Miracle-dz/UniFAR-FTUs huggingface dataset:
Consensus clustering for urban land use analysis using cell phone network data

Pervasive large-scale infrastructures have the ability to capture individual digital footprints and, as a result, provide a new vision on human dynamics. In this context, cell phones and cell phone networks, due to its ubiquity, can be considered one of the main sensors of human behaviour. The information collected by these networks can be used to understand the dynamics of urban environments with a detail not available up to now. One of the areas that can benefit from this information is urban planning. In this paper, we present a technique for the automatic identification of land uses from the information gathered by a cell phone network. Given the inherent diversity of human activities, we use consensus clustering to identify land uses, characterising only those geographical areas with well-defined behaviours. We present and validate our results using cell phone records and official land use data collected for Madrid.
Loran ASF Variations as a Function of Altitude

Abstract : In 2001, the Volpe National Transportation Systems Center completed an evaluation of GPS vulnerabilities and the potential impacts to transportation systems in the United States. One of the recommendations of this study was for the operation of backup system(s) to GPS; Loran-C was identified as one possible backup system. The Federal Aviation Administration (FAA) has been leading a team consisting of members from industry, government, and academia to evaluate the future of Loran-C in the United States. In a recently completed Navigation Transition Study, the FAA concluded that Loran-C, as an independent radio navigation system, is theoretically the best backup for the Global Positioning System (GPS). However, in order for Loran-C to be considered a viable back-up system to GPS, it must be able to meet the requirements for nonprecision approaches (NPA's) for the aviation community, and the Harbor Entrance and Approach (HEA) requirements for the maritime community. A significant factor limiting the accuracy of a Loran system is the spatial and temporal variation in the times of arrival (TOAs) observed by the receiver. A significant portion of these variations is due to the signals propagating over paths of varying conductivity; these TOA corrections which compensate for propagating over non-seawater paths are called additional secondary factors (ASFs). Hence, a key component in evaluating the utility of Loran as a GPS backup is a better understanding of ASFs and a key goal is deciding how to mitigate the effects of ASFs to achieve more accurate Loran-C positions while ensuring that the possibility of providing hazardous and misleading information (HMI) will be no greater than 1x10(-7).
Patients with Renal Cancer Multiple Genes in the Tumor , Urine , and Serum DNA of Quantitative Detection of Promoter Hypermethylation of Updated

Aberrant promoter hypermethylation of several known or putative tumor suppressor genes occurs frequently during the pathogenesis of human cancers and is a promising marker for cancer detection. We investigated the feasibility of detecting aberrant DNA methylation in the urine and serum samples of renal cancer patients. We examined the tumor and the matched urine and serum DNA for aberrant methylation of nine gene promoters (CDH1, APC, MGMT, RASSF1A, GSTP1, p16, RAR2, and ARF) from 17 patients with primary kidney cancer by quantitative fluorogenic real-time PCR. An additional 9 urine samples (total, 26) and 1 serum sample (total, 18) also were tested from renal cancer patients. Urine from 91 patients without genitourinary cancer and serum from 30 age-matched noncancer individuals were used as controls. Promoter hypermethylation of at least two of the genes studied was detected in 16 (94%) of 17 primary tumors. Aberrant methylation in urine and serum DNA generally was accompanied by methylation in the matched tumor samples. Urine samples from 91 control subjects without evidence of genitourinary cancer revealed no methylation of the MGMT, GSTP1, p16, and ARF genes, whereas methylation of RAR2, RASSF1A, CDH1, APC, and TIMP3 was detected at low levels in a few control subjects. Overall, 23 (88%) of 26 urine samples and 12 (67%) of 18 serum samples from cancer patients were methylation positive for at least one of the genes tested. By combination of urine or serum analysis of renal cancer patients, hypermethylation was detected in 16 of 17 patients (94% sensitivity) with high specificity. Our findings suggest that promoter hypermethylation in urine or serum can be detected in the majority of renal cancer patients. This noninvasive high-throughput approach needs to be evaluated in large studies to assess its value in the early detection and surveillance of renal cancer.
Sparse basis selection, ICA, and majorization: towards a unified perspective

Sparse solutions to the linear inverse problem Ax=Y and the determination of an environmentally adapted overcomplete dictionary (the columns of A) depend upon the choice of a "regularizing function" d(x) in several previously proposed procedures. We discuss the interpretation of d(x) within a Bayesian framework, and the desirable properties that "good" (i.e., sparsity ensuring) regularizing functions, d(x) might have. These properties are: Schur-concavity (d(x) is consistent with majorization); concavity (d(x) has sparse minima); parameterizability (d(x) is drawn from a large, parameterizable class); and factorizability of the gradient of d(x) in a certain manner. The last property (which naturally leads one to consider separable regularizing functions) allows d(x) to be efficiently minimized subject to Ax=Y using an affine scaling transformation (AST)-like algorithm "adapted" to the choice of d(x). A Bayesian framework allows the algorithm to be interpreted as an independent component analysis (ICA) procedure.
What Is the Incidence of Intracranial Bleeding in Patients with Mild Traumatic Brain Injury? A Retrospective Study in 3088 Canadian CT Head Rule Patients

