Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Study of Method In-lining for a Java Just-in-Time Compiler

Method inlining is one of the optimizations that have a significant impact on both system performance and total compilation overhead in a dynamic compilation system. This paper describes an empirical study of onlineprofile-directed method inlining for obtaining both performance benefits and compilation time reductions in our dynamic compilation system. We rely solely on the profile information in deciding which methods should be inlined along which call paths, without employing the existing static heuristics, except that methods with extremely small bodies are always inlined. The call site distribution and invocation frequency are collected using dynamically generated instrumentation code, and are given to the recompilation controller for analyzing and organizing the inlining requests. The experimental results show that the strategy of relying on the profile information in method inlining decision is quite effective for reducing compilation overhead and/or for improving the performance. It can also provide flexibility for inlining decisions based on the type of profile, such as spiky or flat profiles. The results show that the strategy can be the basis of automatic optimization selection for a future efficient dynamic compilation framework.
Advancing Theory by Assessing Boundary Conditions With Metaregression: A Critical Review and Best-Practice Recommendations

Understanding boundary conditions, or situations when relations between variables change depending on values of other variables, is critical for theory advancement and for providing guidance for practice. Metaregression is ideally suited to investigate boundary conditions because it provides information on the presence and strength of such conditions. In spite of its potential, results of our review of 63 metaregression articles published in the Journal of Management, Journal of Applied Psychology, Personnel Psychology, Journal of Management, Academy of Management Journal, and Strategic Management Journal uncovered a surprising lack of transparency, frequently implemented erroneous practices, and a lack of attention to important methodological choices. Results also suggest that many substantive conclusions are ambiguous at best and, unbeknownst to authors and readers, potentially misleading. Drawing from our review of the substantive literature as well as the latest statistical and methodological research, we offer evidence-based best-practice recommendations on how to conduct and report the results of a metaregression study. We offer recommendations on calculating statistical power and heterogeneity, choosing an appropriate model, testing boundary condition hypotheses, adjusting R2 for known variance, explaining methodological choices, and reporting and interpreting model coefficients and other results. Also, we conducted two illustrative metaregression studies that incorporate all of our recommendations with accompanying syntax and data. Our recommendations can be used by authors, readers, journal editors, and reviewers wishing to conduct and evaluate metaregression studies, as well as practitioners interested in understanding conditions under which organizational practices are more or less likely to be effective.
A regularization framework for multiple-instance learning

This paper focuses on kernel methods for multi-instance learning. Existing methods require the prediction of the bag to be identical to the maximum of those of its individual instances. However, this is too restrictive as only the sign is important in classification. In this paper, we provide a more complete regularization framework for MI learning by allowing the use of different loss functions between the outputs of a bag and its associated instances. This is especially important as we generalize this for multi-instance regression. Moreover, both bag and instance information can now be directly used in the optimization. Instead of using heuristics to solve the resultant non-linear optimization problem, we use the constrained concave-convex procedure which has well-studied convergence properties. Experiments on both classification and regression data sets show that the proposed method leads to improved performance.
A Review of Clustering Techniques Based on Machine learning Approach in Intrusion Detection Systems

False alarm rate and detection accuracy are still challenging issues that are not completely solved yet in the field of Anomaly based Intrusion Detection System (AIDS). The reasons behind these issues vary according to the algorithm and the dataset used to train the IDS. Consequently, dealing with high dimensional data requires an efficient data reduction technique that considerably reduces the dimensionality without any substantial loss in the important features. However, the excessive reduction of features will lead to model some intrusive patterns similarly as normal ones. Indeed, this will result in misclassifications that will increase false negative rate, which degrades the accuracy of detection. This paper concludes many clustering techniques that were previously proposed to solve the inherent IDS problems. Where, the clustering techniques involved in three general aspects namely: data preprocessing, anomaly detection, and data projection/alarm filtering. Eventually, recommendations for future researches followed by the conclusion are depicted at the end of this paper.
Routing and Address Assignment Using Lane/Position Information in a Vehicular Ad Hoc Network

Connection stability is important in vehicle-to-vehicle communication for applications that exchange messages for collision warning, cooperative driving, and other high-urgency purposes. Many of the existing communication protocols for mobile ad hoc networks assume the use of, portable gaming players, PDAs and other general-purpose devices, thus reducing the reliability of connection stability and communication. Therefore, greater efficiency can be expected by specializing systems for vehicle-to-vehicle communication using position information. To improve connection stability, we propose a method for constructing network groups according to lane position and evaluate the proposed method by simulation. We also describe a method of automatic assignment of IP addresses using IPv6 and a combination of network addresses defined for each lane and a specific host address for each vehicle.
Coding, diversity and ARQ in fading channels: A case-study performance comparison

