Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Tracking for Multiple Lagrangian Systems Using Only Position Measurements.

Abstract This paper investigates the distributed tracking control problem for multiple Lagrangian systems under a general directed graph where only a portion of the agents have access to the desired time-varying trajectory. To overcome the problem that only positions are measured, a observer is designed to estimate the velocity for each follower. By employing the estimated states, the distributed observer-based controller is proposed using only position measurements. Furthermore, the condition for the distributed tracking problem on the directed graph is derived, such that the tracking errors and observer errors semi-globally converge to zero. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.
Model-based software tools for integrated vehicle health management

Present day IVHM systems are often constructed using hand-written code that is hard to produce and difficult to verify and maintain. In this paper we introduce a suite of model-based tools that allow for the construction of embeddable IVHM applications using a model-based approach. Reusable (and potentially validated) reasoners are used in conjunction with executable code that is generated from models, thus allowing the integration of the reasoner as a component into a larger on-board system. This paper describes the toolsuite, the modeling approach used, the runtime environment, and some of the applications where the tools were used
Large scale multi-class classification using latent classifiers

We study the problem of multi-class image classification with large number of classes, of which the one-vs-all based approach is prohibitive in practical applications. Recent state-of-the-art approaches rely on label tree to reduce classification complexity. However, building optimal tree structures and learning precise classifiers to optimize tree loss is challenging. In this paper, we introduce a novel approach using latent classifiers that can achieve comparable speed but better performance. The key idea is that instead of using C one-vs-all classifiers (C is the number of classes) to generate the score matrix for label prediction, a much smaller number of classifiers are used. These classifiers, called latent classifiers, are generated by analyzing the correlation among classes and removing redundancy. Experiments on several large datasets including ImageNet-1K, SUN-397, and Caltech-256 show the efficiency of our approach.
The importance of relational coordination and reciprocal learning for chronic illness care within primary care teams.

BACKGROUND Recent research from a complexity theory perspective suggests that implementation of complex models of care, such as the Chronic Care Model (CCM), requires strong relationships and learning capacities among primary care teams. PURPOSES Our primary aim was to assess the extent to which practice member perceptions of relational coordination and reciprocal learning were associated with the presence of CCM elements in community-based primary care practices. METHODOLOGY/APPROACH We used baseline measures from a cluster randomized controlled trial testing a practice facilitation intervention to implement the CCM and improve risk factor control for patients with Type 2 diabetes in small primary care practices. Practice members (i.e., physicians, nonphysician providers, and staff) completed baseline assessments, which included the Relational Coordination Scale, Reciprocal Learning Scale, and the Assessment of Chronic Illness Care (ACIC) survey, along with items assessing individual and clinic characteristics. To assess the association between Relational Coordination, Reciprocal Learning, and ACIC, we used a series of hierarchical linear regression models accounting for clustering of individual practice members within clinics and controlling for individual- and practice-level characteristics and tested for mediation effects. FINDINGS A total of 283 practice members from 39 clinics completed baseline measures. Relational Coordination scores were significantly and positively associated with ACIC scores (Model 1). When Reciprocal Learning was added, Relational Coordination remained a significant yet notably attenuated predictor of ACIC (Model 2). The mediation effect was significant (z = 9.3, p < .01); 24% of the association between Relational Coordination and ACIC scores was explained by Reciprocal Learning. Of the individual- and practice-level covariates included in Model 3, only the presence of an electronic medical record was significant; Relational Coordination and Reciprocal Learning remained significant independent predictors of ACIC. PRACTICE IMPLICATIONS Efforts to implement complex models of care should incorporate strategies to strengthen relational coordination and reciprocal learning among team members.
Typing as functional-logic evaluation

