Miracle-dz/UniFAR-FTUs huggingface dataset:
Stabilization of Networked Control Systems via Dynamic Output-Feedback Controllers

This paper investigates the problem of stabilization of networked control systems via dynamic output-feedback controllers. The physical plant and the dynamic controller are in continuous time, and a communication channel exists between the output of the physical plant and the input of the dynamic controller. Three important communication features are considered: measurement quantization, signal transmission delay, and data packet dropout, which appear typically in a networked environment. Attention is focused on the design of dynamic output-feedback controllers which ensure asymptotic stability of the closed-loop systems. Linear matrix inequality (LMI)-based conditions are formulated for the existence of admissible controllers. If these conditions are satisfied, a desired controller can be readily constructed. A satellite system is used to illustrate the applicability and effectiveness of the proposed controller design method.
Bayesian recognition of human identities from continuous visual features for safe and secure access in healthcare environments

When using today's admission control systems some kind of interaction is generally required. That is because the system needs something to check a person's identity before it allows access. Often the system will ask for a pin or a password. For many people these interactions are a problem as they might be unable interact with the system in the required way due to impairments. This can be caused by a disease such as Parkinson's or Alzheimer's or they might be unable to use their hands for other reasons. In hospitals, retirement homes or assisted living communities many people would benefit from a contactless admission control system. We propose a system that recognizes human identities from continuous geometrical features that were estimated using the Kinect 2. Those include the distance between the eyes and the estimated height of a person. A Bayesian Classifier for continuous features is used to predict people's identities. The preliminary recognition results suggest that the proposed geometrical features can contribute to reliable admission control. This work was supported by SysTeam GmbH Dortmund, Germany.
Reversal of subjective temporal order due to sensory and motor integrations

It is generally accepted that the brain can resolve the order of two stimuli that are separated in time by 20-50 ms. This applies to temporal order judgment of two tactile stimuli, delivered one to each hand, as long as the arms are uncrossed. However, crossing the arms caused misreporting (that is, inverting) of the temporal order. The reversal was not due to simple confusion of hands, because correct judgment was recovered at longer intervals (e.g., 1.5 s). When the stimuli were delivered to the tips of sticks held in each hand, the judgment was altered by crossing the sticks without changing the spatial locations of the hands. We recently found that temporal order judgments of tactile stimuli are sometimes reversed by visual distractors and by saccadic eye movements. The results suggest that tactile stimuli are ordered in time only after they are referred to relevant locations in space, where multisensory (visual, tactile and proprioceptive) and motor (saccade-related) signals converge. Results from functional imaging generally support this idea. Because performance of congenitally blind people in tactile temporal order judgment is much superior to the sighted and is never impaired by crossing the arms (Roeder et al., 2004), we finally suggest that our integrity of multisensory signals in space is only achieved at the cost of continuity in time.
Quill: Efficient, Transferable, and Rich Analytics at Scale

This paper introduces Quill (stands for a quadrillion tuples per day), a library and distributed platform for relational and temporal analytics over large datasets in the cloud. Quill exposes a new abstraction for parallel datasets and computation, called ShardedStreamable. This abstraction provides the ability to express efficient distributed physical query plans that are transferable, i.e., movable from offline to real-time and vice versa. ShardedStreamable decouples incremental query logic specification, a small but rich set of data movement operations, and keying; this allows Quill to express a broad space of plans with complex querying functionality, while leveraging existing temporal libraries such as Trill. Quill's layered architecture provides a careful separation of responsibilities with independently useful components, while retaining high performance. We built Quill for the cloud, with a master-less design where a language-integrated client library directly communicates and coordinates with cloud workers using off-the-shelf distributed cloud components such as queues. Experiments on up to 400 cloud machines, and on datasets up to 1TB, find Quill to incur low overheads and outperform SparkSQL by up to orders-of-magnitude for temporal and 6× for relational queries, while supporting a rich space of transferable, programmable, and expressive distributed physical query plans.
Chondrocyte Deformations as a Function of Tibiofemoral Joint Loading Predicted by a Generalized High-Throughput Pipeline of Multi-Scale Simulations

