Miracle-dz/UniFAR-FTUs huggingface dataset:
Opportunistic Link Scheduling, Power Control, and Routing for Multi-hop Wireless Networks over Time Varying Channels

Abstract : We consider a cross-layer optimization problem for multi-hop wireless networks over time varying channels. The system consists of L interfering links, where the transmission power and rate of link l(= 1, ., L) are specified in vectors P= [P(1) . P(L)] and X= [X(1). XL] respectively. In every time slot, the scheduler schedules the transmissions by assigning a resource allocation vector V= [P X]. We denote the expectation of V by E(V). Our objective is to find the optimal scheduling policy which minimizes the cost of average resource consumption while maintaining average service guarantees to each user. We develop a unified framework, in which the cost of the average resource consumption is given by a convex function f(E(V)) and the minimum average service guarantees are given by a set of convex constraints g (E(V)) less than or equal to 0. By means of convex optimization and stochastic approximation, we obtain the solution by solving the corresponding dual problem. An iterative algorithm is proposed and analyzed, which schedules the transmission powers and rates adapting to the channel variations. If the channel states are described by a finite-state mixing process, it is shown that our algorithm asymptotically attains the optimal cost.
Sparse representation to localize objects in underwater acoustic images

Screening of underwater images for the presence of submerged objects and phenomena like submarine volcanoes is based on the spotting and localization of objects present in underwater. This paper proposes a dictionary based approach in which a dictionary is created from various sub-images of the target image. The dictionary of sub-images is created for multiple scales. The coefficients are computed by representing the sub-images as a combination of dictionary elements in a linear fashion for the chosen input image. Using these coefficients the confidence measure is determined. This measure designates the likeliness of the presence of the desired object. The confidence measures are computed independently for red and gray scale images which are coalesced to form a confidence map. Blob detection is a set of methods whose objective is to identify parts in an image which have properties namely colour and brightness different from that of the surrounding regions. The maximum response is expected to be at the location of the desired object. This can be determined by running a blob detector on the confidence map.
Explaining Technical Change

Technical change, defined as the manufacture and modification of tools, is generally thought to have played an important role in the evolution of intelligent life on earth, comparable to that of language. In this volume, first published in 1983, Jon Elster approaches the study of technical change from an epistemological perspective. He first sets out the main methods of scientific explanation and then applies those methods to some of the central theories of technical change. In particular, Elster considers neoclassical, evolutionary, and Marxist theories, whilst also devoting a chapter to Joseph Schumpeter's influential theory.
Coal Fly Ash Impairs Airway Antimicrobial Peptides and Increases Bacterial Growth

Air pollution is a risk factor for respiratory infections, and one of its main components is particulate matter (PM), which is comprised of a number of particles that contain iron, such as coal fly ash (CFA). Since free iron concentrations are extremely low in airway surface liquid (ASL), we hypothesize that CFA impairs antimicrobial peptides (AMP) function and can be a source of iron to bacteria. We tested this hypothesis in vivo by instilling mice with Pseudomonas aeruginosa (PA01) and CFA and determine the percentage of bacterial clearance. In addition, we tested bacterial clearance in cell culture by exposing primary human airway epithelial cells to PA01 and CFA and determining the AMP activity and bacterial growth in vitro. We report that CFA is a bioavailable source of iron for bacteria. We show that CFA interferes with bacterial clearance in vivo and in primary human airway epithelial cultures. Also, we demonstrate that CFA inhibits AMP activity in vitro, which we propose as a mechanism of our cell culture and in vivo results. Furthermore, PA01 uses CFA as an iron source with a direct correlation between CFA iron dissolution and bacterial growth. CFA concentrations used are very relevant to human daily exposures, thus posing a potential public health risk for susceptible subjects. Although CFA provides a source of bioavailable iron for bacteria, not all CFA particles have the same biological effects, and their propensity for iron dissolution is an important factor. CFA impairs lung innate immune mechanisms of bacterial clearance, specifically AMP activity. We expect that identifying the PM mechanisms of respiratory infections will translate into public health policies aimed at controlling, not only concentration of PM exposure, but physicochemical characteristics that will potentially cause respiratory infections in susceptible individuals and populations.
Building the Innovative and Entrepreneurial University: An Institutional Case Study of Administrative Academic Capitalism