We present a transformational approach to type inference for functional logic programs. More concretely, we give a broad set of examples showing how, given a functional logic program P, we can synthesize a remarkably simple and natural functional logic program P' such that the evaluation of expressions with respect to P' corresponds to typing the expressions in the original P. We start developing those ideas for the case of type inference with standard Hindley-Milner types, and after that we consider some variations, like local definitions with different degrees of polymorphism, existential types and type checking in the presence of polymorphic recursion. For the basic case of Hindley-Milner types we provide also a formalization of the transformation and proofs of its correctness. Besides its potential applicability to the implementation of different type systems, or to the educational use of the synthesized typing programs to explain different type inference/checking processes, the paper demonstrates vividly the expressive power of functional logic languages, as well as some of their limitations for metaprogramming purposes, that we have overcome by providing a suitable set of metalogical functions to inspect, classify and manipulate expressions according to their structure, similar to well known Prolog metapredicates for such purposes.
Simulation of mobile robot navigation with sensor fusion on an uneven path

This paper describes the navigation of a two-wheel drive mobile robot along a predefined path under uneven road conditions where it cannot solely rely on encoders, GPS or an accelerometer individually. There are conditions when low friction or slippery ground surfaces such as sandy paths and pits cause one or both encoders to halt or rotate less as the robot moving forward. Areas covered with clouds, trees or structures can block GPS signals. Sudden pickups and halts give false information from accelerometers. Therefore Kalman filter based sensor fusion algorithm is implemented in order to get the best position estimation for the mobile robot using above sensor outputs. The Special feature of this algorithm is that it includes a simple method to overcome the effects of encoder errors due to the slipping of wheels of the mobile robot, which does not require complex computations to additional measurement units to directly measure the slipping of the wheels of the robot. Finally the validity of the proposed algorithm is demonstrated via simulation.
Noise Risk and Derivative Price

We consider an asset market traded three types of assets: the risk–free asset, the market portfolio and derivatives written on the market portfolio return. We determine a sufficient condition to guarantee that noise risk monotonically changes their derivatives. The condition is that Arrow–Pratt absolute risk aversion is decreasing and convex. JEL Classification: D51, D81, G12.
A Normal Stellar Disk in the Galaxy Malin 1

Since its discovery, Malin 1 has been considered the prototype and most extreme example of the class of giant low surface brightness disk galaxies. Examination of an archival Hubble Space Telescope I-band image reveals that Malin 1 contains a normal stellar disk that was not previously recognized, having a central I-band surface brightness of μ0 = 20.1 mag arcsec-2 and a scale length of 4.8 kpc. Out to a radius of ∼10 kpc, the structure of Malin 1 is that of a typical SB0/a galaxy. The remarkably extended, faint outer structure detected out to r ≈ 100 kpc appears to be a photometrically distinct component and not a simple extension of the inner disk. In terms of its disk scale length and central surface brightness, Malin 1 was originally found to be a very remote outlier relative to all other known disk galaxies. The presence of a disk of normal size and surface brightness in Malin 1 suggests that such extreme outliers in disk properties probably do not exist, but underscores the importance of the extended outer disk regions for a full understanding of the structure and formation of spiral galaxies.
Dexmedetomidine preconditioning ameliorates kidney ischemia-reperfusion injury

