for an individual responsible for an attack has limited previous studies. In this paper, we take a first step towards overcoming this limitation by building a dataset from the capture-the-flag event held at DEFCON, and propose an argumentation model based on a formal reasoning framework called DeLP (Defeasible Logic Programming) designed to aid an analyst in attributing a cyber-attack. We build models from latent variables to reduce the search space of culprits (attackers), and show that this reduction significantly improves the performance of classification-based approaches from 37% to 62% in identifying the attacker.
Nutrition knowledge in athletes: a systematic review.

CONTEXT Nutrition education aims to enhance knowledge and improve dietary intake in athletes. Understanding athletes' nutrition knowledge and its influence on dietary intake will inform nutrition-education programs in this population. PURPOSE To systematically review the level of nutrition knowledge in athletes, benchmark this against nonathlete comparison groups, and determine the impact of nutrition knowledge on dietary intake. METHODS An extensive literature search from the earliest record to March 2010 using the terms nutrition knowledge or diet knowledge and athlete or sport was conducted. Included studies recruited able or physically disabled, male or female, competitive (recreational or elite) athletes over the age of 13 yr. Quantitative assessment of knowledge and, if available, diet intake was required. Because of variability in the assessment of nutrition knowledge and dietary intake, meta-analysis was not conducted. RESULTS Twenty-nine studies (17 published before 2000) measuring nutrition knowledge (7 including a nonathlete comparison group) met inclusion criteria. Athletes' knowledge was equal to or better than that of nonathletes but lower than comparison groups including nutrition students. When found statistically significant, knowledge was greater in females than males. A weak (r < .44), positive association between knowledge and dietary intake was reported in 5 of 9 studies assessing this. Common flaws in articles included inadequate statistical reporting, instrument validation, and benchmarking. CONCLUSION The nutrition knowledge of athletes and its impact on their dietary intake is equivocal. There is a need for high-quality, contemporary research using validated tools to measure nutrition knowledge and its impact on dietary intake.
Equal pain—Unequal fear response: enhanced susceptibility of tooth pain to fear conditioning

Experimental fear conditioning in humans is widely used as a model to investigate the neural basis of fear learning and to unravel the pathogenesis of anxiety disorders. It has been observed that fear conditioning depends on stimulus salience and subject vulnerability to fear. It is further known that the prevalence of dental-related fear and phobia is exceedingly high in the population. Dental phobia is unique as no other body part is associated with a specific phobia. Therefore, we hypothesized that painful dental stimuli exhibit an enhanced susceptibility to fear conditioning when comparing to equal perceived stimuli applied to other body sites. Differential susceptibility to pain-related fear was investigated by analyzing responses to an unconditioned stimulus (UCS) applied to the right maxillary canine (UCS-c) vs. the right tibia (UCS-t). For fear conditioning, UCS-c and USC-t consisted of painful electric stimuli, carefully matched at both application sites for equal intensity and quality perception. UCSs were paired to simple geometrical forms which served as conditioned stimuli (CS+). Unpaired CS+ were presented for eliciting and analyzing conditioned fear responses. Outcome parameter were (1) skin conductance changes and (2) time-dependent brain activity (BOLD responses) in fear-related brain regions such as the amygdala, anterior cingulate cortex, insula, thalamus, orbitofrontal cortex, and medial prefrontal cortex. A preferential susceptibility of dental pain to fear conditioning was observed, reflected by heightened skin conductance responses and enhanced time-dependent brain activity (BOLD responses) in the fear network. For the first time, this study demonstrates fear-related neurobiological mechanisms that point toward a superior conditionability of tooth pain. Beside traumatic dental experiences our results offer novel evidence that might explain the high prevalence of dental-related fears in the population.
Dynamic Nonuniform Data Approximation in Databases with Haar Wavelet

Data synopsis is a lossy compressed representation of data stored into databases that helps the query optimizer to speed up the query process, e.g. time to retrieve the data from the database. An efficient data synopsis must provide accurate information about the distribution of data to the query optimizer at any point in time. Due to the fact that some data will be queried more often than others, a good data synopsis should consider the use of nonuniform accuracy, e.g. provide better approximation of data that are queried the most. Although, the generation of data synopsis is a critical step to achieve a good approximation of the initial data representation, data synopsis must be updated over time when dealing with time varying data. In this paper, we introduce new Haar wavelet synopses for nonuniform accuracy and time-varying data that can be generated in linear time and space, and updated in sublinear time. We further introduce two linear algorithms, called 2-step and M-Step for the Point-wise approximation problem that clearly outperforms previous algorithms known in literature, and two new algorithm called, Data Mapping and Weight Mapping for the Range-sum approximation problem that, to the best of our knowledge, represent a key research milestone as being the first linear algorithm for arbitrary weights. For both scenarios, we focus not only on the generation of the data synopsis but also on their updates over time. The efficiency of our new data synopses is validated against other linear methods by using both synthetic and real data sets.
Association between Sleep Duration and Urinary Albumin Excretion in Patients with Type 2 Diabetes: The Fukuoka Diabetes Registry

Objective Few studies have so far investigated the impact of sleep duration on chronic kidney disease in diabetic patients. The objective of the present study was to examine the relationship between sleep duration and albuminuria in type 2 diabetic patients. Research Design and Methods A total of 4,870 Japanese type 2 diabetic patients ≥20 years of age were divided into six groups according to self-reported sleep duration: less than 4.5 hours, 4.5–5.4 hours, 5.5–6.4 hours, 6.5–7.4 hours, 7.5–8.4 hours and more than 8.5 hours. The association between sleep duration and urinary albumin-creatinine ratio (UACR) was examined cross-sectionally. Results Both short and long sleep durations were significantly associated with higher UACR levels and higher proportions of patients with albuminuria (≥30 mg/g) and macroalbuminuria (≥300 mg/g) compared with a sleep duration of 6.5–7.4 hours (P for quadratic trend <0.001). A U-shaped association between sleep duration and UACR remained significant even after adjustment for potential confounders, including age, sex, duration of diabetes, current smoking habits, former smoking habits, current drinking habits, regular exercise habits, total energy intake, total protein intake, hypnotic use and estimated glomerular filtration rate. Furthermore, the association remained substantially unchanged after additional adjustment for body mass index, hemoglobin A1c, systolic blood pressure, renin-angiotensin system inhibitor use and depressive symptoms. Conclusions Our findings suggest that sleep duration has a U-shaped association with the UACR levels in type 2 diabetic patients, independent of potential confounders.
Fact or fiction

Many previous studies have aimed at spermatogenesis of male murine germ cells in vitro, but no efficient system has been established yet that covers the entire process of mammalian spermatogenesis in a culture dish permanently. In this review, we report on the requirements of spermatogenesis and the current state of different culture methods using testicular tissue fragments, single cell suspensions or three-dimensional culture environments.