Although researchers have explored dimensions of academic capitalism among students and faculty members, knowledge of the roles of administrators at all levels is underdeveloped in the literature. This institutional case study of a public research-extensive university examines the roles of executive and managerial administrators in bringing a strategic priority of innovation and entrepreneurship to fruition. Using an analytical framework based upon administrative academic capitalism and extended managerial capacity, the study draws upon 31 interviews with administrators, faculty, and students at the institutional case to identify five roles fulfilled by executive and managerial administrators in the facilitation of academic capitalism: building infrastructure, creating new programs, cultivating donors and raising funds, setting a vision around entrepreneurship, and changing policies. The findings show that an institutional orientation to knowledge privatization and profit taking was largely an administrator-driven project. Efforts to promote innovation and entrepreneurship engendered some conflict with faculty members, demonstrating the possible consequences of extended managerial control over processes of production in the academy.
Malaysian Principals’ Technology Leadership Practices and Curriculum Management

School leaders face challenges in using technology to upgrade teaching and learning processes in the twenty-first century as they must also become role models in its usage. Thus technology must be used not only for teaching and learning processes, but also in their organisational management. The aim of this research is first, to identify the level of technology leadership and competency in curriculum activity management among secondary school principals in Malaysia; second, to determine if technology leadership practices and curriculum activity management differ based on school location; third, to analyse if there is a correlation between technology leadership and competency of curriculum activity management. This quantitative style research used questionnaire for data collection involving 341 randomly selected Malaysian principals. Data collected was then analysed using descriptive and inference analysis. Findings show that Malaysian principals practice technology leadership at a very high level, and are also very competent at managing curriculum activity. T-test analysis found that urban principals’ technology leadership practices are significantly higher than their rural counterparts, but their curriculum management competencies are at the same level. Findings also show that there is a low yet significant correlation between technology leadership and curriculum management competencies. In light of this information, we recommend further in-depth studies to explore the rural-urban gap technology leadership scenario, thus finding possible solutions in narrowing the academic achieve- ment gap between rural and urban schools.
Planar millimetre-wave antenna simultaneously producing four orbital angular momentum modes and associated multi-element receiver array

The authors present the design and simulation of a planar Bull's eye antenna that can simultaneously produce four orbital angular momentum (OAM) modes with up to 2.4 GHz impedance bandwidth at 60 GHz. OAM modes l = ±3, ±13 are produced simultaneously by two ring resonators in the centre of the antenna. The peak gain is 13.1 (8.1) dBi at ±10 (±25) degrees for mode l = ±3 (±13), with all modes present over a 30° angular range (15–45° from the boresight). For reception and demultiplexing, the authors use a discretely sampled partial aperture receiver (DSPAR), and show algebraically that such systems can achieve ideal mode orthogonality in cases where existing partial aperture receivers cannot. The authors present a DSPAR design to complement the Bull's eye antenna, needing only four simple receiving elements and hence more readily scaling to work over longer link distances than systems relying on horn antennas with integrated spiral phase plates.
An Intelligent Context-Aware Service Engine Based on Ontology

In this paper, we propose an intelligent context-aware service engine based on two important standards, OSGi and ontology. OSGi platform provides bundle management mechanism, so that we can easily control and manage several services. For reuse of the ontology, we designed two level's ontology epsivneral context, domain context. The general context is generally used context concept in pervasive environment, and the domain concept is specified context ontology according domain area. As we design two level's ontology, any system which uses context in pervasive environment could reuse our context ontology. As we use context ontology, our system has the following useful aspects. First, our system can describe types, hierarchies, relations of contexts, so that any context instance can be accommodated into the system with no additional effort. Second, our system could generate semantically perfect high-level context which is suitable for services. Third, our system could be applied to semantic Web which is called a revolution of a post Web and could provide interoperability
Efficient map merging using a probabilistic generalized Voronoi diagram

Simultaneous Localization and Mapping, or SLAM, is required for mobile robots to be able to explore prior unknown space without a global positioning reference. While multiple robots can achieve the exploration task more quickly, this benefit comes with the cost of added complexity. Probabilistic occupancy grid maps from multiple agents must be merged in real-time without any prior knowledge of their relative transformation. In addition, the probabilistic information of the maps must be accounted for and fused accordingly. In this paper, a probabilistic version of the Generalized Voronoi Diagram (GVD), called the PGVD, is used to determine the relative transformation between maps and fuse them. The new method is effective for finding relative transformations quickly and reliably. In addition, the novel approach accounts for all map uncertainties in the fusion process.
Copy Number Deletion Has Little Impact on Gene Expression Levels in Racehorses