Kidney ischemia‐reperfusion (I/R) injury is a common cause of acute kidney injury. We tested whether dexmedetomidine (Dex), an alpha2 adrenoceptor (α2‐AR) agonist, protects against kidney I/R injury. Sprague–Dawley rats were divided into four groups: (1) Sham‐operated group; (2) I/R group (40 min ischemia followed by 24 h reperfusion); (3) I/R group + Dex (1 μg/kg i.v. 60 min before the surgery), (4) I/R group + Dex (10 μg/kg). The effects of Dex postconditiong (Dex 1 or 10 μg/kg i.v. after reperfusion) as well as the effects of peripheral α2‐AR agonism with fadolmidine were also examined. Hemodynamic effects were monitored, renal function measured, and acute tubular damage along with monocyte/macrophage infiltration scored. Kidney protein kinase B, toll like receptor 4, light chain 3B, p38 mitogen‐activated protein kinase (p38 MAPK), sirtuin 1, adenosine monophosphate kinase (AMPK), and endothelial nitric oxide synthase (eNOS) expressions were measured, and kidney transciptome profiles analyzed. Dex preconditioning, but not postconditioning, attenuated I/R injury‐induced renal dysfunction, acute tubular necrosis and inflammatory response. Neither pre‐ nor postconditioning with fadolmidine protected kidneys. Dex decreased blood pressure more than fadolmidine, ameliorated I/R‐induced impairment of autophagy and increased renal p38 and eNOS expressions. Dex downregulated 245 and upregulated 61 genes representing 17 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, in particular, integrin pathway and CD44. Ingenuity analysis revealed inhibition of Rac and nuclear factor (erythroid‐derived 2)‐like 2 pathways, whereas aryl hydrocarbon receptor (AHR) pathway was activated. Dex preconditioning ameliorates kidney I/R injury and inflammatory response, at least in part, through p38‐CD44‐pathway and possibly also through ischemic preconditioning.
Apparel Classifier and Recommender using Deep Learning Live Demo

We present a scalable approach for automatically detecting and tagging clothing products along with their colors, given a single image without meta-data. This has several interesting applications, including e-commerce, on-line advertising etc. Our pipeline makes use of the state-of-the-art methodology of extracting deep features using Transfer learning. The core of our process is a multi-class learner based on Logistic regression. To automate the whole process, we developed a web application which detects the clothing type and color of any query image. We have achieved a good clothing detection performance ( 86%), while being quite fast. Finally, we present a clothing suggestion scenario, where similar items from our dataset are presented to the user. We have also provided a running service for the above task as a prototype of our work. The URL has been mentioned in the header.
Decentralized Learning for Multiplayer Multiarmed Bandits

We consider the problem of distributed online learning with multiple players in multiarmed bandit (MAB) models. Each player can pick among multiple arms. When a player picks an arm, it gets a reward. We consider both independent identically distributed (i.i.d.) reward model and Markovian reward model. In the i.i. d. model, each arm is modeled as an i.i. d. process with an unknown distribution with an unknown mean. In the Markovian model, each arm is modeled as a finite, irreducible, aperiodic and reversible Markov chain with an unknown probability transition matrix and stationary distribution. The arms give different rewards to different players. If two players pick the same arm, there is a collision, and neither of them get any reward. There is no dedicated control channel for coordination or communication among the players. Any other communication between the users is costly and will add to the regret. We propose an online index-based distributed learning policy called dUCB4 algorithm that trades off exploration versus exploitation in the right way, and achieves expected regret that grows at most as near- O(log2T). The motivation comes from opportunistic spectrum access by multiple secondary users in cognitive radio networks wherein they must pick among various wireless channels that look different to different users. This is the first distributed learning algorithm for multiplayer MABs with heterogeneous players (that have player-dependent rewards) to the best of our knowledge.
The Highly Flattened Dark Matter Halo of NGC 4244

In a previous paper (Olling 1995, \aj, 110, 591; astro-ph/9505002) a method was developed to determine the shapes of dark matter halos of spiral galaxies from an accurate determination of the rotation curve, the flaring of the gas layer and the velocity dispersion in the HI. Here this method is applied to the almost edge-on Scd galaxy NGC 4244 for which the necessary parameters are determined in the accompanying paper (AJ, Aug. 1996; astro-ph/9605110). The observed flaring of the HI beyond the optical disk puts significant constraints on the shape of the dark matter halo, which are almost independent of the stellar mass-to-light ratio. NGC 4244's dark matter halo is found to be highly flattened with a shortest-to-longest axis ratio of 0.2 (-0.1)(+0.3). If the dark matter is disk-like, the data presented in this paper imply that the vertical velocity dispersion of the dark matter must be 10% - 30% larger than the measured tangential dispersion in the HI. Alternatively, the measured flaring curve is consistent with a round halo if the gaseous velocity dispersion ellipsoid is anisotropic. In that case the vertical dispersion of the gas is 50 - 70% of the measured tangential velocity dispersion.
Behavioural decision game between organizational support and work engagement

