Miracle-dz/UniFAR-FTUs huggingface dataset:
Extending the TITE CRM to Multiple Outcomes with Application to a Phase 1 Clinical Trial in Canine Hemangiosarcoma

In traditional phase 1 oncology trials, the safety of a new chemotherapeutic agent is tested in a dose escalation study to identify the maximum tolerated dose, which is defined as the highest dose with acceptable toxicity. An alternate approach is to jointly model toxicity and efficacy and allow dose finding to be directed by a pre-specified trade-off between efficacy and toxicity. With this goal in mind, several phase 1 designs have been proposed to jointly model toxicity and efficacy in phase 1 dose escalation studies. A factor limiting the use of these designs is that toxicity and efficacy must be observed in a timely manner. This is particularly problematic for the efficacy outcome, which is often measured over a longer timeframe than the toxicity outcome. One approach to overcoming this problem is to model toxicity and efficacy as time-to-event outcomes. We propose a phase 1 dose escalation study that jointly models toxicity and efficacy as time-to-event outcomes by extending the time-to-event CRM to accommodate multiple outcomes. We consider the application of our design in the context of a phase 1 clinical trial of a novel targeted toxin for canine hemangiosarcoma. The operating characteristics of our design are evaluated by simulation and compared to the operating characteristics for existing phase 1 designs that consider toxicity and efficacy as binary outcomes.
Set-Theoretic Preliminaries for Computer Scientists

The basics of set theory are usually copied, directly or indirectly, by computer scientists from introductions to mathematical texts. Often mathematicians are content with special cases when the general case is of no mathematical interest. But sometimes what is of no mathematical interest is of great practical interest in computer science. For example, non-binary relations in mathematics tend to have numerical indexes and tend to be unsorted. In the theory and practice of relational databases both these simplifications are unwarranted. In response to this situation we present here an alternative to the ``set-theoretic preliminaries'' usually found in computer science texts. This paper separates binary relations from the kind of relations that are needed in relational databases. Its treatment of functions supports both computer science in general and the kind of relations needed in databases. As a sample application this paper shows how the mathematical theory of relations naturally leads to the relational data model and how the operations on relations are by themselves already a powerful vehicle for queries.
Structural Return Maximization for Reinforcement Learning

Batch Reinforcement Learning (RL) algorithms attempt to choose a policy from a designer-provided class of policies given a fixed set o f training data. Choosing the policy which maximizes an estimate of return often leads to over-fitting when only limited data is available, due to the size of the policy class in relation to the amount of data available. In this work, we focus on learning policy classes that are appropriately sized to the amount of data available. We accomplish this by using the principle of Structural Risk Minimization, from Statistical Learning Theory, which uses Rademacher complexity to identify a policy class that maximizes a bound on the return of the best policy in the chosen policy class, given the available data. Unlike similar batch RL approaches, our bound on return requires only extremely weak assumptions on the true system.
General Relativistic Hydrodynamic Simulations and Linear Analysis of the Standing Accretion Shock Instability around a Black Hole

We study the stability of standing shock waves in advection-dominated accretion flows into a Schwarzschild black hole using two-dimensional general relativistic hydrodynamic simulations, as well as linear analysis, in the equatorial plane. We demonstrate that the accretion shock is stable against axisymmetric perturbations but becomes unstable to nonaxisymmetric perturbations. The results of the dynamical simulations show good agreement with the linear analysis on the stability and the oscillation and growth timescales. A comparison of different wave-travel times with the growth timescales of the instability suggests that it is likely to be of the Papaloizou-Pringle type, induced by the repeated propagations of acoustic waves. However, the wavelengths of the perturbations are too long to allow a clear definition of the reflection point. By analyzing the nonlinear phase in the dynamical simulations, we show that quadratic mode couplings precede the nonlinear saturation. It is also found that not only short-term random fluctuations due to turbulent motion, but also quasi-periodic oscillations on longer timescales, take place in the nonlinear phase. We give some possible implications of the instability for black hole quasi-periodic oscillations and the central engine in gamma-ray bursts.
Industrial Application of Concolic Testing on Embedded Software: Case Studies

Current industrial testing practices often build test cases in a manual manner, which is slow and ineffective. To alleviate this problem, concolic testing generates test cases that can achieve high coverage in an automated fashion. However, due to a large number of possible execution paths, concolic testing might not detect bugs even after spending significant amount of time. Thus, it is necessary to check if concolic testing can detect bugs in embedded software in a practical manner through case studies. This paper describes case studies of applying the concolic testing tool CREST to embedded Applications. Through this project, we have detected new faults in the Samsung Linux Platform (SLP) file manager, Samsung security library, and busy box ls.
Identification of functional progenitor cells in the pulmonary vasculature

