Miracle-dz/UniFAR-FTUs huggingface dataset:
Application-aware prioritization mechanisms for on-chip networks

Network-on-Chips (NoCs) are likely to become a critical shared resource in future many-core processors. The challenge is to develop policies and mechanisms that enable multiple applications to efficiently and fairly share the network, to improve system performance. Existing local packet scheduling policies in the routers fail to fully achieve this goal, because they treat every packet equally, regardless of which application issued the packet. This paper proposes prioritization policies and architectural extensions to NoC routers that improve the overall application-level throughput, while ensuring fairness in the network. Our prioritization policies are application-aware, distinguishing applications based on the stall-time criticality of their packets. The idea is to divide processor execution time into phases, rank applications within a phase based on stall-time criticality, and have all routers in the network prioritize packets based on their applications' ranks. Our scheme also includes techniques that ensure starvation freedom and enable the enforcement of system-level application priorities. We evaluate the proposed prioritization policies on a 64-core CMP with an 8×8 mesh NoC, using a suite of 35 diverse applications. For a representative set of case studies, our proposed policy increases average system throughput by 25.6% over age-based arbitration and 18.4% over round-robin arbitration. Averaged over 96 randomly-generated multiprogrammed workload mixes, the proposed policy improves system throughput by 9.1% over the best existing prioritization policy, while also reducing application-level unfairness.
Mouse Activity across Time Scales: Fractal Scenarios

In this work we devise a classification of mouse activity patterns based on accelerometer data using Detrended Fluctuation Analysis. We use two characteristic mouse behavioural states as benchmarks in this study: waking in free activity and slow-wave sleep (SWS). In both situations we find roughly the same pattern: for short time intervals we observe high correlation in activity - a typical 1/f complex pattern - while for large time intervals there is anti-correlation. High correlation of short intervals ( to : waking state and to : SWS) is related to highly coordinated muscle activity. In the waking state we associate high correlation both to muscle activity and to mouse stereotyped movements (grooming, waking, etc.). On the other side, the observed anti-correlation over large time scales ( to : waking state and to : SWS) during SWS appears related to a feedback autonomic response. The transition from correlated regime at short scales to an anti-correlated regime at large scales during SWS is given by the respiratory cycle interval, while during the waking state this transition occurs at the time scale corresponding to the duration of the stereotyped mouse movements. Furthermore, we find that the waking state is characterized by longer time scales than SWS and by a softer transition from correlation to anti-correlation. Moreover, this soft transition in the waking state encompass a behavioural time scale window that gives rise to a multifractal pattern. We believe that the observed multifractality in mouse activity is formed by the integration of several stereotyped movements each one with a characteristic time correlation. Finally, we compare scaling properties of body acceleration fluctuation time series during sleep and wake periods for healthy mice. Interestingly, differences between sleep and wake in the scaling exponents are comparable to previous works regarding human heartbeat. Complementarily, the nature of these sleep-wake dynamics could lead to a better understanding of neuroautonomic regulation mechanisms.
Compressive sensing for digital holographic interferometry

Compressive sensing (CS) is one of the latest signal processing techniques, which facilitates to reconstruct a complete signal from a small number of randomly chosen signal samples. It has been shown that CS can be applied successfully in digital holography. In this paper, a novel approach has been suggested for holographic reconstruction using CS technique for determining displacement and its derivative maps. Off-axis lensless Fourier holography configuration is used to capture a digital hologram and only Fourier transform is used for phase retrieval. Hence the Fourier transform is used as a CS operator in the proposed method and NESTA algorithm is used for signal reconstruction. Speckle noise should be filtered from phase maps for phase unwrapping process. Filtering speckle noise for the phase maps with high fringe density is not a better solution because high density fringes will be worn out while filtering. However, the method suggested in this paper is able to produce super resolution displacement and derivative maps using CS, which overcomes the problem due to high density fringes. In super resolution phase maps, the fringe density will be reduced due to increasing in pixel count. Experimental results demonstrate that the proposed method is able to determine super resolution displacement and derivative maps effectively.
Transforming UML models to colored petri nets models using graph grammars

