Miracle-dz/UniFAR-FTUs huggingface dataset:
Dose-Escalated Stereotactic Body Radiation Therapy for Prostate Cancer: Quality-of-Life Comparison of Two Prospective Trials

Introduction The optimal prostate stereotactic body radiation therapy (SBRT) dose-fractionation scheme is controversial. This study compares long-term quality of life (QOL) from two prospective trials of prostate SBRT to investigate the effect of increasing dose (NCT01578902 and NCT01146340). Material and methods Patients with localized prostate cancer received SBRT 35 or 40 Gy delivered in five fractions, once per week. QOL was measured using the Expanded Prostate Cancer Index Composite at baseline and every 6 months. Fisher’s exact test and generalized estimating equations were used to analyze proportions of patients with clinically significant change and longitudinal changes in QOL. Results One hundred fourteen patients were included, 84 treated with 35 Gy and 30 treated with 40 Gy. Median QOL follow-up was 56 months [interquartile range (IQR) 46–60] and 38 months (IQR 32–42), respectively. The proportion of patients reporting clinically significant declines in average urinary, bowel, and sexual scores were not significantly different between dose levels, and were 20.5 vs. 24.1% (p = 0.60), 26.8 vs. 41.4% (p = 0.16), and 42.9 vs. 38.5% (p = 0.82), respectively. Similarly, longitudinal analysis did not identify significant differences in QOL between treatment groups. Conclusion Dose-escalated prostate SBRT from 35 to 40 Gy in five fractions was not associated with significant decline in long-term QOL.
An overview of real-time quantitative PCR: applications to quantify cytokine gene expression.

The analysis of cytokine profiles helps to clarify functional properties of immune cells, both for research and for clinical diagnosis. The real-time reverse transcription polymerase chain reaction (RT-PCR) is becoming widely used to quantify cytokines from cells, body fluids, tissues, or tissue biopsies. Being a very powerful and sensitive method it can be used to quantify mRNA expression levels of cytokines, which are often very low in the tissues under investigation. The method allows for the direct detection of PCR product during the exponential phase of the reaction, combining amplification and detection in one single step. In this review we discuss the principle of real-time RT-PCR, the different methodologies and chemistries available, the assets, and some of the pitfalls. With the TaqMan chemistry and the 7700 Sequence Detection System (Applied Biosystems), validation for a large panel of murine and human cytokines and other factors playing a role in the immune system is discussed in detail. In summary, the real-time RT-PCR technique is very accurate and sensitive, allows a high throughput, and can be performed on very small samples; therefore it is the method of choice for quantification of cytokine profiles in immune cells or inflamed tissues.
On Approximate Range Counting and Depth

AbstractWe improve the previous results by Aronov and Har-Peled (SODA’05) and Kaplan and Sharir (SODA’06) and present a randomized data structure of O(n) expected size which can answer 3D approximate halfspace range counting queries in $O(\log{n\over k})$ expected time, where k is the actual value of the count. This is the first optimal method for the problem in the standard decision tree model; moreover, unlike previous methods, the new method is Las Vegas instead of Monte Carlo. In addition, we describe new results for several related problems, including approximate Tukey depth queries in 3D, approximate regression depth queries in 2D, and approximate linear programming with violations in low dimensions.
DL-Lite with Attributes and Datatypes

We extend the DL-Lite languages by means of attributes and datatypes. Attributes—a notion borrowed from data models— associate concrete values from datatypes to abstract objects and in this way complement roles, which describe relationships between abstract objects. The extended languages remain tractable (with a notable exception) even though they contain both existential and (a limited form of) universal quantification. We present complexity results for two most important reasoning problems in DL-Lite: combined complexity of knowledge base satisfiability and data complexity of positive existential query answering.
Illegal Music Downloading and its Impact on Legitimate Sales: Australian Empirical Evidence

This paper explores illegal music file-sharing activity and its effect on Australian sales of singles in the physical and digital retail markets. Using fifteen weeks of Australian Recording Industry Association weekly chart rankings of physical and digital sales, combined with a proxy for download activity derived from the popular peer-to-peer (P2P) network Limewire, the evidence suggests no discernible impact of download activity on legitimate sales. Whilst significant negative correlation between chart rank and download activity is observed in the digital market, once download endogeneity is purged from the model and song heterogeneity is controlled for no significant relationship remains.
Active filters for 1 MHz power circuits under strict ripple limitations

