Miracle-dz/UniFAR-FTUs huggingface dataset:
Which pointer analysis should I use?

During the past two decades many different pointer analysis algorithms have been published. Although some descriptions include measurements of the effectiveness of the algorithm, qualitative comparisons among algorithms are difficult because of varying infrastructure, benchmarks, and performance metrics. Without such comparisons it is not only difficult for an implementor to determine which pointer analysis is appropriate for their application, but also for a researcher to know which algorithms should be used as a basis for future advances. This paper describes an empirical comparison of the effectiveness of five pointer analysis algorithms on C programs. The algorithms vary in their use of control flow information (flow-sensitivity) and alias data structure, resulting in worst-case complexity from linear to polynomial. The effectiveness of the analyses is quantified in terms of compile-time precision and efficiency. In addition to measuring the direct effects of pointer analysis, precision is also reported by determining how the information computed by the five pointer analyses affects typical client analyses of pointer information: Mod/Ref analysis, live variable analysis and dead assignment identification, reaching definitions analysis, dependence analysis, and conditional constant propagation and unreachable code identification. Efficiency is reported by measuring analysis time and memory consumption of the pointer analyses and their clients.
Local motions in a benchmark of allosteric proteins

Allosteric proteins have been studied extensively in the last 40 years, but so far, no systematic analysis of conformational changes between allosteric structures has been carried out. Here, we compile a set of 51 pairs of known inactive and active allosteric protein structures from the Protein Data Bank. We calculate local conformational differences between the two structures of each protein using simple metrics, such as backbone and side‐chain Cartesian displacement, and torsion angle change and rearrangement in residue–residue contacts. Thresholds for each metric arise from distributions of motions in two control sets of pairs of protein structures in the same biochemical state. Statistical analysis of motions in allosteric proteins quantifies the magnitude of allosteric effects and reveals simple structural principles about allostery. For example, allosteric proteins exhibit substantial conformational changes comprising about 20% of the residues. In addition, motions in allosteric proteins show strong bias toward weakly constrained regions such as loops and the protein surface. Correlation functions show that motions communicate through protein structures over distances averaging 10–20 residues in sequence space and 10–20 Å in Cartesian space. Comparison of motions in the allosteric set and a set of 21 nonallosteric ligand‐binding proteins shows that nonallosteric proteins also exhibit bias of motion toward weakly constrained regions and local correlation of motion. However, allosteric proteins exhibit twice as much percent motion on average as nonallosteric proteins with ligand‐induced motion. These observations may guide efforts to design flexibility and allostery into proteins. Proteins 2007. © 2007 Wiley‐Liss, Inc.
A real-time communication framework for wireless sensor-actuator networks

Wireless sensor-actuator network (WSAN) comprises of a group of distributed sensors and actuators that communicate through wireless links. Sensors are small and static devices with limited power, computation, and communication capabilities responsible for observing the physical world. On the other hand, actuators are equipped with richer resources, able to move and perform appropriate actions. Sensors and actuators cooperate with each other: While sensors perform sensing, actuators make decisions and react to the environment with the right actions. WSAN can be applied in a wide range of applications, like environmental monitoring, battlefield surveillance, chemical attack detection, intrusion detection, space missions, etc. Since actuators perform actions in response to the sensed events, real-time communications and quick reaction are necessary. To provide effective applications by WSAN, two major problems remain: how to minimize the transmission delay from sensors to actuators, and how to improve the coordination among the actuators for fast reaction. To tackle these problems, we designed a real-time communication framework to support event detection, reporting, and actuator coordination. This paper explores the timely communication and coordination problems among the sensors and actuators. Moreover, we proposed two self-organized and distributed algorithms for event reporting and actuator coordination. Some preliminary results are presented to demonstrate the advantages of our approach
An Optimized Huffmans Coding by the method of Grouping

Data compression has become a necessity not only the in the field of communication but also in various scientific experiments. The data that is being received is more and the processing time required has also become more. A significant change in the algorithms will help to optimize the processing speed. With the invention of Technologies like IoT and in technologies like Machine Learning there is a need to compress data. For example training an Artificial Neural Network requires a lot of data that should be processed and trained in small interval of time for which compression will be very helpful. There is a need to process the data faster and quicker. In this paper we present a method that reduces the data size. This method is known as Optimized Huffmans Coding. In the Huffmans coding we encode the messages so as to reduce the data and here in the optimized Huffmans coding we compress the data to a great extent which helps in various signal processing algorithms and has advantages in many applications. Here in this paper we have presented the Optimized Huffmans Coding method for Text compression. This method but has advantages over the normal Huffmans coding. This algorithm presented here says that every letter can be grouped together and encoded which not only reduces the size but also the Huffmans Tree data that is required for decoding, hence reducing the data size. This method has huge scientific applications.
p53 Requires the Stress Sensor USF1 to Direct Appropriate Cell Fate Decision