Cross-layer error protection strategies couple systematic error correction at the physical layer with adaptive data retransmission at the data-link layer, to mitigate the effects of fast-fading propagation channels. This work, in the form of a case-study, aims to introduce useful tools for the assessment and analysis of the energetic performance of such strategies. In particular, it is shown that in energy-constrained applications, such as wireless sensor networks, the design guidelines should be based on a precise definition of the energy per bit. This definition also allows to evaluate the minimum energy that is necessary to transmit data in a given setting.
Satellite- and ground-based measurements of CO2 over the Indian region: its seasonal dependencies, spatial variability, and model estimates

ABSTRACT The present study demonstrates the distribution of carbon dioxide (CO2) concentration over the Indian region and the surrounding oceanic regions during 2009–2012, using measurements from satellites viz., Greenhouse Gases Observing Satellite (GOSAT) and Atmospheric Infrared Sounder, Carbon Tracker (CT) model simulations and flask measurements from two Indian stations Sinhagad (SNG) (73°45′ E, 18°21′36″ N) and Cape Rama (CRI) (73°54′ E, 15°6′ N). The concentration of CO2 is observed to be maximum during pre-monsoon and shows a decreasing phase during the post-monsoon season. In a regional scale, it is found that Indo-Gangetic Plain and northern India have relatively higher concentrations compared to the other regions. The probability distribution of the concentration differences shows that for most of the time, the differences lie between ±3 ppmv between GOSAT and CT. The comparison between the CO2 flask measurements over SNG and CRI with respect to that of GOSAT and CT clearly reveals that the differences in CO2 are as high as 10 ppmv between the ground- and satellite-based measurements. Further, we utilized the Lagrangian model FLEXible PARTicle (FLEXPART) to understand the source‒receptor relationship over CRI, SNG, and over the equatorial Indian Ocean (IO). The source contributions from the northern and eastern continental regions of the Indian region are found to be more influential over SNG compared to CRI. It is also found from simulations that the equatorial IO has less influence from the continental source and therefore has a reduced seasonal variability compared to the other regions considered in the present study.
Activation T Cells via Dendritic Cell + Effector CD8 Radiotherapy Promotes Tumor-Specific

Radiotherapy is an important treatment for cancer. The main mode of action is thought to be the irreversible damage to tumor cell DNA, but there is evidence that irradiation mobilizes tumor-speciﬁc immunity, and recent studies showed that the efﬁcacy of high-dose radiotherapy depends on the presence of CD8 + T cells. We show in this study that the efﬁcacy of radiotherapy given as a single, high dose (10 Gy) crucially depends on dendritic cells and CD8 + T cells, whereas CD4 + T cells or macrophages are dispensable. We show that local high-dose irradiation results in activation of tumor-associated dendritic cells that in turn support tumor-speciﬁc effector CD8 + T cells, thus identifying the mechanism that underlies radiotherapy-induced mobilization of tumor-speciﬁc immunity. We propose that in the absence of irradiation, the activation status of dendritic cells rather than the amount of tumor-derived Ag is the bottleneck, which precludes efﬁcient anti-tumor immunity. The Journal of Immunology , 2012, 189: 000– 000. previous ﬁndings, we found that the therapeutic efﬁcacy of high-dose radiotherapy depends on the presence of DCs and CD8 + T cells, but not CD4 + T cells or macrophages, during or immediately after radiotherapy. Our results identify the activation of tumor-associated DCs as the mechanism through which local high-dose radiotherapy supports the function of local, tumor-speciﬁc CD8 + effector T cells. On the basis of our ﬁndings, we propose that the maturation status of tumor-associated DC— and not the amount of tumor-derived Ag that is presented or the availability of tumor-speciﬁc CD8 + T cells—is the bottleneck for immunological control of tumors.
ChimerDB 3.0: an enhanced database for fusion genes from cancer transcriptome and literature data mining

Fusion gene is an important class of therapeutic targets and prognostic markers in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data and manual curations. In this update, the database coverage was enhanced considerably by adding two new modules of The Cancer Genome Atlas (TCGA) RNA-Seq analysis and PubMed abstract mining. ChimerDB 3.0 is composed of three modules of ChimerKB, ChimerPub and ChimerSeq. ChimerKB represents a knowledgebase including 1066 fusion genes with manual curation that were compiled from public resources of fusion genes with experimental evidences. ChimerPub includes 2767 fusion genes obtained from text mining of PubMed abstracts. ChimerSeq module is designed to archive the fusion candidates from deep sequencing data. Importantly, we have analyzed RNA-Seq data of the TCGA project covering 4569 patients in 23 cancer types using two reliable programs of FusionScan and TopHat-Fusion. The new user interface supports diverse search options and graphic representation of fusion gene structure. ChimerDB 3.0 is available at http://ercsb.ewha. ac.kr/fusiongene/.
When moral concerns become a psychological disorder: the case of Obsessive-Compulsive Disorder

