Miracle-dz/UniFAR-FTUs huggingface dataset:
Postrelativity --- A Paradigm For Quantization With Preferred Newtonian Frame

We define a new paradigm --- postrelativity --- based on the hypothesis of a preferred hidden Newtonian frame in relativistic theories. It leads to a modification of general relativity with ether interpretation, without topological problems, black hole and big bang singularities. Semiclassical theory predicts Hawking radiation with evaporation before horizon formation. In quantum gravity there is no problem of time and topology. Configuration space and quasiclassical predictions are different from canonical quantization of general relativity. Uncertainty of the light cone or an atomic structure of the ether may solve ultraviolet problems. The similar concept for gauge fields leads to real, physical gauge potential without Faddeev-Popov ghost fields and Gribov copy problem.
On Relay Selection in Wireless Relay Networks with Cooperative Network Coding

In this paper, we study the cooperative network coding problem in relay-assisted wireless access networks. In such a network, each mobile station can choose different transmission schemes: directly transmitting to the base station, pure relaying by a relay station, or via cooperative network coding. We analyze the impact of the different schemes on the network throughput. We find the performance of pure relaying and cooperative network coding is affected by the selection of the relay station, making the relay selection problem inseparable from the cooperative network coding problem. We formulate the joint relay selection and resource allocation problem, and find this problem equivalent to the multiple 0/1 knapsack problem. To tackle this problem, we propose an efficient network-coding-based resource allocation algorithm, and define a coding gain metric. The simulation results show our algorithm significantly outperform other schemes. More importantly, the performance improvement by cooperative network coding is significantly affected by the number of relay stations and the distribution of mobile stations.
Asymptotic behaviour of Turing Machines

In this paper we show that the probability that a program p of n bits halts at discrete time t (with t > 2n+1 − 2; we define the discrete time t as the integer number of steps done by an Universal Turing Machine during the execution of p, until the halt) tends to zero as the inverse law ∝ 1/t. This means that the greater is the time an n-bit program is running on an Universal Turing Machine (provided that t > 2n+1 − 2), the lesser is the probability that it will ever halt. Some mathematical consequences are also discussed. 1 Introductory remarks As it has been proved by Alan M. Turing in 1936 [1], if we have a program p running on an Universal Turing Machine (UTM), then we have no finite, deterministic algorithm which allows us to know whether and when it will halt (this is the well known halting problem). This is to say that the halting behaviour of a program, with the trivial exception of the simplest ones, is generally uncomputable and unpredictable. In this paper we show that, for what concerns the probability of its halt, every program running on an UTM is characterized by a peculiar asymptotic behaviour in time. Such trend provides us with some information which allows to theoretically solve the halting problem with an arbitrary degree of confidence.
To upgrade or not to upgrade? the release of new versions to survive in the hypercompetitive app market

Very low entry barriers and an exceptionally high degree of competition characterize the market for mobile applications. In such an environment one of the critical issues is how to at- tract the attention of users. Practitioners and developers are well aware that managing app updates (i.e., releasing new versions of an existing app) is critical to increase app visibil- ity and to keep users engaged, disguising a hidden strategy to stimulate downloads. We use unbalanced panel data with characteristics for the top 1,000 apps on iTunes and Google Play stores, for five European countries, to empirically in- vestigate publishers’ strategies concerning the release of up- dates. We find that only in the case of iTunes updates boost downloads and are more likely to be released when the app is experiencing poor performance. We interpret this finding as evidence that the lack of quality control by Google Play leads to an excess of updating of Android apps.
Epidemic syphilis among homosexually active men in Sydney

Objectives: To describe trends in the notification of infectious syphilis in New South Wales, the characteristics of homosexually active men recently notified with early syphilis, and the seroprevalence and incidence of syphilis, as well as associated risk factors, in a Sydney cohort of HIV‐negative homosexually active men.
Green ICT Maturity Model for Czech SMEs

This paper presents a design of a Green ICT maturity model that is aimed at maturity assessment of a Green ICT capability in SMEs that figure as users of ICT services. Based on a literature review and internet search, six maturity models in the Green ICT area were detected and further analysed. Since these maturity models do not suit maturity assessment in non-ICT SMEs, a new Green ICT maturity model for SMEs was developed. This paper introduces the process of model development, description of the resulted model and evaluation of the model.
Soil Parameter Identification and Driving Force Prediction for Wheel-Terrain Interaction