Abstract The relationship between organization and employees is more and more concerned. This study analyses the simultaneous game and step-by-step game of behaviour decision between organizational support and employee engagement. The results show that organization improve incentive coefficient can improve employee’s work engagement and can reduce organizational work check costs under the control of simultaneous game. When behavioural decision is under the control of step-by-step game, whether priority decision is the organization’s work check or employee’s work engagement, the result is organization cannot blindly increase incentive coefficient, and need consider the motivating factor for work check frequency and employee’s work engagement. The study of the three game mechanisms shows that decision-making ability and decision-making credibility of the organization and employees are prerequisite for the normal operation of the game relationship between the organization and employees.
A re vie w stud y of lightweight proactive source routing protocol for MANETs

Mobile Ad hoc systems have recognized expanding mindfulness in the present couple of years. As of late, systems administration of unattended detecting gadgets has expected noteworthy consideration from the exploration group. In this paper we exhibit a survey investigation of lightweight proactive source steering conventions of Mobile Ad hoc systems. In this paper we likewise audit and analyze the proposed Extended Proactive Source Routing convention to minimize adjustment of the tree of every current hub in a way to get most proficiency from the framework which is the examination work done under the undertaking “EPSR: An Enhanced Lightweight Proactive Source Routing Protocol For Mobile Ad Hoc Networks”.
Effect of Exercise at Three Exercise Intensities on Salivary Cortisol

Changes in cortisol concentration in response to exercise at 3 different intensities were quantified. Ten apparently healthy, recreationally active males participated. On 4 separate occasions, subjects were assigned a random order of 1-hour cycle ergometer bouts of exercise at 44.5 ± 5.5%, 62.3 ± 3.8%, and 76.0 ± 6.0% (mean ± SD) of Vo2peak and a resting control session. Saliva samples were collected before exercise at 10, 20, 40, and 59 minutes of exercise and at 20 minutes of recovery. Differences in cortisol concentration were assessed via multivariate analysis of variance (α= 0.05) Tukey post hoc analysis when indicated. During the highest-intensity exercise session, cortisol was significantly higher at 59 minutes of exercise (p = 0.004) and at 20 minutes of recovery (p = 0.016) than at those same time points during the resting control session. No significant differences in cortisol concentration were noted among resting, low-, and moderate-intensity exercise. Exercise <40 minutes in duration elicited no significant differences at any intensity. These data indicate that only exercise of high intensity and long duration results in significant elevations of salivary cortisol.
Cadherin switching: essential for behavioral but not morphological changes during an epithelium-to-mesenchyme transition

Epithelium-to-mesenchyme transitions (EMTs) are characterized by morphological and behavioral changes in cells. During an EMT, E-cadherin is downregulated while N-cadherin is upregulated. The goal of this study was to understand the role cadherin switching plays in EMT using a classical model system: transforming growth factor β1 (TGF-β1)-mediated EMT in mammary epithelial cells. We showed that stress fibers and focal adhesions are increased, and cell-cell junctions are decreased in response to TGF-β1. Moreover, these changes were reversible upon removal of TGF-β1. Downregulation of E-cadherin and upregulation of N-cadherin were both transcriptional. Neither experimental knockdown nor experimental overexpression of N-cadherin interfered with the morphological changes. In addition, the morphological changes associated with EMT preceded the downregulation of E-cadherin. Interestingly, TGF-β1-induced motility in N-cadherin-knockdown cells was significantly reduced. Together, these data suggest that cadherin switching is necessary for increased motility but is not required for the morphological changes that accompany EMT.
A method of computation for worst-case delay analysis on SpaceWire networks