Cells of the musculoskeletal system are known to respond to mechanical loading and chondrocytes within the cartilage are not an exception. However, understanding how joint level loads relate to cell level deformations, e.g. in the cartilage, is not a straightforward task. In this study, a multi-scale analysis pipeline was implemented to post-process the results of a macro-scale finite element (FE) tibiofemoral joint model to provide joint mechanics based displacement boundary conditions to micro-scale cellular FE models of the cartilage, for the purpose of characterizing chondrocyte deformations in relation to tibiofemoral joint loading. It was possible to identify the load distribution within the knee among its tissue structures and ultimately within the cartilage among its extracellular matrix, pericellular environment and resident chondrocytes. Various cellular deformation metrics (aspect ratio change, volumetric strain, cellular effective strain and maximum shear strain) were calculated. To illustrate further utility of this multi-scale modeling pipeline, two micro-scale cartilage constructs were considered: an idealized single cell at the centroid of a 100×100×100 μm block commonly used in past research studies, and an anatomically based (11 cell model of the same volume) representation of the middle zone of tibiofemoral cartilage. In both cases, chondrocytes experienced amplified deformations compared to those at the macro-scale, predicted by simulating one body weight compressive loading on the tibiofemoral joint. In the 11 cell case, all cells experienced less deformation than the single cell case, and also exhibited a larger variance in deformation compared to other cells residing in the same block. The coupling method proved to be highly scalable due to micro-scale model independence that allowed for exploitation of distributed memory computing architecture. The method’s generalized nature also allows for substitution of any macro-scale and/or micro-scale model providing application for other multi-scale continuum mechanics problems.
Activation of the stress protein response inhibits the STAT1 signalling pathway and iNOS function in alveolar macrophages: role of Hsp90 and Hsp70

Background and aim Alveolar fluid clearance is impaired by inducible nitric oxide synthase (iNOS)/nitric oxide (NO)-dependent mechanisms in acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). The activation of the stress protein response (SPR) in alveolar macrophages on iNOS-dependent NO production in response to interferon γ (IFNγ), a major cytokine present in the airspace of patients with ALI, was investigated. Methods The SPR was activated in murine and primary human alveolar macrophages prior to analysis of signal transducer and activator of transcription factor 1 (STAT1) activation, iNOS mRNA and protein synthesis, and NO production. Results SPR activation resulted in inhibition of IFNγ-mediated NO production (p=0.001) with >95% detergent insolubilisation of the STAT1 protein. Its subsequent proteasomal degradation was partially reversed with pretreatment of cells with the chemical chaperone glycerol. This early effect of the SPR was caused by the complete disruption of heat shock protein 90 (Hsp90)–STAT1 binding, as shown by immunoprecipitation. Recovery of STAT1 activation and recovery of iNOS synthesis occurred within 12 h after SPR activation (p=0.02). NO production (as compared with non-SPR controls) did not occur until 48 h later (p=0.02). SPR-induced Hsp70 (Hsp70i) expression caused a late inhibition of NO production (p=0.02). Inhibiting >50% Hsp70i expression recovered NO production to control levels whereas overexpressing Hsp70i in the absence of the SPR inhibited NO production (p=0.02). Conclusion Early inhibition of STAT1 following its dissociation from Hsp90, and later inhibition of iNOS activity by Hsp70i, represent novel mechanisms by which SPR activation modulates the IFNγ signalling in alveolar macrophages. These results highlight a potential clinical application for Hsp90 inhibitors in modulating NO signalling during the early phase of acute lung injury.
Single-labelled music genre classification using content-based features

In this paper we use content-based features to perform automatic classification of music pieces into genres. We categorise these features into four groups: features extracted from the Fourier transform's magnitude spectrum, features designed to inform on tempo, pitch-related features, and chordal features. We perform a novel and thorough exploration of classification performance for different feature representations, including the mean and standard deviation of its distribution, by a histogram of various bin sizes, and using mel-frequency cepstral coefficients. Finally, the paper uses information gain ranking to present a pruned feature vector used by six off-the-shelf classifiers. Logistic regression achieves the best performance with an 81% accuracy on 10 GTZAN genres.
Mining Sequential Pattern Algorithms Comparison

Sequential pattern mining is a very important mining technique with broad applications. It found very useful in various domain like natural disaster, sales record analysis, marketing strategy, shopping sequences, medical treatment and DNA sequences etc. It discovers the subsequence’s and frequent relevant pattern from the given sequences. That we have provided the sequence database having sequences, in which each sequence is a list of the transactions ordered by the transaction time. Each transaction consists of the number of the items. The problem is to discover the all sequential pattern who satisfy the user specified constraint, from the given sequence database. There are various sequential pattern mining algorithms proposed earlier, some of them are GSP, SPADE and PrefixSpan. They are proposed to find the relevant frequent pattern from the sequences. In these algorithm the timestamp is an important attribute of each dataset, and also it is important in the process of data mining for giving the more accurate and useful information. The detail survey of these entire algorithms is presented in this paper. First we categorized these algorithms by their used approaches to solve the mining problem and then we have compared each one with another by their various provided features and performance point of view. Keywords— Sequential Pattern Mining; Sequence Database; Apriori; GSP; SPADE; PrefixSpan
Semidiscretization for Time-Delayed Neural Balance Control

