Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Multi-User Multi-Cell Massive Spatial Modulation Uplink: How Many Antennas for Each User?

Massive spatial modulation aided multiple-input multiple-output (SM-MIMO) systems have recently been proposed as a novel combination of SM and of conventional massive MIMO, where the base station (BS) is equipped with a large number of antennas and simultaneously serves multiple user equipment (UE) that employ SM for their uplink transmission. Since the massive SM-MIMO concept combines the benefits of both the SM and massive MIMO techniques, it has recently attracted substantial research interest. In this paper, we study the achievable uplink spectral efficiency (SE) of a multi-cell massive SM-MIMO system, and derive closed-form expressions to asymptotically lower-bound the SE yielded by two linear BS combining schemes, including maximum ratio combining and zero forcing combining, when a sufficiently large number of BS antennas are equipped. The derivation takes into account the impact of transmitter spatial correlations, imperfect channel estimations, user-specific power controls, and different pilot reuse factors. The proposed asymptotic bounds are shown to be tight, even when the scale of BS antennas is limited. The new SE results facilitate a system-level investigation of the optimal number of uplink transmit antennas (TAs) $N$ with respect to SE maximization. Explicitly, we provide theoretical insights on the SE of massive SM-MIMO systems. Furthermore, we demonstrate that massive SM-MIMO systems are capable of outperforming the SE of conventional massive MIMOs relying on single-TA UEs.
Unsupervised Abnormal Behavior Detection for Real-Time Surveillance Using Observed History

This paper presents a novel method of utilizing observed history for detecting abnormal behaviors in surveillance applications. An unsupervised algorithm is proposed to detect abnormal behaviors and re-train itself in real-time. Motion vectors of objects are estimated using the optical flow method. Encoded feature vectors are stored in an observation matrix, abnormal behaviors can be detected by applying principal copmonent analysis (PCA) on the matrix. This method has been evaluated under both indoor and outdoor surveillance scenarios. It demonstrates promising results that this detection procedure is able to discover abnormal behaviors and adapt to changes in the behavioral patterns incrementally.
An Energy Saving Routing Algorithm for a Green OSPF Protocol

In this paper we analyze the challenging problem of energy saving in IP networks. A novel network-level strategy based on a modification of current link-state routing protocols, such as OSPF, is proposed; according to this strategy, IP routers are able to power off some network links during low traffic periods. The proposed solution is a three-phases algorithm: in the first phase some routers are elected as "exporter" of their own Shortest Path Trees (SPTs); in the second one the neighbors of these routers perform a modified Dijkstra algorithm to detect links to power off; in the last one new network paths on a modified network topology are computed. Performance study shows that, in an actual IP network, even more than the 60% of links can be switched off.
Data-Replicas Scheduler for Heterogeneous MapReduce Cluster

Large scale data processing has rapidly increased in nowadays. MapReduce programming model, which is firstly mentioned in functional languages, appeared in distributed system and perform excellently in large scale data processing since 2006. Hadoop, which is the most popular framework of open-sourced MapReduce runtime environment, supplies reliable, scalable and distributed system processing large scale data across clusters of computers using this virtue programming model. In this system, files are split into many blocks and all blocks are replicated over several computers in clusters. To process these blocks efficiently, each job runs parallel and is divided into many tasks which deals with a file block. In order to fully take advantage of network bandwidth these systems, data locality is paid more and more attentions. Considering the existence of data-replica blocks, we propose a data-replicas scheduler which includes task scheduling and data allocation. The data-replicas scheduler takes fully advantage of data replicas in local Data node, reduce the costs of data transfer and improve the system performance. The results of experiments show that our scheduler not only improves the CPU ratio, but also reduces the packets that transfer in the network.
Labor Market Impacts of a Large-Scale Public Works Program: Evidence from the Indian Employment Guarantee Scheme

Recent years have seen an increasing interest in using public-works programs as anti-poverty measures in developing countries. This paper analyzes the rural labor market impacts of the Indian National Rural Employment Guarantee Scheme, one of the most ambitious programs of its kind, by using a regression discontinuity design. I find that private-sector wages increase substantially for women, but not for men, and that these effects are concentrated during the main agricultural season. In contrast, there is little evidence for negative private employment effects.
The zCOSMOS redshift survey: the role of environment and stellar mass in shaping the rise of the morphology-density relation from z ~ 1