Objective. Only limited data exists in terms of the incidence of intracranial bleeding (ICB) in patients with mild traumatic brain injury (MTBI). Methods. We retrospectively identified 3088 patients (mean age 41 range (7–99) years) presenting with isolated MTBI and GCS 14-15 at our Emergency Department who had undergone cranial CT (CCT) between 2002 and 2011. Indication for CCT was according to the “Canadian CT head rules. ” Patients with ICB were either submitted for neurosurgical treatment or kept under surveillance for at least 24 hours. Pearson's correlation coefficient was used to correlate the incidence of ICB with age, gender, or intake of coumarins, platelet aggregation inhibitors, or heparins. Results. 149 patients (4.8%) had ICB on CCT. No patient with ICB died or deteriorated neurologically. The incidence of ICB increased with age and intake of anticoagulants without clinically relevant correlation (R = 0.11; P < 0.001; R = −0.06; P < 0.001). Conclusion. Our data show an incidence of 4.8% for ICB after MTBI. However, neurological deterioration after MTBI seems to be rare, and the need for neurosurgical intervention is only required in selected cases. The general need for CCT in patients after MTBI is therefore questionable, and clinical surveillance may be sufficient when CCT is not available.
On the Mechanism of Anisotropic Etching of Silicon

A new model is proposed that explains the anisotropy of the etch rate of single crystalline silicon in certain etchants. It is inspired from theories of crystal growth. We assume that the (111)-face is flat on an atomic scale. Then the etch rate should be governed by a nucleation barrier of one atomic layer deep cavities. The origin of the nucleation barrier is that the formation of a too small cavity increases the free energy of the system due to the step-free energy. The step-free energy and the undersaturation governs the activation energy of the etch rate. Having the largest step-free energy, the (111)-face etches the slowest. The model explains qualitatively why the etching is isotropic in certain etchants and anisotropic in others.
Co-Learning in Differential Games

Game playing has been a popular problem area for research in artificial intell igence and machine learning for many years. In almost every study of game playing and machine learning, the focus has been on games with a finite set of states and a finite set of actions. Further, most of this research has focused on a single player or team learning how to play against another player or team that is applying a fixed strategy for playing the game. In this paper, we explore multi-agent learning in the context of game playing and develop algorithms for “co-learning” in which all players attempt to learn their optimal strategies simultaneously. Specifically, we address two approaches to colearning, demonstrating strong performance by a memory-based reinforcement learner and comparable but faster performance with a treebased reinforcement learner.
An Efficient Algorithm for Optimally Solving a Shortest Vector Problem in Compute-and-Forward Design

We consider the problem of finding the optimal coefficient vector that maximizes the computation rate at a relay in the compute-and-forward scheme. Based on the idea of sphere decoding, we propose a highly efficient algorithm that finds the optimal coefficient vector. First, we derive a novel algorithm to transform the original quadratic form optimization problem into a shortest vector problem (SVP) using the Cholesky factorization. Instead of computing the Cholesky factor explicitly, the proposed algorithm realizes the Cholesky factorization with only O(n) flops by taking advantage of the structure of the Gram matrix in the quadratic form. Then, we propose some conditions that can be checked with O(n) flops, under which a unit vector is the optimal coefficient vector. Finally, by considering some useful properties of the optimal coefficient vector, we modify the Schnorr-Euchner search algorithm to solve the SVP. We show that the estimated average complexity of our new algorithm is O(n1.5p0.5) flops for independent identically distributed (i.i.d.) Gaussian channel entries with SNR P based on the Gaussian heuristic. Simulations show that our algorithm is not only much more efficient than the existing ones that give the optimal solution, but also faster than some best known suboptimal methods. Besides, we show that our algorithm can be readily adapted to output a list of L best candidate vectors for use in the compute-and-forward design. The estimated average complexity of the resultant list-output algorithm is O(n2.5p0.5 + n1.5p0.5 log(L) + nL) flops for i.i. d. Gaussian channel entries.
COOL: a language for parallel programming

We present COOL, an object-oriented parallel language derived from C++ by adding constructs to specify concurrent execution. We describe the language design, and the facilities for creating parallelism, performing synchronization, and communicating. The parallel construct is parallel functions that execute asynchronously. Synchronization support includes mutex functions and future types. A shared-memory model is assumed for parallel execution, and all communication is through shared-memory. The parallel programming model of COOL has proved useful in several small programs that we have attempted. We present some examples and discuss the primary implementation issues.
Feigenbaum Attractor and the Generation Structure of Particle Physics