Genomic instability is a major hallmark of cancer. To maintain genomic integrity, cells are equipped with dedicated sensors to monitor DNA repair or to force damaged cells into death programs. The tumor suppressor p53 is central in this process. Here, we report that the ubiquitous transcription factor Upstream Stimulatory factor 1 (USF1) coordinates p53 function in making proper cell fate decisions. USF1 stabilizes the p53 protein and promotes a transient cell cycle arrest, in the presence of DNA damage. Thus, cell proliferation is maintained inappropriately in Usf1 KO mice and in USF1-deficient melanoma cells challenged by genotoxic stress. We further demonstrate that the loss of USF1 compromises p53 stability by enhancing p53-MDM2 complex formation and MDM2-mediated degradation of p53. In USF1-deficient cells, the level of p53 can be restored by the re-expression of full-length USF1 protein similarly to what is observed using Nutlin-3, a specific inhibitor that prevents p53-MDM2 interaction. Consistent with a new function for USF1, a USF1 truncated protein lacking its DNA-binding and transactivation domains can also restore the induction and activity of p53. These findings establish that p53 function requires the ubiquitous stress sensor USF1 for appropriate cell fate decisions in response to DNA-damage. They underscore the new role of USF1 and give new clues of how p53 loss of function can occur in any cell type. Finally, these findings are of clinical relevance because they provide new therapeutic prospects in stabilizing and reactivating the p53 pathway.
Religious Harmony Within Ahmadiyah Community: (Study of Living Qur'an and the Peace Construction in the Village of Gondrong Kenanga

In the midst of intolerant attitudes toward Ahmadiyah, the community of Ahmadiyah in Gondrong Kenanga present as a villagers that value diversity. Not only Ahmadiyah followers but also many other groups live in the village, such as Muslims affiliated to NU, Muhammadiyah, and other groups of religious believers. Interstingly, in everyday life they are able to protect themselve from getting involved in conflict and creating a peaceful and harmonious life. Harmony in the village Gondrong appears in at least two forms including in the pattern of inter-community relations in the environment and in daily life interction of people reflected in the social environment. Gondrong Ahmadiyah community is actively involved in various social activities that promote tolerance, cooperation, and solidarity. The work aims at getting the concept of peace in the Ahmadiyah community in Gondrong Kenanga. This study also examines the living Qur’an regarding the understanding of the concept of pluralism and harmony within Ahmadiyah members. This work is based on a field research project on the phenomenon of living Qur’an studies that examines the understanding of the Islamic teaching on diversity by the Jamaah Ahmadiyah Indonesia of Gondrong-kenanga (JAIG), and also the construction of peace which is practiced in their daily life.
A-Close+: An Algorithm for Mining Frequent Closed Itemsets

Association Rule Mining (ARM) is the most essential technique for data mining that mines hidden associations between data in large databases. The most important function of ARM is to find frequent itemsets. Frequent closed itemsets (FCI) is an important condense representation method for frequent itemsets, and because of its importance in recent years, there have been many algorithms implemented for it. One of the most fundamental algorithms for frequent closed itemset is A-close. In this paper, we optimize this algorithm using both optimized techniques "reducing pruning time" and "reducing database size", called "A-close+". Results show that the performance cost of our algorithm is considerably less than A-close.
An Iterative Approach to Estimating Frequencies over a Semantic Hierarchy

This paper is concerned with using a semant ic hierarchy to es t imate the frequency with which a word sense appears as a given argument of a verb, assuming the da ta is not sense disambiguated. The s tandard approach is to split the count for any noun appearing in the da t a equally among the alternat ive senses of the noun. This can lead to inaccurate estimates. We describe a rees t imation process which uses the accumulated counts of hypernyms of the al ternat ive senses in order to redis t r ibute the count. In order to choose a hypernym for each alternative sense, we employ a novel technique which uses a X 2 test to measure the homogeneity of sets of concepts in the hierarchy.
Global dynamic window approach for holonomic and non-holonomic mobile robots with arbitrary cross-section

This paper presents an extension of current global dynamic window approaches to holonomic and nonholonomic mobile robots with an arbitrary cross-section. The algorithm proceeds in two stages. In order to account for an arbitrary robot footprint, the first stage takes the robot's orientation explicitly into account by constructing a navigation function in the (x, y, /spl theta/) configuration space. In a second stage, an admissible velocity is chosen from a window around the robot's current velocity, which contains all velocities that can be reached under the acceleration constraints. Fast computation over large areas is achieved by adopting multi-resolution (x, y) and (x, y, /spl theta/) planning. Several measures are taken to obtain safe and robust robot behaviour. Experimental results on our wheelchair test platform show the feasibility of the approach.
Variability and classification accuracy of serial high-sensitivity C-reactive protein measurements in healthy adults.

