Miracle-dz/UniFAR-FTUs huggingface dataset:
Link Capacity-Energy Aware WDC for Network Lifetime Maximization

With the increase in flexibility and capabilities of wireless networks, the use of distributed computing over wireless network environments is being researched in order to maximize network sustainability and interoperability among distributed nodes. To this end, a new paradigm is required for optimization of a more generalized environment. This environment would include various nodes of different processing and communication abilities constrained by circuit powers and residual energy over individual dynamic wireless channels. In this paper, we present a novel strategy named link capacity-energy aware wireless distributed computing (LEA-WDC) for maximizing the lifetime of a wireless network. The major advantage of LEA-WDC is its achievement of lifetime maximization by systematically reconciling highly coupled system parameters (tasks, processing power, communication power, and residual energy) in terms of the role of nodes and the layer of each node. To attain an optimal solution, we perform unique interworking optimization via decomposition in accordance with the roles of header and slave nodes. The evaluation results of our simulation verify that the lifetime is further maximized by finding the optimal transmission power of each node according to the Shannon capacity.
Human arm optimal motion analysis in industrial screwing task

In this study, we propose to explore inverse optimization process to better understand human arm motion in industrial screwing task. The process combines several criteria to minimize such as energy expenditure or trajectory smoothness leading to the optimal trajectory of a typical screwing task, often performed by workers. Estimated joint trajectories are similar with the measured ones, with a mean square error of 4 degrees. The resulting cost-function is mainly composed of energy expenditure and geodesic criteria. Results show the relevance of using composite cost function in human motion planning. This study has been conducted to assist workers by using collaborative robots in painful task in PSA Peugeot Citroen factories to improve ergonomics of manual workstations.
Explicit Communication and Synchronization in SARC

A new network interface optimized for SARC supports synchronization and explicit communication and provides a robust mechanism for event responses. Full-system simulation of the authors' design achieved a 10- to 40-percent speed increase over traditional cache architectures on 64 cores, a two- to four-fold decrease in on-chip network traffic, and a three- to five-fold decrease in lock and barrier latency.
Theoretical estimates for the influence of Lake Victoria on flows in the upper White Nile

Abstract A theoretical model is described for estimating the impacts of changes in Lake Victoria levels on river flows, lake levels and swamp areas in the upper White Nile basin. The basis of the model is to represent the main river channel by a series of interconnected lakes and swamps, whose water balances are described by differential equations relating outflows to levels, areas and the net basin supply at each point. Closed form solutions are obtained for two situations: (a) a long-term change in the mean level of Lake Victoria, and (b) a return to equilibrium levels following an initial disturbance in Lake Victoria levels. A simple model for the net basin supply to Lake Victoria is also used to relate these changes in levels to changes in climate and runoff in the basin. The results illustrate the extreme sensitivity of White Nile flows to changes in Lake Victoria levels and outflows, and in particular to variations in the direct rainfall on the lake surface. Estimates are also presented for the various time scales and time delays which affect the White Nile system.
A Real Time Cognitive Radio Testbed for Physical and Network level Experiments

Cognitive Radios have been advanced as a technology for the opportunistic use of under-utilized spectrum. However, Primary users of the spectrum have raised concerns with regards to interference from Cognitive Radios. On the other hand, a variety of techniques have been proposed for reliable sensing and non-interfering use of the spectrum which have yet to be validated in an actual system. In this paper we present a testbed that will allow us to experiment with sensing algorithms and to demonstrate a working prototype of an indoor cognitive radio network. The testbed is based on the BEE2, a multi-FPGA emulation engine which is capable of connecting to 18 radio front-ends. The testbed will be used to experiment with various baseband sensing algorithms and cooperative sensing schemes.
Tracking High Quality Clusters over Uncertain Data Streams

Recently, data mining over uncertain data streams has attracted a lot of attentions because of the widely existed imprecise data generated from a variety of streaming applications. In this paper, we try to resolve the problem of clustering over uncertain data streams. Facing uncertain tuples with different probability distributions, the clustering algorithm should not only consider the tuple value but also emphasis on its uncertainty. To fulfill these dual purposes, a metric named tuple uncertainty will be integrated into the overall procedure of clustering. Firstly, we survey uncertain data model and propose our uncertainty measurement and corresponding properties. Secondly, based on such uncertainty quantification method, we provide a two phase stream clustering algorithm and elaborate implementation detail. Finally, performance experiments over a number of real and synthetic data sets demonstrate the effectiveness and efficiency of our method.
Who Owns the Media?*

We examine the patterns of media ownership in 97 countries around the world. We find that almost universally the largest media firms are owned by the government or by private families. Government ownership is more pervasive in broadcasting than in the printed media. We then examine two theories of government ownership of the media: the public interest (Pigouvian) theory, according to which government ownership cures market failures, and the public choice theory, according to which government ownership undermines political and economic freedom. The data support the second theory.
Bourdieu and the gendered social structure of working time: A study of self-employed human resources professionals