This paper considers wheeled vehicles traversing unknown terrain, and proposes an approach for identifying the unknown soil parameters required for vehicle driving force prediction (drawbar pull prediction). The predicted drawbar pull can potentially be employed for traversability prediction, traction control, and trajectory following which, in turn, improve overall performance of off-road wheeled vehicles. The proposed algorithm uses an approximated form of the wheel-terrain interaction model and the Generalized Newton Raphson method to identify terrain parameters in real-time. With few measurements of wheel slip, i, vehicle sinkage, z, and drawbar pull, DP, samples, the algorithm is capable of identifying all the soil parameters required to predict vehicle driving forces over an entire range of wheel slip. The algorithm is validated with experimental data from a wheel-terrain interaction test rig. The identified soil parameters are used to predict the drawbar pull with good accuracy. The technique presented in this paper can be applied to any vehicle with rigid wheels or deformable wheels with relatively high inflation pressure, to predict driving forces in unknown environments.
Accounting: GAAP Principles

This is a comprehensive book for a generic second year accounting course. It is approachable without sacrificing content. The book uses a concepts-based approach and incorporates worked examples, definitions in the margins, chapter summaries which are linked to outcomes and website references. The various parts of the book cover financial reporting, balance sheets, income statements, owners' equity, group accounts and cash flow statements. the book is in keeping with the changes to GAAP that are currently being implemented in order to bring SA in line with international trends. A question bank and solution bank will be available in October, and a set of PowerPoint slides and an instructor manual will be developed in 2006.
Finding Heaviest k-Subgraphs and Events in Social Media

In recent years, social media have become a useful tool to stay in contact with friends, to share thoughts but also to be informed about events. Users can follow news channels, but they can be the ones reporting updates, which distinguishes social media from traditional media. In this paper, we use a graph mining approach for finding events in a graph constructed starting from posts of users. We develop an exact algorithm for solving the heaviest k-subgraph problem which is an NP-hard problem. Our experimental analysis on large real-world graphs shows that our algorithm is able to compute the exact solutions for k up to 15 or more depending on the structure of the graph. We also develop an approximation version of our algorithm scaling to larger k. In comparison, for this setting, the classical heuristic based on weighted core decomposition only leads to sub-optimal solutions. Finally, we show that our algorithm can be used to find relevant events in Twitter. Indeed, as an event is usually described by a small number of words, our algorithm is a useful tool to detect them.
Intramuscular fiber conduction velocity, isometric force and explosive performance

Abstract Conduction of electrical signals along the surface of muscle fibers is acknowledged as an essential neuromuscular component which is linked with muscle force production. However, it remains unclear whether muscle fiber conduction velocity (MFCV) is also linked with explosive performance. The aim of the present study was to investigate the relationship between vastus lateralis MFCV and countermovement jumping performance, the rate of force development and maximum isometric force. Fifteen moderately-trained young females performed countermovement jumps as well as an isometric leg press test in order to determine the rate of force development and maximum isometric force. Vastus lateralis MFCV was measured with intramuscular microelectrodes at rest on a different occasion. Maximum MFCV was significantly correlated with maximum isometric force (r = 0.66, p < 0.01), nevertheless even closer with the leg press rate of force development at 100 ms, 150 ms, 200 ms, and 250 ms (r = 0.85, r = 0.89, r = 0.91, r = 0.92, respectively, p < 0.01). Similarly, mean MFCV and type II MFCV were better correlated with the rate of force development than with maximum isometric leg press force. Lower, but significant correlations were found between mean MFCV and countermovement jump power (r = 0.65, p < 0.01). These data suggest that muscle fiber conduction velocity is better linked with the rate of force development than with isometric force, perhaps because conduction velocity is higher in the larger and fastest muscle fibers which are recognized to contribute to explosive actions.
Conscious Capitalism: Liberating the Heroic Spirit of Business

As seen on Oprah's Super Soul Sunday A New York Times and Wall Street Journal Bestseller In this book, Whole Foods Market cofounder John Mackey and professor and Conscious Capitalism, Inc. cofounder Raj Sisodia argue for the inherent good of both business and capitalism. Featuring some of today's best-known companies, they illustrate how these two forces can--and do--work most powerfully to create value for all stakeholders: including customers, employees, suppliers, investors, society, and the environment. These "Conscious Capitalism" companies include Whole Foods Market, Southwest Airlines, Costco, Google, Patagonia, The Container Store, UPS, and dozens of others. We know them; we buy their products or use their services. Now it's time to better understand how these organizations use four specific tenets--higher purpose, stakeholder integration, conscious leadership, and conscious culture and management--to build strong businesses and help advance capitalism further toward realizing its highest potential. As leaders of the Conscious Capitalism movement, Mackey and Sisodia argue that aspiring leaders and business builders need to continue on this path of transformation--for the good of both business and society as a whole. At once a bold defense and reimagining of capitalism and a blueprint for a new system for doing business grounded in a more evolved ethical consciousness, this book provides a new lens for individuals and companies looking to build a more cooperative, humane, and positive future.
Stochastic extrema as stationary phases of characteristic functions