BACKGROUND Increased concentrations of high-sensitivity C-reactive protein (hs-CRP), a marker of systemic inflammation, are associated with increased risk for coronary heart disease. Because of its relationship to inflammation, hs-CRP has considerable biologic variation. This study was carried out to characterize CRP variation and to compare it to another risk factor, total serum cholesterol. METHODS One hundred thirteen individuals were scheduled to have five measurements each of hs-CRP and total cholesterol carried out at quarterly intervals over a 1-year period. Variations of hs-CRP and total cholesterol were characterized, and classification accuracy was described and compared for both. RESULTS The relative variation was comparable for hs-CRP and total cholesterol. When classified by quartile, 63% of first and second hs-CRP measurements were in agreement; for total cholesterol it was 60%. Ninety percent of hs-CRP measurements were within one quartile of each other. This relationship was not altered by the use of log-transformed hs-CRP data. CONCLUSION hs-CRP has a degree of measurement stability that is similar to that of total cholesterol.
Model-Based Clustering, Discriminant Analysis, and Density Estimation

Cluster analysis is the automated search for groups of related observations in a dataset. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures, and most clustering methods available in commercial software are also of this type. However, there is little systematic guidance associated with these methods for solving important practical questions that arise in cluster analysis, such as how many clusters are there, which clustering method should be used, and how should outliers be handled. We review a general methodology for model-based clustering that provides a principled statistical approach to these issues. We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and multivariate density estimation. We give examples from medical diagnosis, minefield detection, cluster recovery from noisy data, and spatial density estimation. Finally, we mention limitations of the methodology and discuss recent developments in model-based clustering for non-Gaussian data, high-dimensional datasets, large datasets, and Bayesian estimation.
Longitudinal analysis of extreme prematurity: a neuroimage investigation of early brain development

Brain development is a complex process, and disruptions from its normal course may affect the later neurological outcome of an individual. Preterm infants are at higher risk of disability, since a substantial part of brain development happens outside the mother’s womb, making it vulnerable to a range of insults. Understanding the early brain development during the preterm period is of critical importance and magnetic resonance imaging (MRI) allows us to investigate this. Methodologically, this is a challenging task, as classical approaches of studying longitudinal development over this period do not cope with the large changes taking place. This thesis focuses on the development of tools to study the changes in cortical folding, shape of different brain structures and microstructural changes over the preterm period from longitudinal data of extremely preterm-born infants. It describes a tissue segmentation pipeline, optimised on a postmortem fetal dataset, and then focuses on finding longitudinal correspondences between the preterm and termequivalent brain regions and structures in extremely preterm-born infants using MRI. Three novel registration techniques are proposed for longitudinal registration of this challenging data. These are based on matching the spectral components associated with either the cortical surfaces, diffusion tensor images, or both. These allow us to quantify longitudinal changes in different brain regions and structures. We investigated changes in cortical folding of different lobes, microstructural changes and tracts in the white matter, cortical thickness and changes in cortical fractional anisotropy and mean diffusivity. We used cortical surface registration to look at shape differences between controls and extremely preterm-born young adults to gain an insight into the long-term impact of prematurity. This research may contribute to the development of early biomarkers for predicting the neurological outcome of preterm infants and illuminate our understanding of brain development during this crucial period.
Robust visual SLAM across seasons

In this paper, we present an appearance-based visual SLAM approach that focuses on detecting loop closures across seasons. Given two image sequences, our method first extracts one descriptor per image for both sequences using a deep convolutional neural network. Then, we compute a similarity matrix by comparing each image of a query sequence with a database. Finally, based on the similarity matrix, we formulate a flow network problem and compute matching hypotheses between sequences. In this way, our approach can handle partially matching routes, loops in the trajectory and different speeds of the robot. With a matching hypothesis as loop closure information and the odometry information of the robot, we formulate a graph based SLAM problem and compute a joint maximum likelihood trajectory.
Accurate 3-Dimensional Imaging Method Based on Extended RPM for Rotating Target Model