This article uses the sociology of Bourdieu to explore the social structure of working time and uses this approach to analyse interview data from 25 self-employed human resources professionals practicing in the UK. Bourdieu’s approach to exploring resources, as forms of capital that are deployed strategically by actors within social fields, is used to compare outcomes for respondents with different working time patterns. The findings demonstrate that self-employed professionals’ uses of resources are affected by distinctive and gendered temporal rhythms within and between social fields. These temporal patterns typically serve the interests of well-resourced (more typically male) actors who structure their lives according to specific routines. Self-employed people with less working time often struggle to synchronize their lives with their environments and so are often at a disadvantage in accessing and using resources. The analysis, which develops novel propositions about the ways in which actors become differentially adapted to the social structure of time, facilitates a more fine-grained and relational appreciation of gendered advantages within self-employed careers, which is likely to have wider applicability and the potential for broader impact.
OCCLUSION HANDLING FOR PEDESTRIAN TRACKING USING PARTIAL OBJECT TEMPLATE-BASED COMPONENT PARTICLE FILTER

Pedestrian tracking plays a crucial role in security and intelligent video surveillance. Occlusion handling is a challenging concern in tracking multiple people. Adaptive, advanced solutions are required to accurately track pedestrians for video surveillance purposes. This paper presents a novel method of tracking multiple people in occlusion conditions. In this study, we developed a combined componentbased human-shaped template and a particle filter, resulting in improved object occlusion handling. The proposed system is capable of tracking specific people in real-time, as well as handling the occlusion of multiple objects. We predicted occlusions using the Kalman filter, and tracked objects continuously using our component-shape-template particle filter. The experimental results show that our algorithm is feasible and stable. In tests, the proposed tracking algorithm achieved an accuracy of up to 99.7%. The proposed approach outperformed that of comparable methods in analyzing all test video datasets in this study. Our low false-negative rate demonstrates that the proposed tracking method is robust and offers superior occlusion handling.
Combining Edges and Points for Interactive Anti-Aliased Rendering

This paper presents a new rendering and display paradigm that uses both discontinuities (edges) and sparsely sampled shading (points) to interactively generate anti-aliased images of complex scenes. Geometric and shadow discontinuities in the image are found at interactive rates using a novel data structure, the Normal–Position Interval tree, and algorithms based on interval arithmetic. After projecting discontinuities onto the image plane, shading information is interpolated from nearby point samples while never interpolating across a discontinuity edge. We introduce a new compact representation of the discontinuities and point samples called the edge-andpoint image. An efficient interpolation algorithm uses this image to generate anti-aliased output images at interactive rates without using supersampling. Our rendering technique is extensible, permitting the use of arbitrary shaders to collect radiance samples. Our software implementation supports interactive navigation and object manipulation in scenes that include complex lighting effects (such as global illumination) and geometrically complex objects. We show that highquality anti-aliased images of these scenes can be rendered at several frames per second on typical desktop machines.
Ambulatory-assessment Based Sampling Method:Experience Sampling Method

The generalizability of research findings is the golden standard of psychological research.To achieve this goal,researchers constantly develop more realistic research methods with higher ecological validity.Experience sampling method(ESM),a method of data collection by which participants respond to repeated assessments at moments during the course of the time in real situation,is highly favored by various researchers since it was proposed.This article introduced this data collection method and its statistical analyses processes,compared and elaborated the strengths and weakness of this method,and illustrated its main application fields.Finally,the future studies of experience sampling method were discussed as well.
Planktonic carbon cycling and transport in surface waters of the highly urbanized Hudson River estuary

We examined variations in organic carbon (OC) pools and microplanktonic carbon fluxes at three stations in the Hudson River estuary during 12 cruises between Octobers of 1996 and 1998. Phytoplankton biomass and net primary production varied from 5 to 40 µmol C L−1 and 0.3 to 318.3 mmol C m−2 d−1, respectively. Biomass and production of bacterioplankton in the surface layer commonly exceeded those of phytoplankton, varying from 0.2 to 72 mmol C L−1 and 1.4 to 70 mmol C m−2 d−1, respectively. Median planktonic respiration varied from 275 to 605 mmol CO2 m−2 d−1 between stations along the salinity gradient. Primary production/respiration (P :R) ratios varied temporally and spatially between 0.003 and 6.60, averaging 0.22. Carbon mass balances revealed that under low river discharges (<250 m3 s−1), the estuary processed 2.4‐fold more carbon internally and advected 2.7‐fold less carbon seaward than under higher flows. Annual OC budgets suggest that ~19 x 109 mol C yr−1 of total organic carbon (TOC) entered the estuary, whereas 30 x 109 mol TOC yr−1 was exported seaward, representing a net gain of 11 x 109 mol TOC yr−1 within the estuary. Microplankton processed ~32 x 109 mol C yr−1, of which 5.6 x 109 mol C was attributed to photo‐ and chemoautotrophic (~72%) and heterotrophic (~28%) production; the remainder was respired to CO2 (26.5 x 109 mol CO2 yr−1). The persistent imbalance between carbon oxidation and carbon fixation strongly suggests that the Hudson River estuary usually is net heterotrophic and is a significant source of inorganic carbon to the coastal ocean and atmosphere.
RNA-Guided Human Genome Engineering via Cas9