Recent advances in MOSFET technology have moved the switching frequencies of power circuits into the mega-Hertz range. The drive to these higher switching frequencies is motivated entirely by the smaller energy storage elements that supposedly result. Unfortunately, at these very high frequencies, the allowable ripple levels on the input and output of the power circuits are often much lower than they are at lower frequencies. This then requires greater ripple attenuation which means that all of the desired energy storage reduction cannot be achieved. As switching frequencies are pushed higher, the passive filtration elements often come to dominate the size and cost of the converter. In many cases, however, some of the large, costly, passive filter elements can be replaced with small, simple, active circuits. The result is savings in cost, size, and weight, of the overall filter. It is the purpose of this thesis to outline these cases and develope these active circuits. The thesis first describes the situations in which these active filters find their greatest import. It then moves on to developing the theory and evaluation criteria surrounding these filters. After discussing in depth, the various filter topologies, and available active devices, it presents a working circuit which was constructed in the lab. The performance of this circuit agrees quite closely with both theory and simulation, verifying all of the previous analysis. Thesis Supervisor: Dr. Martin F. Schlecht Title: Assistant Professor of Electrical Engineering
Frequency Affinity: Analyzing and Maximizing Power Efficiency in Multi-core Systems

Performance optimization and energy efficiency are the major challenges in multi-core system design. Of the state-of-the-art approaches, cache affinity aware scheduling and techniques based on dynamic voltage frequency scaling (DVFS) are widely applied to improve performance and save energy consumptions respectively. In modern operating systems, schedulers exploit high cache affinity by allocating a process on a recently used processor whenever possible. When a process runs on a high-affinity processor it will find most of its states already in the cache and will thus achieve more efficiency. However, most state-of-the-art DVFS techniques do not concentrate on the cost analysis for DVFS mechanism. In this paper, we firstly propose frequency affinity which retains the voltage frequency as long as possible to avoid frequently switching, and then present a frequency affinity aware scheduling (FAS) to maximize power efficiency for multi-core systems. Experimental results demonstrate our frequency affinity aware scheduling algorithms are much more power efficient than single-ISA heterogeneous multi-core processors.
Performance Management of Virtual Machines via Passive Measurement and Machine Learning

Virtualization is commonly used to efficiently operate servers in data centers. The autonomic management of virtual machines enhances the advantages of virtualization. For the development of such management, it is important to establish a method to accurately detect performance degradation in virtual machines. This paper proposes a method that detects degradation via the passive measurement of traffic exchanged by virtual machines. Using passive traffic measurement is advantageous because it is robust against heavy loads, nonintrusive to the managed machines, and independent of hardware/software platforms. From the measured traffic metrics, performance state is determined by a machine learning technique that algorithmically determines the complex relationship between traffic metrics and performance degradation from training data. Moreover, the feasibility and effectiveness of the proposed method are confirmed experimentally.
Hodgkin's Lymphoma in Children and Adolescents: A Saint Petersburg Hodgkin's Lymphoma Group Study

Purpose. Prospective analysis of the efficacy of the original protocol SPbHL-05 was performed. Patients and Methods. Sixty patients with Hodgkin's lymphoma (HL) aged less than 18 years old were treated in accordance with SPbHL-05 from January 2000, to July 2009. In induction chemotherapy we used VBVP and ABVD schedules followed by involved-field radiotherapy. Fourteen patients (23,3%) with 0–2 adverse factors (the favourable group) received two cycles of chemotherapy (VBVP), 25 children (41,7%) with 3-4 unfavorable signs (the intermediate group) received two cycles of VBVP alternating with two cycles of ABVD, 21 patient (35%) who had 5 or more adverse prognostic factors (the unfavourable group) received three cycles of VBVP alternating with three cycles of ABVD. Results. With a median follow-up of 68 months, overall survival (OS) at 5 years is 91.3%, event-free survival (EFS) is 82.8%. OS in the favourable and intermediate risk group were 100%, EFS were 92,9% and 90,7%, respectively, OS and EFS in unfavourable risk group—77,1% and 55,6%, respectively. Conclusion. The identification of prognostic risk factors and using medicines with less prominent side effects would be of major importance in the development of new strategies of treatment for childhood HL.
The effect of reionization on the cosmic microwave background–density correlation

In this paper, we show how the rescattering of cosmic microwave background photons after cosmic reionization can give a significant linear contribution to the temperature–matter cross-correlation measurements. These anisotropies, which arise via a late-time Doppler effect, are on scales much larger than the typical scale of non-linear effects at reionization; they can contribute to degree scale cross-correlations and could affect the interpretation of similar correlations resulting from the integrated Sachs–Wolfe effect. While expected to be small at low redshifts, these correlations can be large given a probe of the density at high redshift, and so could be a useful probe of the cosmic reionization history.
A framework for personal data protection in the IoT