Three-dimensional (3-D) reconstruction techniques employed by airborne radars are essential for object recognition in scenarios where optically vision is blurry, and are required for the monitoring of disasters and coast-guard patrols. There have been reports on 3-D reconstruction methods that exploit the layover appearing in inverse synthetic aperture radar (ISAR) imagery, which are suitable for the recognition of artificial targets such as buildings, aircraft or ships. However, existing methods assume only a point target or the aggregate of point targets, and most require the tracking of the multiple points over sequential ISAR images. In the case of a solid object with a continuous boundary, such as a wire or polyhedral structure, the positioning accuracy of such methods is severely degraded owing to scattering centers continuously shifting on the target surface with changes in the rotation angle. To overcome this difficulty, this paper extends the original Range Points Migration (RPM) method to the ISAR observation model, where a double mono-static model with two transmitting and receiving antennas is introduced to suppress cross-range ambiguity. The results of numerical simulation and experimental validation demonstrate that the extended RPM method has a distinct advantage for accurate 3-D imaging, even for non-point targets.
Towards an Ecological Design of a 4-Dimensional Separation Assistance Interface

Currently the world’s airspace is being operated at the limits of both capacity and safety by different stakeholders. Various initiatives worldwide are investigating future air traffic management concepts. A shift towards trajectory-based environments is identified, where user needs and performance capabilities are leading to user preferred routing by using Airborne Separation Assistance Systems. Within the Control & Simulation Department two innovative interfaces have been developed based on the Ecological Interface Design paradigm, that prevent potential loss of separation in the horizontal and vertical plane respectively. This paper describes the foundations of these interfaces and describes the research heading towards an ecological design of a 4-dimensional (space and time) separation assistance interface.
Blind PARAFAC receivers for DS-CDMA systems

This paper links the direct-sequence code-division multiple access (DS-CDMA) multiuser separation-equalization-detection problem to the parallel factor (PARAFAC) model, which is an analysis tool rooted in psychometrics and chemometrics. Exploiting this link, it derives a deterministic blind PARAFAC DS-CDMA receiver with performance close to non-blind minimum mean-squared error (MMSE). The proposed PARAFAC receiver capitalizes on code, spatial, and temporal diversity-combining, thereby supporting small sample sizes, more users than sensors, and/or less spreading than users. Interestingly, PARAFAC does not require knowledge of spreading codes, the specifics of multipath (interchip interference), DOA-calibration information, finite alphabet/constant modulus, or statistical independence/whiteness to recover the information-bearing signals. Instead, PARAFAC relies on a fundamental result regarding the uniqueness of low-rank three-way array decomposition due to Kruskal (1977, 1988) (and generalized herein to the complex-valued case) that guarantees identifiability of all relevant signals and propagation parameters. These and other issues are also demonstrated in pertinent simulation experiments.
Empirical Applications of Multidimensional Inequality Analysis

This paper explores the empirical application of theoretical multidimensional inequality analysis using real household welfare distributions. The paper operationalises recent conceptual developments in multidimensional inequality theory and assesses their usefulness for measurement and policy analysis. Despite the existence of a thriving theoretical literature on multidimensional inequality, empirical applications, particularly at the individual and household levels, are few and far between. This paper compares and contrasts different methodologies for the analysis of multidimensional welfare, including multidimensional inequality indices and stochastic dominance techniques. The results strongly highlight the importance of bringing non-monetary aspects of household welfare into the forefront of inequality analysis since measurements based solely on the distribution of income variables may misrepresent the degree of overall inequality in society. Agreement over the various approaches to the measurement of multidimensional inequality entails, however, non-trivial decisions that may limit the practical usefulness of these measures. We suggest that the use of multidimensional inequality ranges and restrictive dominance criteria may open significant scope for further developments in the empirical analysis of multidimensional inequality.
Automatic verification of textbook programs that usecomprehensions

Textbooks on program verification make use of simple programs in mathematical domains as illustrative examples. Mechanical verification tools can give students a quicker way to learn, because the feedback cycle can be reduced from days (waiting for hand-proofs to be graded by the teaching assistant) to seconds or minutes (waiting for the tool’s output). However, the mathematical domains that are so familiar to students (for example, sum-comprehensions) are not directly supported by first-order SMT solvers. This paper presents a technique for translating common comprehension expressions (sum, count , product , min, and max) into verification conditions that can be tackled by two first-order SMT solvers. The technique has been implemented in the Spec# program verifier. The paper also reports on the experience of using Spec# to verify several challenging programming examples drawn from a textbook by Dijkstra and Feijen.