The pulmonary vasculature comprises a complex network of branching arteries and veins all functioning to reoxygenate the blood for circulation around the body. The cell types of the pulmonary artery are able to respond to changes in oxygen tension in order to match ventilation to perfusion. Stem and progenitor cells in the pulmonary vasculature are also involved, be it in angiogenesis, endothelial dysfunction or formation of vascular lesions. Stem and progenitor cells may be circulating around the body, residing in the pulmonary artery wall or stimulated for release from a central niche like the bone marrow and home to the pulmonary vasculature along a chemotactic gradient. There may currently be some controversy over the pathogenic versus therapeutic roles of stem and progenitor cells and, indeed, it is likely both chains of evidence are correct due to the specific influence of the immediate environmental niche a progenitor cell may be in. Due to their great plasticity and a lack of specific markers for stem and progenitor cells, they can be difficult to precisely identify. This review discusses the methodological approaches used to validate the presence of and subtype of progenitors cells in the pulmonary vasculature while putting it in context of the current knowledge of the therapeutic and pathogenic roles for such progenitor cells.
Classification and reconstruction of compressed GMM signals with side information

This paper offers a characterization of performance limits for classification and reconstruction of high-dimensional signals from noisy compressive measurements, in the presence of side information. We assume the signal of interest and the side information signal are drawn from a correlated mixture of distributions/components, where each component associated with a specific class label follows a Gaussian mixture model (GMM). We provide sharp sufficient and/or necessary conditions for the phase transition of the misclassification probability and the reconstruction error in the low-noise regime. These conditions, which are reminiscent of the well-known Slepian-Wolf and Wyner-Ziv conditions, are a function of the number of measurements taken from the signal of interest, the number of measurements taken from the side information signal, and the geometry of these signals and their interplay.
Psychovisual and statistical optimization of quantization tables for DCT compression engines

The paper presents a new and statistically robust algorithm able to improve the performance of the standard DCT compression algorithm for both perceived quality and compression size. The approach proposed combines together an information theoretical/statistical approach with HVS (human visual system) response functions. The methodology applied permits us to obtain a suitable quantization table for specific classes of images and specific viewing conditions. The paper presents a case study where the right parameters are learned after an extensive experimental phase, for three specific classes: document, landscape and portrait. The results show both perceptive and measured (in term of PSNR) improvement. A further application shows how it is possible obtain significant improvement profiling the relative DCT error inside the pipeline of images acquired by typical digital sensors.
Plurality and Resemblance in fMRI Data Analysis

We apply nine analytic methods employed currently in imaging neuroscience to simulated and actual BOLD fMRI signals and compare their performances under each signal type. Starting with baseline time series generated by a resting subject during a null hypothesis study, we compare method performance with embedded focal activity in these series of three different types whose magnitudes and time courses are simple, convolved with spatially varying hemodynamic responses, and highly spatially interactive. We then apply these same nine methods to BOLD fMRI time series from contralateral primary motor cortex and ipsilateral cerebellum collected during a sequential finger opposition study. Paired comparisons of results across methods include a voxel-specific concordance correlation coefficient for reproducibility and a resemblance measure that accommodates spatial autocorrelation of differences in activity surfaces. Receiver-operating characteristic curves show considerable model differences in ranges less than 10% significance level (false positives) and greater than 80% power (true positives). Concordance and resemblance measures reveal significant differences between activity surfaces in both data sets. These measures can assist researchers by identifying groups of models producing similar and dissimilar results, and thereby help to validate, consolidate, and simplify reports of statistical findings. A pluralistic strategy for fMRI data analysis can uncover invariant and highly interactive relationships between local activity foci and serve as a basis for further discovery of organizational principles of the brain. Results also suggest that a pluralistic empirical strategy coupled formally with substantive prior knowledge can help to uncover new brain-behavior relationships that may remain hidden if only a single method is employed.
Novel virtual lead identification in the discovery of hematopoietic cell kinase (HCK) inhibitors: application of 3D QSAR and molecular dynamics simulation