The paper is dealing with semi-classical asymptotics of a characteristic function for a stochastic process. The main technical tool is provided by the stationary phase method. The extremal range for a stochastic process is defined by limit values of the complex logarithm of the characteristic function. The paper also outlines a numerical method for calculating stochastic extrema.
Systemic Gender Barriers in the Building and Construction Industry: Co-Preneurs as Managers

Co-preneurs’ (Lewis & Massey, 2011) are defined as women who are in business partnerships (either formal or informal) with a spouse or life partner. These women working as administrators and managers in smaller firms in the building industry frequently experience isolation, poor access to career development and low levels of self-esteem. This is detrimental to their wellbeing and productivity and the productivity of the industry overall. This phenomenon is likely to be present in other industries and industrialised societies with similar family structures. This research project investigated why the ‘glass ceiling’ preventing women from reaching senior levels in employment has been resistant to attempts to remove it in this context.
Forecasting the Demand for Privatized Transport: What Economic Regulators Should Know and Why

Forecasting has long been a challenge, and will remain so for the foreseeable future. But the analytical instruments and data processing capabilities available through the latest technology, and software, should allow much better forecasting than transport ministries, or regulatory agencies typically observe. Privatization brings new needs for demand forecasting. More attention is paid to risk under privatization, than when investments are publicly financed. And regulators must be able to judge traffic studies done by operators, and to learn what strategic behavior influenced these studies. Many governments, and regulators avoid good demand, modeling out of lack of conviction that theory, and models can do better than the"old hands"of the sector. This is dangerous when privatization changes the nature of business. For projects amounting to investments of $ 100-200 million, a cost of $ 100,000-200,000 is not a reason to reject a reasonable modeling effort. And some private forecasting firms are willing to sell guarantees, or insurance with their forecasts, to cover significant gaps between forecasts, and reality.
Local stabilizer

A local stabilizer protocol that takes any on-line or of-line distributed algorithm and converts it into a synchronous self-stabilizing algorithm with local monitoring and repairing properties is presented. Whenever the self-stabilizing version enters an inconsistent state, the inconsistency is detected, in O(1) time, and the system state is repaired in a local manner. The expected computation time that is lost during the repair process is proportional to the largest diameter of a faulty region.
Finite-time blow-up of solutions of some long-wave unstable thin film equations

We consider the family of long–wave unstable lubrication equations ht = −(hhxxx)x − (hhx)x with m ≥ 3. Given a fixed m ≥ 3, we prove the existence of a weak solution that becomes singular in finite time. Specifically, given compactly supported nonnegative initial data with negative energy, there is a time T ∗ < ∞, determined by m and the H norm of the initial data, and a compactly supported nonnegative weak solution such that lim supt→T∗ ||h(·, t)||L∞ = lim supt→T∗ ||h(·, t)||H1 = ∞. We discuss the relevance of these singular solutions to an earlier conjecture [Comm Pure Appl Math 51:625–661, 1998] on when finite–time singularities are possible for long–wave unstable lubrication equations.
Effects of Recombinant Human IGF-I/IGF-Binding Protein-3 Complex on Glucose and Glycerol Metabolism in Type 1 Diabetes

Recombinant human IGF-I (rhIGF-I) complexed with its natural binding protein IGF-binding protein (IGFBP)-3 (rhIGF-I/IGFBP-3) is a novel formulation that has been shown to improve insulin sensitivity in type 1 diabetes, yet the mechanisms are not clear. We used stable isotopes to investigate the effects of rhIGF-I/IGFBP-3 on glucose and glycerol metabolism in type 1 diabetes. Fifteen subjects (age 13–24 years; 10 males) were studied on three occasions in random order. Each study period lasted for two days, and an injection of either placebo or rhIGF-I/IGFBP-3 (0.1–0.8 mg · kg−1 · day −1) was given subcutaneously at 6:00 p.m. on days 1 and 2. Following the second injection, the subjects were kept euglycemic overnight by a variable rate insulin infusion, followed by a 4-h, two-step (insulin 0.6 and 1.5 mU · kg−1 · min −1) hyperinsulinemic-euglycemic clamp. During the overnight basal steady state, rhIGF-I/IGFBP-3 dose-dependently reduced endogenous glucose production rate (Ra) (P = 0.004), while peripheral glucose uptake (Rd) was not different from placebo. The increase in glucose Rd during hyperinsulinemic clamp was greater following rhIGF-I/IGFBP-3 than placebo, both during the first (P = 0.008) and second step (P = 0.008) of the clamp. No significant differences were found in glycerol Ra, a measure of lipolysis, between rhIGF-I/IGFBP-3 and placebo. In conclusion, rhIGF-I/IGFBP-3 enhances glucose metabolism by controlling both endogenous glucose output and peripheral glucose uptake.
IL-6 Inhibits the Targeted Modulation of PDCD4 by miR-21 in Prostate Cancer