Genome Editing Clustered regularly interspaced short palindromic repeats (CRISPR) function as part of an adaptive immune system in a range of prokaryotes: Invading phage and plasmid DNA is targeted for cleavage by complementary CRISPR RNAs (crRNAs) bound to a CRISPR-associated endonuclease (see the Perspective by van der Oost). Cong et al. (p. 819, published online 3 January) and Mali et al. (p. 823, published online 3 January) adapted this defense system to function as a genome editing tool in eukaryotic cells. A bacterial genome defense system is adapted to function as a genome-editing tool in mammalian cells. [Also see Perspective by van der Oost] Bacteria and archaea have evolved adaptive immune defenses, termed clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) systems, that use short RNA to direct degradation of foreign nucleic acids. Here, we engineer the type II bacterial CRISPR system to function with custom guide RNA (gRNA) in human cells. For the endogenous AAVS1 locus, we obtained targeting rates of 10 to 25% in 293T cells, 13 to 8% in K562 cells, and 2 to 4% in induced pluripotent stem cells. We show that this process relies on CRISPR components; is sequence-specific; and, upon simultaneous introduction of multiple gRNAs, can effect multiplex editing of target loci. We also compute a genome-wide resource of ~190 K unique gRNAs targeting ~40.5% of human exons. Our results establish an RNA-guided editing tool for facile, robust, and multiplexable human genome engineering.
Salient Region of Textured 3D Model

In this paper, we propose a salient region detection method for textured 3D models. We define the salient region, not only based on geometry properties, such as mean curvature, but also based on texture properties, such as texture color. Feature maps of geometry and texture are combined to form a saliency map for capturing the visually important regions of a textured 3D model. During the feature maps construction, we apply a novel filter window to obtain proper sample vertices and texels from the 3D geometry domain and 2D texture image domain. Given the saliency map, we have developed a model reduction method, which comprises the geometry simplification mechanism and the texture space optimization mechanism. Experimental results show that the salient regions of a model can be well preserved even if the model texture and geometry are simplified heavily.
Distributed under Creative Commons Cc-by 4.0 the Metabolic Cost of Walking on an Incline in the Peacock (pavo Cristatus)

Altering speed and moving on a gradient can affect an animal's posture and gait, which in turn can change the energetic requirements of terrestrial locomotion. Here, the energetic and kinematic effects of locomoting on an incline were investigated in the Indian peacock, Pavo cristatus. The mass-specific metabolic rate of the Indian peacock was elevated on an incline, but this change was not dependent on the angle ascended and the cost of lifting remained similar between the two inclines (+5 and +7 •). Interestingly, the Indian peacock had the highest efficiency when compared to any other previously studied avian biped, despite the presence of a large train. Duty factors were higher for birds moving on an incline, but there was no difference between +5 and +7 •. Our results highlight the importance of investigating kinematic responses during energetic studies, as these may enable explanation of what is driving the underlying metabolic differences when moving on inclines. Further investigations are required to elucidate the underlying mechanical processes occurring during incline movement.
Haze in the Klang Valley of Malaysia

Continuous measurements of dry aerosol light scattering (Bsp) were made at two sites in the Klang Valley of Malaysia between December 1998 and December 2000. In addition 24-h PM2.5 samples were collected on a one-day-in-six cycle and the chemical composition of the aerosol was determined. Periods of excessive haze were defined as 24-h average Bsp values greater than 150 Mm-1 and these occurred on a number of occasions, between May and September 1999, during May 2000, and between July and September 2000. The evidence for smoke being a significant contributor to aerosol during periods of excessive haze is discussed and includes features of the aerosol chemistry, the diurnal cycle of Bsp, and the coincidence of forest fires on Sumatra during the southwest (SW) monsoon period, as well as transport modelling for one week of the southwest Monsoon of 2000. The study highlights that whilst transboundary smoke is a major contributor to poor visibility in the Klang Valley, smoke from fires on Peninsular Malaysia is also a contributor, and at all times, the domestic source of secondary particle production is present.
Variations in the Spectral Slope of Sagittarius A* during a Near-Infrared Flare