The standard model (SM) for high-energy physics describes fundamental interactions between subatomic particles down to a distance scale on the order of 10-18 m. Despite its widespread acceptance, SM operates with a large number of arbitrary parameters whose physical origin is presently unknown. Our work suggests that the generation structure of at least some SM parameters stems from the chaotic regime of renormalization group flow. Invoking the universal route to chaos in systems of nonlinear differential equations, we argue that the hierarchical pattern of parameters amounts to a series of scaling ratios depending on the Feigenbaum constant. Leading order predictions are shown to agree reasonably well with experimental data.
Innovation‐Related Diversification and Firm Value

We examine a novel determinant of corporate diversification and its valuation effect: corporate innovations. We find consistent evidence that corporate innovations increase the extent of diversification. To establish causality, we estimate the firm fixed effects, 2SLS and GMM models. The 2SLS model uses the US stateâ€ level R&D tax credits as an instrumental variable for corporate innovations. We also find that a firm is more likely to diversify into an industry where it has more applicable innovations. Further, such innovationâ€ related diversification is associated with significantly higher firm value. Our results are robust to various measures of corporate innovations.
Effects of Aging and Anatomic Location on Gene Expression in Human Retina

Objective: To determine the effects of age and topographic location on gene expression in human neural retina. Methods: Macular and peripheral neural retina RNA was isolated from human donor eyes for DNA microarray and quantitative RT-PCR analyses. Results: Total RNA integrity from human donors was preserved. Hierarchical clustering analysis demonstrates that the gene expression profiles of young, old, macula, and peripheral retina cluster into four distinct groups. Genes which are highly expressed in macular, peripheral, young, or old retina were identified, including inhibitors of Wnt Signaling Pathway (DKK1, FZD10, and SFRP2) which are preferably expressed in the periphery. Conclusion: The transcriptome of the human retina is affected by age and topographic location. Wnt pathway inhibitors in the periphery may maintain peripheral retinal cells in an undifferentiated state. Understanding the effects of age and topographic location on gene expression may lead to the development of new therapeutic interventions for age-related eye diseases.
The efficacy of motivational interviewing with offenders : an outcome and process evaluation : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in Psychology at Massey University

This research comprised an evaluation of a motivational interviewing programme, the Short Motivational Programme (SMP), delivered by the Department of Corrections and adapted for use with incarcerated offenders. The evaluation consisted of two components: outcome and process. Outcome Results indicated that offenders' motivation to change significantly increased from pre to post the SMP (eta squared = .19, p < .05), and a sub-group of offenders demonstrated that motivation to change was maintained at follow-up. This result, given the sample's risk of recidivism demographic, provided evidence for the SMP' s efficacy with high risk offenders. Process On a measure of motivational interviewing skills, psychologists demonstrated full competence only in the use of direction, although global clinician ratings, percent open questions, evocation, collaboration, autonomy and empathy approached competence. Other than, "rolling with resistance", offenders reported strongly experiencing motivational interviewing principles during the SMP. Lastly, offenders who experienced the motivational interviewing principles to the greater extent did not demonstrate any greater increases in motivation to change from pre to post the SMP and at follow-up. These findings are discussed and recommendations are made for further research.
THE SIGNALLING EFFECTS OF BANK LOAN-LOSS RESERVE ADDITIONS

This study examines the market’s reaction to announcements of additions to the Loan Loss Reserve (LLR) account resulting from diverse problems in a bank’s loan portfolio which are unrelated to an international debt crisis. For the overall sample, with no division by type of loan, the reaction to an increase in LLRs is negative and statistically significant before the announcement; however, it turns positive and remains statistically significant for several days afterwards. Viewing each category individually, the results vary. The largest statistically significant results are for Lesser Developed Country Loans and Foreign and Domestic Loans (positive reaction) and combination Real Estate and Energy Loans (negative reaction). A division of the data into two sub-samples, before and after 1987, indicates that investors appear to be more discerning of individual BHCs’ circumstances surrounding announcements after 1987.
Analysis of the niche genetic algorithm in learning classifier systems

Learning Classifier Systems (LCS) evolve IF-THEN rules for classification and control tasks. The earliest Michigan-style LCS used a panmictic Genetic Algorithm (GA) (in which all rules compete for selection) but newer ones tend to use a niche GA (in which only a certain subset of rules compete for selection at any one time). The niche GA was thought to be advantageous in all learning tasks, but recent research suggests it has difficulties when the rules composing the solution overlap. Furthermore, the niche GA's effects are implicit, making it difficult study, and fixed, which prevents tuning its performance. Given these issues, we set out on a long-term project to reevaluate the niche GA. This work is our starting point and in it we address the implicit and unquantified effects of the niche GA by building a mathematical model of the probability of rule selection. This model reveals a number of insights into the components of rule fitness, particularly the bonus for rule generality and penalty for overlaps, both previously unquantified. These theoretical results are our primary contribution. However, to demonstrate one way to apply this theory, we then introduce a new variant of the UCS algorithm, which uses a hybrid panmictic/niche GA. Preliminary results suggest, unexpectedly, that the niche GA may have even more drawbacks than previously thought.
SFOIL: Stochastic Approach to Inductive Logic Programming