Abstract High level of hematopoietic cell kinase (Hck) is associated with drug resistance in chronic myeloid leukemia. Additionally, Hck activity has also been connected with the pathogenesis of HIV-1 and chronic obstructive pulmonary disease. In this study, three-dimensional (3D) QSAR pharmacophore models were generated for Hck based on experimentally known inhibitors. A best pharmacophore model, Hypo1, was developed with high correlation coefficient (0.975), Low RMS deviation (0.60) and large cost difference (49.31), containing three ring aromatic and one hydrophobic aliphatic feature. It was further validated by the test set (r = 0.96) and Fisher’s randomization method (95%). Hypo 1 was used as a 3D query for screening the chemical databases, and the hits were further screened by applying Lipinski’s rule of five and ADMET properties. Selected hit compounds were subjected to molecular docking to identify binding conformations in the active site. Finally, the appropriate binding modes of final hit compounds were revealed by molecular dynamics (MD) simulations and free energy calculation studies. Hence, we propose the final three hit compounds as virtual candidates for Hck inhibitors.
Effective Transliteration

The translation of texts written in different languages is r equired in many domains, such as machine translation and cross-lingual information retrieval. Translating wor ds of a text from asource language into a different target language can be efficiently achieved using a bilingual vocab ulary, where every source word has a counterpart in the target language. In practice, however, there are ofte n out-of-vocabulary (OOV) words that do not appear in the bilingual dictionary. This is particularly common fo r proper nouns such as company, people, place and product names. The goal of this research is to explore the pro blem of dealing with these words for Persian and related languages such as Urdu. The problem of OOV words has n ot previously been studied for Persian. We will investigate novel techniques making use of special feature s of this language for: transliteration; character alignme t; and, OOV dictionary construction.
Evolutionary Computation 2.1 Conceptual Foundations of Evolutionary Computation

The goal of this chapter is to introduce the key algorithms and theory that constitute the core of Evolutionary computation. The principles and foundations of a basic genetic Algorithm are described in detail. In addition, a separate section discusses extensions to the simple Genetic Algorithm, such as reened crossover, mutation and selection schemes. and real coded representations. The chapter presents real-coded genetic Algorithms and messy genetic Algorithms which encode genetic information in a diierent way. The chapter concludes with an introduction to other types of Evolutionary Algorithms such as Evolution Strategies, Evolutionary programming and Genetic Programming. Evolutionary Algorithms (EAs) constitute a class of search and optimization methods, which imitate the principles of natural evolution (Goldberg, 1989; Holland, 1975). The common term Evolutionary Computation comprises techniques such as Genetic Algorithms, Evolution Strategies, Evolutionary Programming and Genetic Programming. Their principal mode of operation is based on the same generic concepts, a population of competing candidate solutions, random combination and alteration of potentially useful structures to generate new solutions and a selection mechanism to increase the proportion of better solutions. The diierent approaches are distinguished by the genetic structures that undergo adaptation and the genetic operators that generate new candidate solutions. Since Evolutionary Algorithms emulate natural evolution they adopt a 1
A System for Synergistically Structuring News Content from Traditional Media and the Blogosphere

Abstract: News and social media are emerging as a dominant source of information for numerous applications. However, their vast unstructured content present challenges to efficient extraction of such information. In this paper, we present the SYNC3 system that aims to intelligently structure content from both traditional news media and the blogosphere. To achieve this goal, SYNC3 incorporates innovative algorithms that first model news media content statistically, based on fine clustering of articles into so-called “news events”. Such models are then adapted and applied to the blogosphere domain, allowing its content to map to the traditional news domain. Furthermore, appropriate algorithms are employed to extract news event labels and relations between events, in order to efficiently present news content to the system end users. 1. Introduction News content in the internet, available through both traditional news media portals and the blogosphere, constitutes valuable information to both professionals and casual internet users, who however can be inundated by its vast amount. Clearly, such information could be much more useful if presented and delivered in a well-structured way. Many attempts, taking the form of either research projects or commercial solutions, have been made to provide centralised repositories of such content [1][2][3]. However, to date, there exists no integrated system that structures blog post content across these two broad sources of news information in parallel, capable to meet the requirements of a broad range of end users, such as professional journalists, communication experts, and citizen bloggers. The SYNC3 system
Using Science as Evidence in Public Policy