In this paper we propose an approach for transforming UML Statechart and collaboration diagrams to Colored Petri nets models. This transformation aims to bridge the gap between informal notation (UML diagrams) and more formal notation (colored Petri nets models) for analysis purpose. It produces highly-structured, graphical, and rigorously-analyzable models that facilitates early detection of errors like deadlock, livelock, etc . The approach is based on graph transformation since the input and output of the transformation process are graphs. The meta-modeling tool ATOM3 is used. An example illustrates our approach.
Filling Radii of Finitely Presented Groups

The lling radius function R of Gromov measures the minimal radii of van Kampen diagrams lling edge-circuits w in the Cayley 2complex of a nite presentation P. It is known that the Dehn function can be bounded above by a double exponential in R and the length of the loop, and it is an open question whether a single exponential bound suces. We dene the upper lling radius R(w) of w to be the maximal radius of minimal area llings of w and let R be the corresponding lling function, so R(n) is the maximum of R(w) over all edge-circuits w of length at most n. We show that the Dehn function is bounded above by a single exponential in R and the length of the loop. We give an example of a nite presentation P where R is linearly bounded but R grows exponentially. 1991 Mathematics Subject Classication: 20F05, 20F32, 57M07
A Game-Theoretical Capacity Allocation Scheme Design under Seasonal Demand

In this paper, we study capacity allocation under seasonal demand in two-echelon supply chain. In this supply chain the supply and demand is seriously out-off-balance. In detail the capactiy of the supplier decreases in high-demand state while the capacity can be fully released in low-demand state. Hence a game-theoretical capacity allocation scheme is proposed to increase retailers’ orders in low-demand state. This allocation scheme based on turn-and-earn uses orders in low-demand state to allocate capacity in high-demand state. Our competitive game is base on Nash equilibrium. A contract based on Nash equilibrium is designed to guarantee a reasonable allocation of interests between the supplier and retailers by changing incentives. We show that turn-and-earn allocation does coordinate the system and with cooperation both the supplier and the retailer can both benefit from it. A numerical study is conducted.
Compact 90$^{\circ}$ Twist Formed by a Double-Corner-Cut Square Waveguide Section

Simple and compact rectangular waveguide 90deg twist is proposed. The twist transformer region is a square waveguide section with two square corner cuts. The twist full-wave model is based on the mode-matching and generalized S-matrix techniques. It is found that the optimized twists can provide the bandwidths of no less than 30% at the 30-dB return-loss level or 16% at the 40-dB level over a lower, middle, or higher part of the rectangular waveguide operating band. The twist lengths are five times less than the rectangular waveguide wavelength at the central frequency of the chosen operating band. Role of evanescent modes in the interaction of two small-distanced twist transitions is discussed. The design results are confirmed by the results of real device measurements.
Rapid eye movement sleep deprivation and hypertension. Genetic influence.

We studied the importance of genetic predisposition in the development of stress-induced hypertension in the spontaneously hypertensive rat (SHR), Wistar-Kyoto (WKY) rat, and borderline hypertensive rat (BHR; first-generation offspring of SHR and WKY). Rats were submitted to seven 72-hour sessions of rapid eye movement sleep deprivation (REM-sd) every other week during 13 weeks. Tail arterial pressure was determined throughout the experiment. At the end of the study, mean arterial pressure (direct measurement), sympathetic activity (acute blockade with propranolol and phentolamine), and ventricular weight were determined. Results showed that REM-sd induced sustained hypertension only in rats with a partial predisposition to developing hypertension (BHRs). Values of tail arterial pressure at the end of the study were BHR REM-sd, 175 +/- 1.6 mm Hg and control BHR, 155.9 +/- 0.9 mm Hg, p less than 0.05; SHR REM-sd, 219 +/- 2.6 mm Hg and control SHR, 211.9 +/- 3.4 mm Hg, NS; WKY REM-sd, 123.9 +/- 2 mm Hg and control WKY, 125.4 +/- 2.2 mm Hg, NS. Stressed groups showed higher reduction of mean arterial pressure than their controls when submitted to sympathetic blockade (SHR REM-sd, -75.7 +/- 13.2 mm Hg and control SHR, -60 +/- 4.5 mm Hg, p less than 0.05; BHR REM-sd, -38.4 +/- 3.6 mm Hg and control BHR, -24.3 +/- 2.1 mm Hg, NS; WKY REM-sd, -34.4 +/- 2.5 mm Hg and control WKY, -25.6 +/- 3.3 mm Hg, NS). REM-sd increased ventricular weight in all strains. These increments showed no correlation with blood pressure. (ABSTRACT TRUNCATED AT 250 WORDS)
2-clickAuth Optical Challenge-Response Authentication