For more than two decades we have known that galaxy morphological segregation is present in the Local Universe. It is important to see how this relation evolves with cosmic time. To investigate how galaxy assembly took place with cosmic time, we explore the evolution of the morphology-density relation up to redshift z~1 using about 10000 galaxies drawn from the zCOSMOS Galaxy Redshift Survey. Taking advantage of accurate HST/ACS morphologies from the COSMOS survey, of the well-characterised zCOSMOS 3D environment, and of a large sample of galaxies with spectroscopic redshift, we want to study here the evolution of the morphology-density relation up to z~1 and its dependence on galaxy luminosity and stellar mass. The multi-wavelength coverage of the field also allows a first study of the galaxy morphological segregation dependence on colour. We further attempt to disentangle between processes that occurred early in the history of the Universe or late in the life of galaxies. The zCOSMOS field benefits of high-resolution imaging in the F814W filter from the Advanced Camera for Survey (ACS). We use standard morphology classifiers, optimised for being robust against band-shifting and surface brightness dimming, and a new, objective, and automated method to convert morphological parameters into early, spiral, and irregular types. We use about 10000 galaxies down to I_AB=22.5 with a spectroscopic sampling rate of 33% to characterise the environment of galaxies up to z~1 from the 100 kpc scales of galaxy groups up to the 100 Mpc scales of the cosmic web. ABRIDGED
Genome-Wide Scan Reveals LEMD3 and WIF1 on SSC5 as the Candidates for Porcine Ear Size

The quantitative trait loci (QTL) for porcine ear size was previously reported to mainly focus on SSC5 and SSC7. Recently, a missense mutation, G32E, in PPARD in the QTL interval on SSC7 was identified as the causative mutation for ear size. However, on account of the large interval of QTL, the responsible gene on SSC5 has not been identified. In this study, an intercross population was constructed from the large-eared Minzhu, an indigenous Chinese pig breed, and the Western commercial Large White pig to examine the genetic basis of ear size diversity. A GWAS was performed to detect SNPs significantly associated with ear size. Thirty-five significant SNPs defined a 10.78-Mb (30.14–40.92 Mb) region on SSC5. Further, combining linkage disequilibrium and haplotype sharing analysis, a reduced region of 3.07-Mb was obtained. Finally, by using a selective sweep analysis, a critical region of about 450-kb interval containing two annotated genes LEMD3 and WIF1 was refined in this work. Functional analysis indicated that both represent biological candidates for porcine ear size, with potential application in breeding programs. The two genes could also be used as novel references for further study of the mechanism underlying human microtia.
In Proceedings of the AAAI 2008 Workshop on Enhanced Messaging Learning Email Procedures for the Desktop

In this electronic age, we are all knowledge workers, tackling on a daily basis the information that flows through our email, file systems, web browsers, calendars, and various other desktop applications. Email has come to be the center of desktop activity for many of us: we set up meetings, exchange documents, manage projects, forward interesting links, track requisitions, and perform a whole host of tasks through email. Many of these activities involve information flowing from one action to the next, across applications. They are also often repetitive, structured procedures, involving an ordered set of steps, many but not all of which are amenable to automation. In this paper, we present our approach to learning generalized dataflow procedures on the desktop from demonstrations by the user. Our system LAPDOG runs on the CALO desktop assistant and is capable of acquiring fully or partially automated procedures, spanning multiple applications, from demonstrations that may include unobservable actions.
The Level Set Method Lecture Notes , MIT 16 . 920 J / 2 . 097 J / 6 . 339 J Numerical Methods for Partial Differential Equations

Evolving boundaries or interfaces are part of many problems in science and engineering. In 1988, James A. Sethian and Stanley Osher proposed to represent these boundaries implicitly and model their propagation using appropriate partial differential equations. The boundary is given by level sets of a function φ(x), and they named their technique the Level Set Method. These notes give a short introduction to the method, and for more details we refer to the books by Sethian [4] and Osher [1], as well as their original paper [2]. Consider a boundary curve in two dimensions or a surface in three dimensions, see Figure 1. We are given a velocity field v, which in general depends on space, time, properties of the boundary (such as the normal direction and the curvature), as well as an indirect dependence from physical simulations using the boundary shape. To goal is to accurately model the evolution of the boundary under the velocities v. In many cases we are only interested in the motion normal to the interface. We can then use a (scalar) speed function F , and let the velocities be given by v = Fn, where n is the normal direction.
Can feedback increase the degrees of freedom of a wireless network with delayed CSIT?