Author: Doron, Guy; Sar-El, Dar; Mikulincer, Mario; and Kyrios, Mike Title: When moral concerns become a psychological disorder: the case of Obsessive-Compulsive Disorder Editor: Mario Mikulincer and Phillip R. Shaver Book title: Social psychology of morality: exploring the causes of good and evil Place published: Washington, DC Publisher: American Psychological Association Year: 2011 Pages: 293-310 URL: http://www.apa. org/pubs/books/4318093.aspx
A new heuristic for rectilinear Steiner trees

The minimum rectilinear Steiner tree (RST) problem is one of the fundamental problems in the field of electronic design automation. The problem is NP-hard, and much work has been devoted to designing good heuristics and approximation algorithms; to date, the champion in solution quality among RST heuristics is the Batched Iterated 1-Steiner (BI1S) heuristic of Kahng and Robins (1992). In a recent development, exact RST algorithms have witnessed spectacular progress: the new release of the GeoSteiner code of Warme, Winter, and Zachariasen (1998) has average running time comparable to that of the fastest available Bl1S implementation, due to Robins. We are thus faced with the paradoxical situation that an exact algorithm for an NP-hard problem is competitive in speed with a state-of-the-art heuristic for the problem. The main contribution of this paper is a new RST heuristic, which has at its core a recent 3/2 approximation algorithm of Rajagopalan and Vazirani (1999) for the metric Steiner tree problem on quasi-bipartite graphs-these are graphs that do not contain edges connecting pairs of Steiner vertices. The RV algorithm is built around the linear programming relaxation of a sophisticated integer program formulation, called the bidirected cut relaxation. Our heuristic achieves a good running time by combining an efficient implementation of the RV algorithm with simple, but powerful geometric reductions. Experiments conducted on both random and real VLSI instances show that the new RST heuristic runs significantly faster than Robins' implementation of BI1S and than the GeoSteiner code. Moreover, the new heuristic typically gives higher-quality solutions than Bl1S.
Comparative Analysis of Portuguese Named Entities Recognition Tools

This paper describes an experiment to compare four tools to recognize named entities in Portuguese texts. The experiment was made over the HAREM corpora, a golden standard for named entities recognition in Portuguese. The tools experimented are based on natural language processing techniques and also machine learning. Specifically, one of the tools is based on Conditional random fields, an unsupervised machine learning model that has being used to named entities recognition in several languages, while the other tools follow more traditional natural language approaches. The comparison results indicate advantages for different tools according to the different classes of named entities. Despite of such balance among tools, we conclude pointing out foreseeable advantages to the machine learning based tool.
Mixture Model-based Minimum Bayes Risk Decoding using Multiple Machine Translation Systems

We present Mixture Model-based Minimum Bayes Risk (MMMBR) decoding, an approach that makes use of multiple SMT systems to improve translation accuracy. Unlike existing MBR decoding methods defined on the basis of single SMT systems, an MMMBR decoder reranks translation outputs in the combined search space of multiple systems using the MBR decision rule and a mixture distribution of component SMT models for translation hypotheses. MMMBR decoding is a general method that is independent of specific SMT models and can be applied to various commonly used search spaces. Experimental results on the NIST Chinese-to-English MT evaluation tasks show that our approach brings significant improvements to single system-based MBR decoding and outperforms a state-of-the-art system combination method.
Ecological models of human performance based on affordance, emotion and intuition

This paper proposes a complementary approach to Rasmussen's taxonomy of the human skill-, rule-, and knowledge-based performance models by combining the ecological concept of affordances with the neural concepts of human emotion and intuition. The classical cognitive engineering framework is extended through the neuro-ecological approach, including personal human attributes important in exercising control over the work environment. The proposed affordance-, emotion-, and intuition-based models correspond to the three types of human performance, namely: learning, adaptive and tuning control, respectively. The new framework is not a predictive model of the operator behaviour, but rather it describes the processes of neuro-ecological control of the human environment.
PPreDeConStream: A Parallel Version of PreDeConStream Algorithm

Clustering is one of the major techniques in data mining. Clustering data streams have drawn attentions in the past few years because of their ever-growing presence. Data streams add more challenges to clustering such as limited time, limited memory and one pass clustering. Further, discovering clusters with arbitrary shapes is important in data stream applications. Now a few clustering techniques for data streams exist in multidimensional spaces and the technique of "clustering projected or subspace" is used. Therefore, the task of projected clustering (or subspace clustering) has to be defined. PreDeConStream is a density-based data stream clustering algorithm for clustering high-dimensional data streams. In this paper, PPreDeConStream is present as a parallel version of PreDeConStream algorithm in the shared memory model. The theoretical and experimental results show that PPreDeConStream offers nearly linear speedup while keeps other advantages of PreDeConStream.
Digital right management based on Grid Computing architecture (GC-DRM)