Prostate cancer is the most common cancer among men in the Unites States. The cytokine IL-6 activates several prostate cancer pathways, but its upstream trans-signaling pathway remains poorly understood. In this study, we evaluated the role of IL-6 in PDCD4 gene expression and how the microRNA miR-21 regulates this process in prostate cancer cell lines PC-3 and LNCaP. The expression pattern of PDCD4 from samples from human prostate cancer, precancerous lesions, and benign prostatic hyperplasia was investigated by immunohistochemistry. PDCD4 transcription and translation were detected by quantitative real-time PCR (qRT-PCR) and Western blot analysis, respectively. The targeted modulation of PDCD4 by miR-21 was analyzed in PC-3 and LNCaP cells, and the effect of IL-6 on the expression of PDCD4 was studied in vitro. PDCD4 expression in samples from the 3 tissue types progressively increased, and the expression levels of PDCD4 and prostate-specific antigen were negatively correlated. The levels of PDCD4 mRNA and protein in PC-3 and LNCaP cells transfected with anti–miR-21 constructs were lower than those in control cells. The expression of PDCD4 was inhibited by IL-6, but this effect was weakened in cell lines with low expression of miR-21. Our study demonstrates that the regulation of PDCD4 by miR-21 is targeted and IL-6 inhibits expression of the PDCD4 gene in PC-3 and LNCaP cells through the targeted function of miR-21 on PDCD4. These findings support the feasibility of future efforts for diagnosis and gene therapy for prostate cancer that are based on IL-6, miR-21, and PDCD4.
TCSST: transfer classification of short & sparse text using external data

Short & sparse text is becoming more prevalent on the web, such as search snippets, micro-blogs and product reviews. Accurately classifying short & sparse text has emerged as an important while challenging task. Existing work has considered utilizing external data (e.g. Wikipedia) to alleviate data sparseness, by appending topics detected from external data as new features. However, training a classifier on features concatenated from different spaces is not easy considering the features have different physical meanings and different significance to the classification task. Moreover, it exacerbates the "curse of dimensionality" problem. In this study, we propose a transfer classification method, TCSST, to exploit the external data to tackle the data sparsity issue. The transfer classifier will be learned in the original feature space. Considering that the labels of the external data may not be readily available or sufficiently enough, TCSST further exploits the unlabeled external data to aid the transfer classification. We develop novel strategies to allow TCSST to iteratively select high quality unlabeled external data to help with the classification. We evaluate the performance of TCSST on both benchmark as well as real-world data sets. Our experimental results demonstrate that the proposed method is effective in classifying very short & sparse text, consistently outperforming existing and baseline methods.
Distributed Bayesian networks reconstruction on the whole genome scale

Background Bayesian networks are directed acyclic graphical models widely used to represent the probabilistic relationships between random variables. They have been applied in various biological contexts, including gene regulatory networks and protein-protein interactions inference. Generally, learning Bayesian networks from experimental data is NP-hard, leading to widespread use of heuristic search methods giving suboptimal results. However, in cases when the acyclicity of the graph can be externally ensured, it is possible to find the optimal network in polynomial time. While our previously developed tool BNFinder implements polynomial time algorithm, reconstructing networks with the large amount of experimental data still leads to computations on single CPU growing exceedingly. Results In the present paper we propose parallelized algorithm designed for multi-core and distributed systems and its implementation in the improved version of BNFinder - tool for learning optimal Bayesian networks. The new algorithm has been tested on different simulated and experimental datasets showing that it has much better efficiency of parallelization than the previous version. BNFinder gives comparable results in terms of accuracy with respect to current state-of-the-art inference methods, giving significant advantage in cases when external information such as regulators list or prior edge probability can be introduced. Conclusions We show that the new method can be used to reconstruct networks in the size range of thousands of genes making it practically applicable to whole genome datasets of prokaryotic systems and large components of eukaryotic genomes. Our benchmarking results on realistic datasets indicate that the tool should be useful to wide audience of researchers interested in discovering dependencies in their large-scale transcriptomic datasets.
Exploring visualisation in web information retrieval

Most conventional web search engines return a small fraction of results per display in textual format. Such renderings require the user to scroll over multiple pages to find relevant results. To enhance the user's ability to identify relevant documents among large sets of results, visualisation techniques are suggested. In addition, integrating the user in the process of query reformulation – by visualising the process itself – can lead to more relevant search results. This paper explores research concerned with visualising the processes of query reformulation and results rendering in web search, and provides research recommendations for further investigations of how best to integrate visualisation into web information retrieval.