There is no known network with instantaneous channel state information at the transmitters (CSIT) for which (output) feedback enhances the degrees of freedom (or the signaling dimensions) of the network. In this paper, we show that the DoF of wireless networks with delayed CSIT however, can increase with output feedback. This result is shown by obtaining the degrees of freedom (DoF) region for the multiple-input multiple-output (MIMO) fast fading interference channel (IC) with an arbitrary number of antennas at each node, under the Shannon feedback setting wherein the channel matrices (i.e., the channel state) and the channel outputs are available at the transmitters with a finite delay. While for most numbers of antennas at the four terminals, it is shown that the DoF region with Shannon feedback is the same as the DoF region with just delayed CSIT, it is shown that this is not always the case. For a specific class of MIMO ICs characterized by a certain relationship between the numbers of antennas at the four nodes, the DoF region with Shannon feedback is strictly bigger than the DoF region with just delayed CSIT. To realize these DoF gains attainable with Shannon feedback, a new retrospective interference alignment scheme is developed wherein transmitter cooperation made possible by output feedback in addition to delayed CSIT is employed to effect a more efficient form of interference alignment than feasible with previously known schemes that require just delayed CSIT.
Beyond the adoption/ non-adoption dichotomy: the impact of innovation characteristics on potential adopters' transition through adoption process stages

Research on innovation adoption has suffered from a bias towards understanding the factors that affect the dichotomous adoption/non-adoption decision. Much less attention is devoted to the question why potential adopters fail to progress to the adoption stage from earlier stages in the decision making process. Such knowledge is essential to understand what factors actually underlie the non-adoption of an innovation. As perceived innovation characteristics have been found to influence adoption in a substantial way, we develop hypotheses on their influence not only with respect to the adoption stage, but with respect to previous stages of the adoption process as well. Specifically, we develop hypotheses on the perceived levels and importance of relative advantage, compatibility, complexity and perceived risk in the awareness, evaluation, and adoption stages of the innovation adoption process. The hypotheses are tested using both multivariate analysis of variance and multinomial logit modeling on a sample of 242 organizations, focusing on the adoption process of electronic banking. The results show that the levels of perceived relative advantage and compatibility increase over the stages of the adoption process, whereas the perceived levels of complexity and risk largely decrease. The influence of the characteristics across the adoption stages shows that positive beliefs related to the innovation have highest salience in the initial stage of the process, whereas the salience of perceived complexity - generally considered an undesirable attribute - is highest in the final stage. In sum, our results imply that non-adopters are affected by innovation characteristics in a different way, depending on their stage in the adoption process, and therefore should not be considered as one homogeneous group of "potential adopters". These findings have important implications for marketing innovations.
Hierarchical Content Stores in High-Speed ICN Routers: Emulation and Prototype Implementation

Recent work motivates the design of Information-centric routers that make use of hierarchies of memory to jointly scale in the size and speed of content stores. The present paper advances this understanding by (i) instantiating a general purpose two-layer packet-level caching system, (ii) investigating the solution design space via emulation, and (iii) introducing a proof-of-concept prototype. The emulation-based study reveals insights about the broad design space, the expected impact of workload, and gains due to multi-threaded execution. The full-blown system prototype experimentally confirms that, by exploiting both DRAM and SSD memory technologies, ICN routers can sustain cache operations in excess of 10Gbps running on off-the-shelf hardware.
Reduced complexity online sparse signal reconstruction using projections onto weighted ℓ1 balls

This paper presents a novel online method for sparse signal reconstruction. In particular, the notion of sub-dimensional projections is introduced, which allows a significant complexity reduction in the Adaptive Projection-based Algorithm using Weighted ℓ1 balls (APWL1). This is achieved without sacrificing performance. The proposed method is evaluated in both stationary and time-varying environments and its performance is compared with state-of-the-art online and batch LASSO-based methods.
Occurrence of Lipophilic Marine Toxins in Shellfish from Galicia (NW of Spain) and Synergies among Them

Lipophilic marine toxins pose a serious threat for consumers and an enormous economic problem for shellfish producers. Synergistic interaction among toxins may play an important role in the toxicity of shellfish and consequently in human intoxications. In order to study the toxic profile of molluscs, sampled during toxic episodes occurring in different locations in Galicia in 2014, shellfish were analyzed by liquid chromatography tandem mass spectrometry (LC–MS/MS), the official method for the detection of lipophilic toxins. The performance of this procedure was demonstrated to be fit for purpose and was validated in house following European guidelines. The vast majority of toxins present in shellfish belonged to the okadaic acid (OA) group and some samples from a particular area contained yessotoxin (YTX). Since these toxins occur very often with other lipophilic toxins, we evaluated the potential interactions among them. A human neuroblastoma cell line was used to study the possible synergies of OA with other lipophilic toxins. Results show that combination of OA with dinophysistoxin 2 (DTX2) or YTX enhances the toxicity triggered by OA, decreasing cell viability and cell proliferation, depending on the toxin concentration and incubation time. The effects of other lipophilic toxins as 13-desmethyl Spirolide C were also evaluated in vitro.
Classifying modality learning styles based on Production-Fuzzy Rules