The observation that time-delayed feedback can stabilize an inverted pendulum motivates the formu- lation of models of human balance control in terms of delay differential equations (DDEs). Recently the intermittent, digital-like nature of the neural feedback control of balance has become evident. Here, semidiscretization methods for DDEs are used to investigate an unstable dynamic system sub- jected to a digital controller in the context of a switching model for postural control. In addition to limit cycle and chaotic ("microchaos") oscillations, transiently stabilized balance states are possible even though both the open-loop and the closed-loop systems are globally unstable. The possibility that falls can be an intrinsic component of neural control of balance may provide new insights into how the risk of falling in the elderly can be minimized.
Human-Scale Bimanual Haptic Interface

This article presents a haptic system for bimanual haptic feedback that is composed of two light-weight robot arms. The system has a workspace and force capabilities similar to that of two human arms. Sophisticated control strategies are implemented to enable using this system as haptic interface. Depending on the requirements of the task one of three different handles can connect the human hand to the robot. Besides the human-machine-interface, the haptic rendering software is improved such that collisions of two dynamical interaction objects are computed in real-time. The system has been proven to be well suited as haptic interface for multimodal virtual assembly simulations.
Measuring Dark Energy with Gamma-Ray Bursts and Other Cosmological Probes

It has been widely shown that cosmological parameters and dark energy can be constrained by using data from Type Ia supernovae (SNe Ia), the cosmic microwave background (CMB) anisotropy, the baryon acoustic oscillation (BAO) peak from Sloan Digital Sky Survey (SDSS), the X-ray gas mass fraction in clusters, and the linear growth rate of perturbations at z = 0.15 as obtained from the 2dF Galaxy Redshift Survey. Recently, gamma-ray bursts (GRBs) have also been argued to be promising standard candles for cosmography. In this paper, we present constraints on the cosmological parameters and dark energy by combining a recent GRB sample including 69 events with the other cosmological probes. First, we find that for the ΛCDM cosmology this combination makes the constraints stringent, and the best fit is close to the flat universe. Second, we fit the flat Cardassian expansion model and find that this model is consistent with the ΛCDM cosmology. Third, we present constraints on several two-parameter dark energy models and find that these models are also consistent with the ΛCDM cosmology. Finally, we reconstruct the dark energy equation of state parameter w(z) and the deceleration parameter q(z). We see that the acceleration could have started at a redshift from zt = 0.40 to 0.65. This difference in the transition redshift is due to different dark energy models that we adopt. The most stringent constraint on w(z) lies in the redshift range z ~ 0.3-0.6.
Sharing beyond Slacktivism: the effect of socially observable prosocial media sharing on subsequent offline helping behavior

ABSTRACT New forms of youth social and political participation have been termed ‘Slacktivism’ – low-cost online forms of social engagement that decrease subsequent offline participation. Previous experimental work has provided support for a ‘Slacktivism effect,’ but it is unclear if this theoretical model applies to youth media sharing on social networking sites. This study uses a novel sharing simulation paradigm to test the effect of publicly vs. anonymously sharing a social cause video on subsequent willingness to engage in offline helping behavior. Results show that publicly (as compared to anonymously) sharing a selected video on one’s own Facebook wall led to a greater willingness to volunteer for an issue-related cause. Participants’ existing use of social media for engagement in social issues/causes moderated the effect, such that only participants low in use of social media for social engagement were susceptible to the sharing manipulation. Implications for reconceptualizing media sharing as a unique form of online participation beyond ‘Slacktivism’ are discussed.
Research of Multipolar Permanent Magnet Synchronous Submersible Motor for Screw Pump

This paper attempts to present the structures and characteristics of multipolar permanent magnet synchronous submersible motor (MPMSSM) for screw pump. The structures of stator lamination, rotor lamination and stator winding are analyzed. And how temperature influences the performance of the motor is presented. Moreover, how to calculate air-gap length and the size of permanent magnet of MPMSSM are discussed. The MATLAB is used to design the prototype. And, the computer simulation results of magnetic field of the motor are shown by ANSOFT according to its model. The structure and design method of this motor can be used on common MPMSSM.
Randomized user-centric clustering for cloud radio access network with PHY caching