In this paper, we proposed a load balancing digital right management platform based on grid computing (GC-DRM) and compliance of grid portal standards by using porlet. The platform integrates Globus Toolkit 4 and Condor 6.9. 2 and uses Web 2.0 to construct the Web-based user interface for providing job submission, control, management, monitor and DRM services. It provides six categories of services, including watermark embedding and extraction, image scrambling, visible watermark embedding, image tamper-detection and recovery, watermark algorithms attack testing, user customized tasks. It can dispatch tasks to remote computers and return results to complete the mission. Therefore, GC-DRM provides a user friendly environment with efficient DRM service performance based on grid computing architecture.
A study of presolar material in hydrated lithic clasts from metal‐rich carbonaceous chondrites

We report on the investigation of presolar grain inventories of hydrated lithic clasts in three metal‐rich carbonaceous chondrites from the CR clan, Acfer 182 (CH3), Isheyevo (CH3/CBb3), and Lewis Cliff (LEW) 85332 (C3‐un), as well as the carbon‐ and nitrogen‐isotopic compositions of the fine‐grained clast material. Eleven presolar silicate grains as well as nine presolar silicon carbide (SiC) grains were identified in the clasts. Presolar silicate abundances range from 4 to 22 parts per million (ppm), significantly lower than in pristine meteorites and interplanetary dust particles (IDP), and comparable to recent findings for CM2s and CR2 interchondrule matrix. SiC concentrations lie between 9 and 23 ppm, and are comparable to the values for CI, CM, and CR chondrites. The results of our investigation suggest similar alteration pathways for the clast material, the interchondrule matrix of the CR2 chondrites, and the fine‐grained fraction of CM2 chondrites. Fine‐grained matter of all three meteorites contains moderate to high 15N‐enrichments (~50‰ ≤ δ15N ≤ ~1600‰) compared to the terrestrial value, indicating the presence of primitive organic material. We observed no correlation between 15N‐enrichments and presolar dust concentrations in the clasts. This is in contrast to the findings from a suite of primitive IDPs, which display in several cases enhanced bulk 15N/14N ratios and high presolar grain abundances of several hundred or even thousand ppm. The bulk 15N/14N ratios of the clasts are comparable to the range for primitive IDPs, suggesting a nitrogen carrier less susceptible to destruction by aqueous alteration than silicate stardust.
A Multichip Pulse-Based Neuromorphic Infrastructure and Its Application to a Model of Orientation Selectivity

The growing interest in pulse-mode processing by neural networks is encouraging the development of hardware implementations of massively parallel networks of integrate-and-fire neurons distributed over multiple chips. Address-event representation (AER) has long been considered a convenient transmission protocol for spike based neuromorphic devices. One missing, long-needed feature of AER-based systems is the ability to acquire data from complex neuromorphic systems and to stimulate them using suitable data. We have implemented a general-purpose solution in the form of a peripheral component interconnect (PCI) board (the PCI-AER board) supported by software. We describe the main characteristics of the PCI-AER board, and of the related supporting software. To show the functionality of the PCI-AER infrastructure we demonstrate a reconfigurable multichip neuromorphic system for feature selectivity which models orientation tuning properties of cortical neurons
Energy Homeostasis and Body Weight before and after Cessation of Block and Replacement Therapy in Euthyroid Patients with Graves' Disease

Patients with Graves' hyperthyroidism (GH) treated with a combination of thyrostatic drugs and T4, that is, block and replacement therapy (BRT), often report body weight (BW) gain. We aimed to determine changes in BW and energy metabolism upon cessation of BRT in these patients, and to identify possible endocrine determinants. We analysed 22 patients with GH (i) during BRT, and (ii) 12 weeks after BRT cessation. Patients were euthyroid at both visits. There were no differences in BW or resting energy expenditure (REE) between visits. At visit 1, after 13.5 (9.5–48.0) months of BRT, serum free (F)T4 correlated positively with REE (r = 0.433, P = 0.044) and negatively with body fat % (r = -0.450, P = 0.035). Plasma FT3 and FT3/FT4 ratio showed an increase 12 w after cessation of BRT (20%, P < 0.0001 and 16%, P = 0.007, resp.). Moreover, the relative change in FT3/FT4 ratio showed a significant, positive correlation with the relative change in REE between the 2 visits (r = 0.465, P = 0.029). In conclusion, serum FT4 determines REE in euthyroid patients with GH treated with BRT. Twelve weeks after BRT cessation, BW and energy homeostasis are unaltered. However, as serum FT3/FT4 ratio increases after cessation of BRT, which is a positive determinant of changes in REE, a longer term BW decrease is likely to occur.