SpaceWire is a standard for on-board satellite networks chosen by the ESA as the basis for future data-handling architectures. However, network designers need tools to ensure that the network is able to deliver critical messages on time. Current research only seek to determine probabilistic results for end-to-end delays on Wormhole networks like SpaceWire. This does not provide sufficient guarantee for critical traffic. Thus, in this paper, we propose a method to compute an upper-bound on the worst-case end-to-end delay of a packet in a SpaceWire network.
Generative Adversarial Active Learning

We propose a new active learning by query synthesis approach using Generative Adversarial Networks (GAN). Different from regular active learning, the resulting algorithm adaptively synthesizes training instances for querying to increase learning speed. We generate queries according to the uncertainty principle, but our idea can work with other active learning principles. We report results from various numerical experiments to demonstrate the effectiveness the proposed approach. In some settings, the proposed algorithm outperforms traditional pool-based approaches. To the best our knowledge, this is the first active learning work using GAN.
Efficient Parallel Implementation of the Fox Algorithm

This paper presents an efficient parallel implementation of the Fox algorithm on a shared-memory supercomputer, the SGI Altix 350. The Fox algorithm is concerned with matrix multiplication, C ← A × B. In this study, the matrices are block square matrices with order n and with equal block size n = n √ p , where p is the number of processors involved in the implementation. The performance of the parallel implementation is acceptable with the speedup, sp, and efficiency, p, approaching their respective upper limiting values of p and 1 only when p is not too small and n too large or when p not too large and n too small. The implementation is also found to be space-optimal but not timeoptimal due to the heavy inter-process data traffic and the consequent dominance of that traffic time, tcomm, over the computation time, tcalc, especially when p is large.
First-class polymorphism with type inference

Languages like ML and Haskell encourage the view of values as first-class entities that can be passed as arguments or results of functions, or stored as components of data structures. The same languages offer parametric polymorphism, which allows the use of values that behave uniformly over a range of different types. But the combination of these features is not supported-- polymorphic values are not first-class. This restriction is sometimes attributed to the dependence of such languages on type inference, in contrast to more expressive, explicitly typed languages, like System F, that do support first-class polymorphism.This paper uses relationships between types and logic to develop a type system, FCP, that supports first-class polymorphism, type inference, and also first-class abstract datatypes. The immediate result is a more expressive language, but there are also long term implications for language design.
Pathological Complete Remission of Pancreatic Cancer Following Neoadjuvant Chemoradiation Therapy; Not the End of Battles

AbstractIn spite of controversial issues, pancreatectomy following neoadjuvant chemoradiation therapy (NeoCRT) has been applied in treating advanced pancreatic cancer. Cases of pathological complete remission (pCR) following NeoCRT is rare, and its long-term follow-up data are still lacking.From January 2000 to December 2012, medical records of the patients who underwent pancreatectomy for pancreatic ductal adenocarcinoma were retrospectively reviewed. Characteristics of the patients with pCR were summarized and their long-term follow-up data were analyzed.Among 86 patients with pancreatic cancer who underwent radical pancreatectomy following NeoCRT, 10 patients (11.6%) were reported to pCR. Nine out of 10 patients received gemcitabine-based chemoradiation therapy. Median pre-NeoCRT serum CA 19-9 was 313.5 U/ml, and post-NeoCRT serum CA 19-9 was 9.9 U/ml, which was shown to be significant difference between 2 serum CA 19-9 level (P = 0.005). Pylorus-preserving pancreaticoduodenectomy was done in 8 patients, and the others received distal pancreatosplenectomy. Postoperative chemotherapy was received in 6 patients. Disease-free survival was statistically superior in patients with pCR than patients without pCR (P < 0.05). However, 5 patients experienced cancer recurrence and no clinicopathologic variables including preoperative resectability could not predict the potential recurrence of tumor in patients with pCR (P > 0.05). pCR is rarely reported following NeoCRT, but this condition is not telling the cure of the disease. Early recurrence in the pattern of liver metastasis and peritoneal seeding can be expected. However, long-term survival could be maintained in patients without recurrence. Further investigation is necessary for predicting failure of treatment.