Copy number variations (CNVs), important genetic factors for study of human diseases, may have as large of an effect on phenotype as do single nucleotide polymorphisms. Indeed, it is widely accepted that CNVs are associated with differential disease susceptibility. However, the relationships between CNVs and gene expression have not been characterized in the horse. In this study, we investigated the effects of copy number deletion in the blood and muscle transcriptomes of Thoroughbred racing horses. We identified a total of 1,246 CNVs of deletion polymorphisms using DNA re-sequencing data from 18 Thoroughbred racing horses. To discover the tendencies between CNV status and gene expression levels, we extracted CNVs of four Thoroughbred racing horses of which RNA sequencing was available. We found that 252 pairs of CNVs and genes were associated in the four horse samples. We did not observe a clear and consistent relationship between the deletion status of CNVs and gene expression levels before and after exercise in blood and muscle. However, we found some pairs of CNVs and associated genes that indicated relationships with gene expression levels: a positive relationship with genes responsible for membrane structure or cytoskeleton and a negative relationship with genes involved in disease. This study will lead to conceptual advances in understanding the relationship between CNVs and global gene expression in the horse.
Prediction of ribosome footprint profile shapes from transcript sequences

Motivation: Ribosome profiling is a useful technique for studying translational dynamics and quantifying protein synthesis. Applications of this technique have shown that ribosomes are not uniformly distributed along mRNA transcripts. Understanding how each transcript-specific distribution arises is important for unraveling the translation mechanism. Results: Here, we apply kernel smoothing to construct predictive features and build a sparse model to predict the shape of ribosome footprint profiles from transcript sequences alone. Our results on Saccharomyces cerevisiae data show that the marginal ribosome densities can be predicted with high accuracy. The proposed novel method has a wide range of applications, including inferring isoform-specific ribosome footprints, designing transcripts with fast translation speeds and discovering unknown modulation during translation. Availability and implementation: A software package called riboShape is freely available at https://sourceforge.net/projects/riboshape Contact: yss@berkeley.edu
Robust principal component analysis?: Recovering low-rank matrices from sparse errors

The problem of recovering a low-rank data matrix from corrupted observations arises in many application areas, including computer vision, system identification, and bioinformatics. Recently it was shown that low-rank matrices satisfying an appropriate incoherence condition can be exactly recovered from non-vanishing fractions of errors by solving a simple convex program, Principal Component Pursuit, which minimizes a weighted combination of the nuclear norm and the ℓ1 norm of the corruption [1]. Our methodology and results suggest a principled approach to robust principal component analysis, since they show that one can efficiently and exactly recover the principal components of a low-rank data matrix even when a positive fraction of the entries are corrupted. These results extend to the case where a fraction of entries are missing as well.
Multi-Pass Decoding With Complex Feature Guidance for Statistical Machine Translation

In Statistical Machine Translation, some complex features are still difficult to integrate during decoding and usually used through the reranking of the k-best hypotheses produced by the decoder. We propose a translation table partitioning method that exploits the result of this reranking to iteratively guide the decoder in order to produce a new k-best list more relevant to some complex features. We report experiments on two translation domains and two translations directions which yield improvements of up to 1.4 BLEU over the reranking baseline using the same set of complex features. On a practical viewpoint, our approach allows SMT system developers to easily integrate complex features into decoding rather than being limited to their use in one-time k-best list reranking.
Cluster quality based performance evaluation of hierarchical clustering method

Clustering is an important phase in data mining. A number of different clustering methods are used to perform cluster analysis: Partitioning Clustering, hierarchical clustering, grid-based clustering, model-based, graph based clustering and density based clustering and so on. Hierarchical method helps us to cluster the data objects in the form of a tree known as hierarchy. And each node in hierarchy is known as the cluster. Hierarchical clustering can be performed in two ways: agglomerative clustering and divisive clustering. Agglomerative clustering is always more preferable. For a good cluster analysis, the quality of the clusters should be high. In this paper, we will measure the quality of clusters with the help of three parameters: Cohesion measurement, Silhouette index and Elapsed time.
An Access Control Model for Web-Services That Supports Delegation and Creation of Authority