Internet users today often have usernames and passwords at multiple web sites. To simplify things, many sites support some form of federated identity management, such as OpenID, that enables users to have a single account that allows them to log on to many different sites by authenticating to a single identity provider. Most identity providers perform authentication using a username and password. Should these credentials be compromised, e.g. captured by a key logger or malware on an untrusted computer, all the user’s accounts become compromised. Therefore a more secure authentication method is desirable. We have implemented 2-clickAuth, an optical challenge-response solution where a web camera and a camera phone are used for authentication. Two-dimensional barcodes are used for the communication between phone and computer, which allows 2-clickAuth to transfer relatively large amounts of data in a short period of time. 2-clickAuth is considerably more secure than passwords while still being easy to use and easy to distribute to users. This makes 2-clickAuth a viable alternative to passwords in systems where enhanced security is desired, but availability, ease-of-use, and cost cannot be compromised. We have implemented an identity provider in the OpenID federated identity management system that uses 2-clickAuth for authentication, making 2-clickAuth available to all users of sites that support OpenID, including Facebook, Sourceforge and MySpace.
Unsupervised Clustering Analysis: a Multiscale Complex Networks Approach

Unsupervised clustering, also known as natural clustering, stands for the classification of data according to their similarities. Here we study this problem from the perspective of complex networks. Mapping the description of data similarities to graphs, we propose to extend two multiresolution modularity based algorithms to the finding of modules (clusters) in general data sets producing a multiscales' solution. We show the performance of these reported algorithms to the classification of a standard benchmark of data clustering and compare their performance.
Preparation and Physico-Chemical Study of Sandwich Glass-Jute-Bisphenol-C-Formaldehyde Resin Composites

Glass-Jute-bisphenol-C-formaldehyde (Glass-Jute-BCF) sandwich composites were prepared by hand lay-up technique at 150°C under 30.4 MPa pressure for 2 h. The resin, glass and jute fiber content in the sandwich composite were 33.3, 10.4 and 56.3 wt%, respectively. 10 prepregs containing 8 inner prepregs of jute mats sandwiched between 2 outer prepregs of glass mats. Glass-Jute-BCF sandwich composite has 23 MPa tensile strength, 119 MPa flexural strength, 1.72 kV/mm electric strength and 1.25 × 1012 ohm cm volume resistivity. Tensile strength and volume resistivity both decreased, while flexural strength and electrical strength both improved upon hybridization. Sandwich composite showed high diffusivity in water, 10% NaCl and 10% HCl solutions as compared to Glass-BCF composite. Equilibrium water absorption time is found to be 72 h in all 3 environments. Comparatively low diffusivity is observed due to silane treated glass fibers. No effect of boiling water is observed on stability of composite. Saturation time in boiling water reduced 18 times without any damage to the composite. Glass-Jute-BCF sandwich composite may be useful for low load bearing applications in construction, electrical and electronic industries as well as in harsh acidic and saline environments.
Discovering geometric patterns in genomic data

ChIP-chip and ChIP-seq are techniques for the isolation and identification of the binding sites of DNA-associated proteins along the genome. Both techniques produce genome-wide location data. The geometric arrangements of these binding sites can provide valuable information about biological function, such as the activation or repression of genes. In this paper, we formalize this problem and propose a novel graph based algorithm called Patterns of Marks (PoM) to discover efficiently these types of geometric patterns in genomic data. We also describe how we validate the algorithm using experimental data.
Insulin Resistance and Environmental Pollutants: Experimental Evidence and Future Perspectives