IoT personal devices are having an undoubtedly explosive growth as many different products become available on the market such as smart watches, contact lenses, fitness bands, microchips under skin, among others. These devices and their related applications process the collected sensor data and use them to provide services to their users; in addition most of them require data from other applications in order to enhance their service. However, data sharing increases the risk for privacy protection since the aggregation of multiple data may favor the prediction of unrevealed private information. This paper presents a general framework for managing the issue of privacy protection from unwanted disclosure of personal data. The framework integrates two approaches in privacy protection: the use of personal data managers to control and manage user data sharing and the use of techniques for inference prevention. The contribution of the framework is to exploit the advantages of the two mentioned lines of research in a user-centric approach that empowers users with higher control of their data and makes them aware about data-sharing decisions.
A new online tool for gamer network performance analysis

This paper discusses a new online tool for online gamers to share networking performance results and to produce aggregate networking performance reports generated from the data reported by all users. It is necessary to improve the quality of service and make online games faster. This will help improve latency sensitive game data collection. It will help to improve gaming performance through the global network of servers, optimizing the game connection from end to end. A Web API was developed to receive data in the form of a HTTP post from WTFast clients while they are playing their games. A data generator was also built to simulate thousands of concurrent users posting data to the web API. The web API stores the data in a Elasticsearch database. A web interface was built for WTFast clients to view their historical network data and to share it on forums or social media. The web interface shows a graph of their network performance with and without using the GPN R . The complete system is anticipated to need to handle approximately 400,000 concurrent users sending network data every 2 seconds.
Genome-Wide Association Study Reveals Host Genetic Factors for Liver Diseases

A number of disease-associated genetic markers for common liver diseases have been identified using genome-wide association studies (GWASs). The GWAS strategy is based on genome-wide single-nucleotide polymorphism typing technologies, which are now commercially available, accompanied by statistical methods to identify host genetic factors that are associated with target diseases or complex genetic traits. One of the most striking features of the GWAS strategy is the ability to identify unexpected disease-associated genetic markers across the entire human genome. Here, we describe the technological aspects of the GWAS strategy with examples from actual GWAS reports related to hepatitis research, including drug response for patients with chronic hepatitis C, susceptibility to primary biliary cirrhosis, and hepatitis-B-related hepatocellular carcinoma.
Quadratically Constrained Minimum Dispersion Beamforming via Gradient Projection

A quadratically constrained minimum dispersion (QCMD) beamformer that is robust against model uncertainties is devised for non-Gaussian signals. Different from the minimum variance based beamformers, the QCMD beamformer minimizes the lp-norm (p ≥ 1) of the output while constraining the magnitude response of any steering vector within a spherical uncertainty set to exceed unity. A gradient projection algorithmic framework is proposed to efficiently solve the resulting convex optimization problem instead of directly applying the standard optimization algorithm which has a high computational complexity. In each iteration, the gradient projection updates the solution along the gradient direction and projects it back to the constraint set. Importantly, a closed-form expression of the projection onto the constraint set is derived, which only needs a low complexity of O(M) with M being the sensor number. Therefore, the proposed algorithm is much faster and simpler to implement compared with the standard method. In addition, the robust constant modulus beamformer (RCMB) is also discussed as a special case of the QCMD beamformer. Simulation results demonstrate the efficiency of the gradient projection algorithm and superiority of the QCMD beamformer over several representative robust beamformers, indicating that it can approach the optimal performance bound.
A new species of Mantidactylus (subgenus Chonomantis) from Ranomafana National Park, eastern Madagascar (Amphibia, Anura, Mantellidae)

We describe a new frog species of Mantidactylus belonging to the subgenus Chonomantis from Ranomafana National Park, in the Southern Central East region of Madagascar, at mid-elevations (about 950 m above sea level). Specimens of Mantidactylus paidroa sp. nov. were observed during the day in cavities under large rocks next to a stream in rainforest. Their advertisement calls are unique in Chonomantis and consist of a long series of note pairs. The new species shows a high divergence to other Chonomantis species in DNA sequences of the mitochondrial 16S rRNA gene (5.6–10.8%). Mantidactylus paidroa is so far only known from several streams in Ranomafana National Park but may have been overlooked at other rainforest sites in eastern Madagascar. We propose an IUCN threat status of Data Deficient for this new species.
Reducing multi-qubit interactions in adiabatic quantum computation without adding auxiliary qubits. Part 1: The "deduc-reduc" method and its application to quantum factorization of numbers