We have observed a bright flare of Sgr A* in the near-infrared with the adaptive optics-assisted integral-field spectrometer SINFONI. Within the uncertainties, the observed spectrum is featureless and can be described by a power law. Our data suggest that the spectral index is correlated with the instantaneous flux and that both quantities experience significant changes within less than 1 hour. We argue that the near-infrared flares from Sgr A* are due to synchrotron emission of transiently heated electrons, the emission being affected by orbital dynamics and synchrotron cooling, both acting on timescales of ≈20 minutes.
SciDAC Institute for Ultra-Scale Visualization DE-FG 02-06 ER 06-04 and LAB-06-04

Understanding the science behind ultra-scale simulations and high-throughput experiments requires extracting meaning from data sets of hundreds of terabytes or more. Parallel visualization is the most plausible path to understanding data at this scale; however, existing parallel visualization tools have limited functionality, are not portable or scalable to our largest systems, or are not readily adapted to new applications. Our proposed SciDAC Institute for Ultra-scale Visualization will assemble the scalable parallel visualization infrastructure needed to enable knowledge discovery at this scale and instruct application scientists on how to best use these tools. The Institute will bring together leading experts from visualization, high-performance computing, and science application areas to make parallel visualization technology a commodity for SciDAC scientists and the broader community. We will develop high-performance visualization strategies on diverse platforms including general-purpose clusters, dedicated visualization clusters, and especially high-end computing systems, leveraging existing technologies when appropriate. We will produce benchmarks of parallel visualization tools as guides for scientists and as methods to assess the capabilities of these tools on new ultra-scale systems. We will educate SciDAC application scientists and the broader community through an active outreach program, ensuring that our knowledge and technologies are widely disseminated. This integrated set of activities is strengthened by our SciDAC application connections, who serve as early adopters, and computer science partners, whose tools combine with those developed in the Institute to further aid in the scientific process.
Examination of Organizational Information Security Strategy: A Pilot Study

The prevailing approach to cyber security continues to be the implementation of controls—technical, formal, and informal. We have seen little departure from a fundamentally preventive strategy. The criminal justice field has called for an increased emphasis on deterrence strategies, specifically Situational Crime Prevention (SCP). This paper presents the results of an exploratory (pilot) study based on interviews of CISOs (or approximate equivalents). We found that while the balance of controls does appear to be improving, technical controls are still the priority— particularly in small organizations. We found that IS security strategies are still predominantly preventive; organizations do not view offender deterrence as a strategy. The respondents definitely see room for strategic improvement. By and large, the information security professionals interviewed believe that cyber offenders are rational decision makers, that reducing anticipated benefit would be the most lucrative influence, followed by perceived effort required and perceived risk of being caught, in that order.
Extensions and Limitations of the Neural GPU

The Neural GPU is a recent model that can learn algorithms such as multi-digit binary addition and binary multiplication in a way that generalizes to inputs of arbitrary length. We show that there are two simple ways of improving the performance of the Neural GPU: by carefully designing a curriculum, and by increasing model size. The latter requires a memory efficient implementation, as a naive implementation of the Neural GPU is memory intensive. We find that these techniques increase the set of algorithmic problems that can be solved by the Neural GPU: we have been able to learn to perform all the arithmetic operations (and generalize to arbitrarily long numbers) when the arguments are given in the decimal representation (which, surprisingly, has not been possible before). We have also been able to train the Neural GPU to evaluate long arithmetic expressions with multiple operands that require respecting the precedence order of the operands, although these have succeeded only in their binary representation, and not with perfect accuracy. In addition, we gain insight into the Neural GPU by investigating its failure modes. We find that Neural GPUs that correctly generalize to arbitrarily long numbers still fail to compute the correct answer on highly-symmetric, atypical inputs: for example, a Neural GPU that achieves near-perfect generalization on decimal multiplication of up to 100-digit long numbers can fail on $000000\dots002 \times 000000\dots002$ while succeeding at $2 \times 2$. These failure modes are reminiscent of adversarial examples.
Design of a dual band monopole antenna for WLAN/WiMAX applications

In this paper, a novel dual-band design of a coplanar waveguide (CPW)-fed monopole antenna with slots on the radiating patch is proposed. The parametric study is performed to understand the characteristics of the proposed antenna. The pair of slots is incorporated along the edges of the rectangle shaped structure to excite three resonant modes. Prototypes of the proposed antenna have been designed and constructed. The simulation results shows that antenna operates in two wide frequency bands from 3.404 GHz to 3.804 GHz and 5.032 GHz to 5.932 GHz covering wireless local area network (WLAN) and Worldwide interoperability for microwave access (WiMAX) bands. The maximum gain of the proposed antenna is 3.31 dBi at 3.66 GHz in the lower band and 4.39 dBi at 5.124 GHz in the higher band.