We propose a randomized user-centric remote radio head (RRH) clustering (UCRC) scheme for cloud radio access network (C-RAN) with physical layer (PHY) caching. The randomized UCRC design is modeled as a multi-timescale non-convex problem to optimize the average weighted sum rate (WSR). We first decompose the problem into short-term precoding and medium-term UCRC subproblems. Then we propose a low complexity algorithm which finds the optimal solution for week interference regime and achieves good performance for general cases. Finally, we exploit PHY caching at RRH node of the C-RAN to reduce the fronthaul loading. We show that there is a phase transition of the PHY caching gain according to the skewness of the popularity distribution. Specifically, when the skewness parameter is larger than 1, a large saving in C-RAN fronthaul loading is possible even when the RRH cache capacity is small compared to the total content size.
Detecting DGA malware using NetFlow

Botnet detection systems struggle with performance and privacy issues when analyzing data from large-scale networks. Deep packet inspection, reverse engineering, clustering and other time consuming approaches are unfeasible for large-scale networks. Therefore, many researchers focus on fast and simple botnet detection methods that use as little information as possible to avoid privacy violations. We present a novel technique for detecting malware using Domain Generation Algorithms (DGA), that is able to evaluate data from large scale networks without reverse engineering a binary or performing Non-Existent Domain (NXDomain) inspection. We propose to use a statistical approach and model the ratio of DNS requests and visited IPs for every host in the local network and label the deviations from this model as DGA-performing malware. We expect the malware to try to resolve more domains during a small time interval without a corresponding amount of newly visited IPs. For this we need only the NetFlow/IPFIX statistics collected from the network of interest. These can be generated by almost any modern router. We show that by using this approach we are able to identify DGA-based malware with zero to very few false positives. Because of the simplicity of our approach we can inspect data from very large networks with minimal computational costs.
Frequency Response and Gap Tuning for Nonlinear Electrical Oscillator Networks

We study nonlinear electrical oscillator networks, the smallest example of which consists of a voltage-dependent capacitor, an inductor, and a resistor driven by a pure tone source. By allowing the network topology to be that of any connected graph, such circuits generalize spatially discrete nonlinear transmission lines/lattices that have proven useful in high-frequency analog devices. For such networks, we develop two algorithms to compute the steady-state response when a subset of nodes are driven at the same fixed frequency. The algorithms we devise are orders of magnitude more accurate and efficient than stepping towards the steady-state using a standard numerical integrator. We seek to enhance a given network's nonlinear behavior by altering the eigenvalues of the graph Laplacian, i.e., the resonances of the linearized system. We develop a Newton-type method that solves for the network inductances such that the graph Laplacian achieves a desired set of eigenvalues; this method enables one to move the eigenvalues while keeping the network topology fixed. Running numerical experiments using three different random graph models, we show that shrinking the gap between the graph Laplacian's first two eigenvalues dramatically improves a network's ability to (i) transfer energy to higher harmonics, and (ii) generate large-amplitude signals. Our results shed light on the relationship between a network's structure, encoded by the graph Laplacian, and its function, defined in this case by the presence of strongly nonlinear effects in the frequency response.
The Effect of Surface Nanometre-Scale Morphology on Protein Adsorption

BACKGROUND Protein adsorption is the first of a complex series of events that regulates many phenomena at the nano-bio interface, e.g. cell adhesion and differentiation, in vivo inflammatory responses and protein crystallization. A quantitative understanding of how nanoscale morphology influences protein adsorption is strategic for providing insight into all of these processes, however this understanding has been lacking until now. METHODOLOGY/PRINCIPAL FINDINGS Here we introduce novel methods for quantitative high-throughput characterization of protein-surface interaction and we apply them in an integrated experimental strategy, to study the adsorption of a panel of proteins on nanostructured surfaces. We show that the increase of nanoscale roughness (from 15 nm to 30 nm) induces a decrease of protein binding affinity (<or=90%) and a relevant increase in adsorbed proteins (<or=500%) beyond the corresponding increase of specific area. We demonstrate that these effects are caused by protein nucleation on the surface, which is promoted by surface nanoscale pores. CONCLUSIONS/SIGNIFICANCE These results show that the adsorption of proteins depends significantly on surface nanostructure and that the relevant morphological parameter regulating the protein adsorption process is the nanometric pore shape. These new findings improve our understanding of the role of nanostructures as a biomaterial design parameter and they have important implications for the general understanding of cell behavior on nanostructured surfaces.