Adiabatic quantum computing has recently been used to factor 56153 [Dattani & Bryans, arXiv:1411.6758] at room temperature, which is orders of magnitude larger than any number attempted yet using Shor's algorithm (circuit-based quantum computation). However, this number is still vastly smaller than RSA-768 which is the largest number factored thus far on a classical computer. We address a major issue arising in the scaling of adiabatic quantum factorization to much larger numbers. Namely, the existence of many 4-qubit, 3-qubit and 2-qubit interactions in the Hamiltonians. We showcase our method on various examples, one of which shows that we can remove 94% of the 4-qubit interactions and 83% of the 3-qubit interactions in the factorization of a 25-digit number with almost no effort, without adding any auxiliary qubits. Our method is not limited to quantum factoring. Its importance extends to the wider field of discrete optimization. Any CSP (constraint-satisfiability problem), psuedo-boolean optimization problem, or QUBO (quadratic unconstrained Boolean optimization) problem can in principle benefit from the "deduction-reduction" method which we introduce in this paper. We provide an open source code which takes in a Hamiltonian (or a discrete discrete function which needs to be optimized), and returns a Hamiltonian that has the same unique ground state(s), no new auxiliary variables, and as few multi-qubit (multi-variable) terms as possible with deduc-reduc.
Article: Banking on Bananas, Crediting Crafts: Financing Women's Work in the Philippine Cordillera

This paper addresses the emergence of microfinance programmes in international development as a preferred strategy for povertyalleviation and empowering women. Drawing on ethnographic research in the Philippine Cordillera, it argues that microfinance projects have embedded social change objectives in initiatives driven by market-led forces thereby failing to realize social justice for women. To effect a more normative agenda for development, this paper suggests integrating women's perspectives and initiatives other than credit.
BAYESIAN METHOD FOR SEGMENTATION OF SAR IMAGES IN ROUGH TERRAIN

Radiometric correction is the essential prerequisite to obtain precise and valuable segmentation of remote sensing images, especially when dealing with mountainous regions where the terrain is more likely to be rough. Important applications such as snow cover segmentation have usually to be performed on images of very rough mountainous terrain where this preprocessing step turns out to be especially demanding. The knowledge of the topography of the imaged area through a digital elevation model (DEM) and of the backscatter function for the different terrain cover types are the basis for radiometric correction. Considering SAR images, the huge amount of processing for geographic and geometric calibration and registration that is needed prior to analysis is well established. Nonetheless, even assuming that these calibration and registration steps can been carried out with high precision algorithms, they are still prone to inaccuracies due to the quality of the terrain geometrical description. In the following is presented a model-based method that, exploiting the information contained both in the DEM and in the image, provides improved estimates, in a Bayesian framework, of the terrain itself and of the radiometric characteristics of the land cover.
Techniques for Dynamic State Estimation of Machines in Power Systems

The knowledge of dynamic states of electrical machine, especially the relative rotor position and velocity, are very important for us to understand the machine performance and to possibly design advanced control systems. This paper addresses the state estimation problem of synchronous machines in power systems, both in deterministic and stochastic cases during small transients. The paper examines Extended Kalman Filters (EKF) and Particle Filter (PF) approaches. With real-time data collected by phasor measurement unit (PMU) and sufficiently known machine model, the simulation results show that the states dynamics can be successfully and accurately estimated. The method proposed in this paper can be easily applied to other type machines or extended to include parameter estimation.
Quadrilateral Modelling and Robust Control of a Nonlinear Piezoelectric Cantilever

Piezocantilevers are commonly used for the actuation of micromechatronic systems. These systems are generally used to perform micromanipulation tasks which require high positioning accuracy. However, the nonlinearities, i.e., the hysteresis and the creep, of piezoelectric materials and the influence of the environment (vibrations, temperature change, etc.) create difficulties for such a performance to be achieved. Various models have been used to take into account the nonlinearities but they are often complex. In this paper, we study a one degree of freedom piezoelectric cantilever. For that, we propose a simple new model where the hysteresis curve is approximated by a quadrilateral and the creep is considered to be a disturbance. To facilitate the modelling, we first demonstrate that the dynamic hysteresis of the piezocantilever is equivalent to a static hysteresis, i.e., a varying gain, in series with a linear dynamic part. The obtained model is used to synthesize a linear robust controller, making it possible to achieve the performances required in micromanipulation tasks. The experimental results show the relevance of the combination of the developed model and the synthesized robust H infin controller.
The influence of knowledge attributes on innovation protection mechanisms

This article deals with the appropriability of technological innovations. Specifically, it looks at how certain attributes of technological knowledge (codifiability, teachability, complexity, observability and dependency) may influence the choices that firms make among different protection mechanisms (patents, secrecy, lead time, learning curve and complementary resources). Based on a sample of 670 innovations developed by Spanish industrial firms, it is found that dependency and, to a lesser extent, codifiability are the attributes that have the most influence the choice of appropriation method. Copyright # 2004 John Wiley & Sons, Ltd.