Using Science as Evidence in Public Policy encourages scientists to think differently about the use of scientific evidence in policy making. This report investigates why scientific evidence is important to policy making and argues that an extensive body of research on knowledge utilization has not led to any widely accepted explanation of what it means to use science in public policy. Using Science as Evidence in Public Policy identifies the gaps in our understanding and develops a framework for a new field of research to fill those gaps. For social scientists in a number of specialized fields, whether established scholars or Ph.D. students, Using Science as Evidence in Public Policy shows how to bring their expertise to bear on the study of using science to inform public policy. More generally, this report will be of special interest to scientists who want to see their research used in policy making, offering guidance on what is required beyond producing quality research, beyond translating results into more understandable terms, and beyond brokering the results through intermediaries, such as think tanks, lobbyists, and advocacy groups. For administrators and faculty in public policy programs and schools, Using Science as Evidence in Public Policy identifies critical elements of instruction that will better equip graduates to promote the use of science in policy making.
Reusing or Reinventing The Wheel: The Search-Transfer Issue in Open Source Communities

Despite the raising awareness about the importance of open innovation communities in knowledge economies, empirical evidence about the structural determinants of knowledge reuse in these communities is lacking. In order to address this gap, the current study sets out to investigate the network-level determinants of knowledge reuse in open source projects. I suggest tracking code reuse across open source projects as a feasible and accessible proxy measure for knowledge reuse. I argue that in spite of favorable conditions, search and processing costs associated with knowledge reuse remain high enough to localize reuse behavior of open source developers. I hypothesize that network-level proximity of projects within the social network of open source community is a significant determinant of code reuse. A report on the progress of the empirical section of the research project along with some confirmatory preliminary results has been included.
Cooperative reinforcement learning approach for routing in ad hoc networks

Most of the routing algorithms over ad hoc networks are based on the status of the link (up or down). They are not capable of adapting the run time changes such as traffic load, delay and delivery time to reach to the destination etc, thus though provides shortest path, these shortest path may not be optimum path to deliver the packets. Optimum path can only be achieved when quality of links within the network is detected on continuous basis instead of discrete time. Thus for achieving optimum routes we model ad hoc routing as a cooperative reinforcement learning problem. In this paper, agents are used to optimize the performance of a network on trial and error basis. This learning strategy is based work in swarm intelligence: those systems whose design is inspired by models of social insect behaviour. This paper describes the algorithm used in cooperative reinforcement learning approach and performs the analysis by comparing with existing routing protocols.
Design and implementation of a low cost 3D printed humanoid robotic platform

Humanoid robot with dual manipulators and dexterous hands shows great significance in domestic, medical and service applications. They can provide companion, operation, manipulation, material handling and many other services to human beings. However, current humanoid robots are either too expansive or too clumsy. There is a trade-off between robot flexibility and costs. It is a huge obstacle blocking the road of humanoid robot toward our daily life. In this paper, the design and implementation of a low cost dual wheel and dual arm humanoid robotic platform is introduced. The robot is based on the open source 3D printed humanoid robot “InMoov”. To fully explore the potential of “InMoov”, we redesigned its electrical system based on the developed Embedded Controller, and equipped it with a Mini PC and a touch screen for human-robot interaction. By using the 485 bus and modbus protocol, the wiring complexity is greatly decreased. A differential drive mobile platform is integrated to enable the robot with mobility and furthermore, a data glove system is also designed to explore a new type of robot programming technology. Robot Operating System(ROS) is used to realize the robot control and human-robot interaction. Comparing to the existing humanoid robots, the developed humanoid robotic platform is low cost but has fully functionality.
Gaussian Process Assisted Fingerprinting Localization

This paper presents an application of the firefly algorithm (FA) to Gaussian process (GP)-based localization. Partial radio-frequency (RF) signature map is first collected and used to train the GP model. The hyperparameters of the GP prior model are searched by the FA. GP regression is then used to generate an estimation of the RF signature map for the entire area to be localized. This is in contrast to traditional fingerprinting-based localization where a database of RF signature has to be collected for the entire area of interest. Using the estimated signature map, the position of the device is estimated using a combined likelihood function from multiple access points (APs). The proposed scheme relies on only existing infrastructures and can be used both indoor and outdoor. Experiments using indoor WiFi APs show that median error is around 3 m.
Analysis of land surface characteristics and monitoring of surface changes over Patagony using ERS SAR Interferometric Land Use images

This paper presents one of the interferometric related applications that has recently attracted more interest from the scientific community. The objective is to exploit the coherence information derivable from a couple of ERS SAR images to evaluate land surface characteristics and its changes. An area located in Patagony has been selected for this work. Results should provide information about the surface characteristics and their evolution in time. Simultaneously, they are expected to confirm the potential and validity of a new kind of ERS SAR product for land use analysis and surface changes monitoring that is currently being tested at ESRIN.