Current systems in the eld of Inductive Logic Programming (ILP) use, primarily for the sake of eeciency, heuristically guided search techniques. Such greedy algorithms suuer from local optimization problem. Present paper describes a system named SFOIL, that tries to alleviate this problem by using a stochastic search method, based on a generalization of simulated annealing, called Markovian neural network. Various tests were performed on benchmark, and real-world domains. The results show both, advantages and weaknesses of stochastic approach.
Indexing rules in rule sets for fast classification

Using sets of rules for classification of examples usually involves checking a number of conditions to see if they hold or not. If the rule set is large the time to make the classification can be lengthy. In this paper we propose an indexing algorithm to decrease the classification time when dealing with large rule sets. Unordered rule sets have a high time complexity when conducting classification; we hence conduct experiments comparing our novel indexing algorithm with the standard way of classifying ensembles of unordered rule sets. The result of the experiment shows decreased classification times for the novel method that are ranging from 0.6 to 0.8 of that of the standard approach averaged over all experimental datasets. This time gain is obtained while retaining an accuracy ranging from 0.84 to 0.99 with regard to the standard classification method. The index bit size used with the indexing algorithm influence both the classification accuracy and time needed for conducting the classification task.
Plasmonic-based GaAs balanced metal-semiconductor-metal photodetector with high common mode rejection ratio

We propose and demonstrate a plasmonic-based GaAs balanced metal-semiconductor-metal photodetector (BMSM-PD) structure. A dual-beam FIB/SEM is employed for the fabrication of the metal nano-gratings and slits of the B-MSMPD. A common mode rejection ratio (CMRR) value less than 25 dB at 830nm wavelength, dependent on the applied bias, is measured. This adequate CMRR value indicates that the BMSM-PD structure substantially suppresses laser intensity noise, making it suitable for ultra-high-speed optical telecommunication systems. In addition, this work paves the way for the monolithic integration of B-MSM-PDs into large scale semiconductor circuits.
Scalable Influence Maximization in Social Networks Using the Community Discovery Algorithm

Influence maximization is the problem of finding a small set of most influential vertices in a social network so that their aggregated influence in the network is maximized. Most social networks influence maximization problem are based on the following two basic propagation model: Independent Cascade Model and Linear Threshold Model. They all believe that the impact of all the vertices in a community is the same. It is inconsistent with the actual observed. in social networks, the influence of the different members in a community is not the same. Every community have some core members, their influence is far greater than the others. in view of this, a community discovery algorithm is proposed to find the core members of the community. Selecting the initial members from these core members will have the greatest influence.
Towards shorter task completion time in datacenter networks

Datacenters are now used as the underlying infrastructure of many modern commercial operations, powering both large Internet services and a growing number of data-intensive scientific applications. The tasks in these applications always consist of rich and complex flows which require different resources at different time slots. The existing data center scheduling frameworks are however base on either task or flow level metrics. This simplifies the design and deployment, but hardly unleashes the potentials of obtaining low task completion time for delay sensitive applications. In this paper, we show that the performance (e.g., tail and average task completion time) of existing flow-aware and task-aware network scheduling is far from being optimal. To address such a problem, we carefully examine the possibility to consider both task and flow level metrics together and present the design of TAFA (Task-Aware and Flow-Aware) in data center networks. This approach seamlessly combines the existing flow and task metrics together while successfully avoids their problems as flow-isolation and flow indiscrimination. The evaluation result shows that TAFA can obtain a near-optimal performance and reduce over 35% task completion time for the existing data center systems.
Personally driven professional development: reflective self-study as a way for teachers to take control of their own professional development

This article is about personally driven professional development through the use of reflective self-study. The argument that teachers need to take responsibility for their own learning while also taking decisions on how and in what areas to develop is strongly made throughout the article. Data for this article were gathered over a 10-year period where the author engaged in several studies using self-study as methodology. Between studies, the author kept using reflective self-study as a professional development tool, and it is these data between various studies that were analysed using thematic and reflective analysis. Such analysis resulted in various overarching themes that show how reflective self-study can be of benefit if used by teachers researching their own classrooms. Benefits include teachers taking full ownership of their learning, relevance of learning, learning that is ongoing and not restricted to specific times and places, taking problematic situations as learning opportunities and how other sources take on new relevance through reflective self-study. Some implications for teacher-education programmes and current school structures are also discussed.