We present a new access control model for XML Web-Services that provides users with two kinds of authorities: the authority to delegate their authorities to other users and the authority to create new authorities based on their own authorities. We developed this model by introducing capability- based access control to Web services. A capability consists of an object identifier and the list of permitted operations for that object. We map an authority of a Web-Services object to a capability of the object and express the capability as a description in Web Services Description Language (WSDL). Delegation of an authority corresponds to distribution of a capability, which is done by passing a WSDL description. Creation of a new authority corresponds to generating a restricted capability based on an original capability, which is done by stacking an object on an original object. Stacking objects also makes it possible to add new functions to existing Web-Services objects without modifying the existing objects. We demonstrate the effectiveness of the proposed model using a schedule management application, which enables a project leader to delegate his or her tasks to subordinates by comparing it with Google Calendar. We also show that the execution times of stackable objects are acceptable by comparing them with typical Internet delay.
Developing a Measure for Process Cluster Evaluation Developing a Measure for Process Cluster Evaluation

Our preliminary research identi ed multiple approaches to automatic process clustering. Prototypical tools implementing these approaches were developed. The resulting cluster hierarchies have been evaluated by comparing them to the author's understanding of the application design. This paper discusses two quantitative measures for process cluster evaluation, taken from the literature and adopted to our speci c context. The rst set of measures, employing information about the interprocess communication during program execution, does not appear to be useful. The evaluation of clusters with these measures di ers from a human evaluation. The second measure, evaluating the degree of similarity among processes using information derived by a static source analysis, shows greater conformance with a human evaluation. This measure is improved by adding information about actual interprocess communication during runtime and an example is given that demonstrates the use of this measure.
Rescue operations using coordinated multicast MANET

Disaster relief and rescue operations are common situations where the need for communication is crucial. We propose the use of multicast MANETs (Mobile Ad Hoc Networks) and investigate via simulation whether they can efficiently support the communications during such operations. MANETs are suitable for such operations, since they do not need any pre-existing infrastructure. We simulate two realistic scenarios. The first one is a rescue operation to find any survivors at a mountain. We split the rescuers into four teams. The rescuers in each team are coordinated and scan an area of 1000m*4000m. The purpose of this experiment is to find out the number of the rescuers per team that achieves reliable communication. We also investigate the effect of the antenna range transmissions. The second scenario is a gas pipe explosion in an inhabited area. The damage is in an area of 1500m*1500m. A rescue group of 40 rescuers and 10 doctors scans the area for any survivors. We use two multicast groups: the rescuers and the doctors. We also use one unicast connection between the rescuer‘s leader and the doctor‘s leader. We examine the effect of the traffic on the communication reliability.
Extraplanar diffuse ionized gas in a small sample of nearby edge-on galaxies

We present narrowband H imaging data of a small survey of nearby edge-on spiral galaxies, aiming at the detection of 'extraplanar' diffuse ionized gas (DIG). A few of our stud- ied edge-on spirals show signs of disk-halo interaction (DHI), where extended line emission far above the galactic plane of these galaxies is detected. In some cases an extraplanar dif- fuse ionized gas (eDIG) layer is discovered, e.g., NGC4634, NGC 3044, while other galaxies show only filamentary fea- tures reaching into the halo (e.g., IC 2531) and some galaxies show no sign of eDIG at all. The extraplanar distances of the DIG layer in our narrowband H images reach values of z 2 kpc above the galactic plane. The derived star formation rates (SFRs ) from the H flux of the studied galaxies range from 0:05 0:7M yr 1 , neglecting a correction for internal ab- sorption. The variation of the SFR values among our sample galaxies reflects the diversity of star formation within this sam- ple. A diagnostic diagram is introduced, which allows to pre- dict the existence of gas halos in 'quiescent' galaxies based on the ratio S60=S100 versus LFIR=D 2 25 in this diagram. We com- pare the positions of the non-starburst galaxies with starburst galaxies, since these galaxies populate distinct positions in these diagrams.
Discrete Wavelet Transform and One-Class Support Vector Machines for anomaly detection in wireless sensor networks

Data readings from wireless sensor networks (WSNs) may be abnormal due to detection of unusual phenomena, limited battery power, sensor malfunction, or noise from the communication channel. It is thus, important to detect such data anomalies available in WSNs to determine a suitable course of action. This paper proposes an integrated data compression and anomaly detection algorithm in WSNs which can detect anomalies accurately by employing half of sensor data measurement, instead of using all the sensor data measurement. The contribution of this paper centers on data compression by using Discrete Wavelet Transform (DWT) then feeding to anomaly detection by using One-Class Support Vector Machine (OCSVM). We tested our algorithm with several synthetic and real world datasets. The results showed that the proposed algorithm outperformed previous techniques in terms of near 100% detection rate and with marginal increase in false positive rates in presence of short and noise faults.