Adaptive Intelligent Web Based Education System, (AIWBES) is an education technology which has been used world-wide. An Intelligent and adaptive AIWBES is materialized from the combination of Users' Model, Knowledge Based and Inference Engine. The development of adaptation or personalization in AIWBES will provide an Intelligence system for the users to obtain knowledge and information. This paper will focus on the user model to enhance AIWBES personalization based on its users' modality learning style. The objective of this paper is to compare the precision between Production-Fuzzy Rule and Naives Bayes for classifying modality learning styles in the user model. A prototype namely K-Stailo, is developed. These two different techniques were applied in K-Stailo. A test was carried out by the researcher to evaluate the precision between these two techniques. The results show that Production — Fuzzy Rule is the better technique when compared to Naives Bayes in user's modality learning style prediction.
Are External Technology Sourcing Strategies Substitutes or Complements?: The Case of Embodied versus Disembodied Technology Acquisition

This paper analyzes the choice between different external technology sourcing activities of a firm. On the one hand, the firm can acquire new technology which is embodied in personnel. On the other hand, the firm can obtain new technology disembodied through a licensing agreement or by outsourcing the technology development from an R&D contractor. Building on Cassiman and Veugelers (2006), we test whether embodied and disembodied technology acquisitions are complementary activities or rather behave as substitute technology acquisition alternatives. We find that while internal and external technology acquisition are complementary innovation activities, the actual choice of external technology sourcing between embodied or disembodied modes is substitutive for smaller firms. The evidence for larger firms suggests that different external technology sourcing activities are complementary, but in this case the results are suggestive although not strongly significant.
Fisher Information of Mine Collapse Hole Detection Based on Sensor Nodes Connectivity

It is very important to detect a collapse hole for coal mine workers. The possibility of detecting the collapse hole using WSN is presented because the tunnel in coal mine is narrow and has poor working condition. Comparing three types of the hole detection methods, it is seen that the connectivity-based methods are used to detect coal mine collapse better than other methods. By establishing a 2D model of the collapse hole in coal mine, a class of algorithms for detecting the collapse hole in coal mine is described. Based on log-normal shadowing channel model, the accuracy of detecting the collapse hole in coal mine using Fisher information is analyzed. Numerical calculation shows that connectivity-based localization schemes are better to detect collapse hole of coal mine.
Comparison of LCL-Filter-Based PWM Rectifier with Different Current Sensor Positions

For LCL-based PWM rectifier, there are two options of sensing currents. Either grid side currents or converter side currents can be selected as the feedback signals for the current control loop. The selection of sensor position makes the design of the control system different. In this paper, the control stability and dynamic performance of LCL filter-based PWM rectifier with converter side current control or with grid side current control are compared. System stability and dynamic performance are compared with taking into account the effect of all possible variants. Further more, the comparison results provide a very useful design guideline for LCL filter-based PWM rectifiers. Finally experimental results prove the theoretical analysis.
Image mining for robot vision based on concept analysis

- In the process of image mining for robot vision, concept analysis is an important technique. The paper proposes a novel framework of image mining for robot vision based on concept lattice theory and cloud model theory. Concept lattice reflects the process of human's concept formation with mathematical formal language. Cloud model is a transformation model between qualitative concepts and quantitative numerical values. Image mining for robot vision is considered as a process of concept extraction from different granularities (image pixels, image pixel groups, image features, image objects, image files and image databases). The methods of image mining from image features(texture features, color features, shape features, spatial relationship features) are introduced in the paper, which include the following basic steps: firstly pre-process images, secondly use cloud model to extract concepts, lastly use concept lattice to extract a series of image knowledge(association rules, clustering rules and classification rules). At last, a software prototype is designed and developed, and some experiments confirm the validity of the proposed framework.
Wavepacket models for supersonic jet noise

Abstract Gudmundsson and Colonius (J. Fluid Mech., vol. 689, 2011, pp. 97–128) have recently shown that the average evolution of low-frequency, low-azimuthal modal large-scale structures in the near field of subsonic jets are remarkably well predicted as linear instability waves of the turbulent mean flow using parabolized stability equations. In this work, we extend this modelling technique to an isothermal and a moderately heated Mach 1.5 jet for which the mean flow fields are obtained from a high-fidelity large-eddy simulation database. The latter affords a rigourous and extensive validation of the model, which had only been pursued earlier with more limited experimental data. A filter based on proper orthogonal decomposition is applied to the data to extract the most energetic coherent components. These components display a distinct wavepacket character, and agree fairly well with the parabolized stability equations model predictions in terms of near-field pressure and flow velocity. We next apply a Kirchhoff surface acoustic propagation technique to the near-field pressure model and obtain an encouraging match for far-field noise levels in the peak aft direction. The results suggest that linear wavepackets in the turbulence are responsible for the loudest portion of the supersonic jet acoustic field.