Background: The metabolic disruptor hypothesis postulates that environmental pollutants may be risk factors for metabolic diseases. Because insulin resistance is involved in most metabolic diseases and current health care prevention programs predominantly target insulin resistance or risk factors thereof, a critical analysis of the role of pollutants in insulin resistance might be important for future management of metabolic diseases. Objectives: We aimed to critically review the available information linking pollutant exposure to insulin resistance and to open the discussion on future perspectives for metabolic disruptor identification and prioritization strategies. Methods: We searched PubMed and Web of Science for experimental studies reporting on linkages between environmental pollutants and insulin resistance and identified a total of 23 studies as the prime literature. Discussion: Recent studies specifically designed to investigate the effect of pollutants on insulin sensitivity show a potential causation of insulin resistance. Based on these studies, a summary of viable test systems and end points can be composed, allowing insight into what is missing and what is needed to create a standardized insulin resistance toxicity testing strategy. Conclusions: It is clear that current research predominantly relies on top-down identification of insulin resistance–inducing metabolic disruptors and that the development of dedicated in vitro or ex vivo screens to allow animal sparing and time- and cost-effective bottom-up screening is a major future research need. Citation: Hectors TL, Vanparys C, Van Gaal LF, Jorens PG, Covaci A, Blust R. 2013. Insulin resistance and environmental pollutants: experimental evidence and future perspectives. Environ Health Perspect 121:1273–1281; http://dx.doi. org/10.1289/ehp.1307082
Estimation of uncertainty in measurement of power quality characteristics with a virtual measurement instrument

The paper deals with modeling and estimation of uncertainty in measurement of power quality (PQ) characteristics by using a virtual measurement instrument. The described virtual PQ analyzer is comprised of National Instruments (NI) analog input modules, NI data acquisition board, personal computer and software in LabVIEW™. The main sources of uncertainty are identified and evaluated. A Monte Carlo simulation is used for the model estimation and generation of the combined uncertainty of particular characteristics. Experimental measurement and statistical analysis of the results are made and compared with the simulation results. The experimental results have served for a verification of the model and the Monte Carlo simulation results.
TALEN-mediated Nanog disruption results in less invasiveness, more chemosensitivity and reversal of EMT in Hela cells

Emerging evidence suggests that Nanog is involved in cervical tumorigenesis. However, the regulating role of Nanog in tumorigenesis and chemosensitivity are still poorly understood. In this study, Nanog was disrupted by transcription activatorlike effector nucleases (TALEN) in Hela cells and its expression was significantly decreased in a single-cell derived sub-clone with biallelic mutations. The disruption of Nanog not only induced down regulation of some other core transcription factor genes for cell self-renewal, such as Oct4, Sox2 and FoxD3, but also led to the down regulation of some mesenchymal representative genes, vimentin and N-adherin, and up regulation of the epithelial gene, E-cadherin. In addition, the invasiveness and clonogenicity of the Hela cells were obviously affected, and surprisingly their sensitivities to anti-cancer drugs were also significantly increased in vitro. After Xenograft into nude mice, the growth volumes of the neoplasms from the Nanog disrupted Hela cells were significantly smaller compared with those from wild type ones. In conclusion, these results suggest that disruption of Nanog may reverse the status of epithelial-mesenchymal transition, which is critical in tumorigenesis, and alleviate chemoresistance, as well as their invasiveness, in cervical cancer cells.
Local Search Topology in Planning Benchmarks: A Theoretical Analysis

Many state-of-the-art heuristic planners derive their heuristic function by relaxing the planning task at hand, where the relaxation is to assume that all delete lists are empty. The success of such planners on many of the current benchmarks suggests that in those task's state spaces relaxed goal distances yield a heuristic function of high quality. Recent work has revealed empirical evidence confirming this intuition, stating several hypotheses about the local search topology of the current benchmarks, concerning the non-existence of dead ends and of local minima, as well as a limited maximal distance to exits on benches. Investigating a large range of planning domains, we prove that the above hypotheses do in fact hold true for the majority of the current benchmarks. This explains the recent success of heuristic planners. Specifically, it follows that FF's search algorithm, using an idealized heuristic function, is polynomial in (at least) eight commonly used benchmark domains. Our proof methods shed light on what the structural reasons are behind the topological phenomena, giving hints on how these phenomena might be automatically recognizable.
Multi-Objective Community Detection Based on Memetic Algorithm

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.