Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable concurrent and parallel mark

Parallel marking algorithms use multiple threads to walk through the object heap graph and mark each reachable object as live. Parallel marker threads mark an object "live" by atomically setting a bit in a mark-bitmap or a bit in the object header. Most of these parallel algorithms strive to improve the marking throughput by using work-stealing algorithms for load-balancing and to ensure that all participating threads are kept busy. A purely "processor-centric" load-balancing approach in conjunction with a need to atomically set the mark bit, results in significant contention during parallel marking. This limits the scalability and throughput of parallel marking algorithms. We describe a new non-blocking and lock-free, work-sharing algorithm, the primary goal being to reduce contention during atomic updates of the mark-bitmap by parallel task-threads. Our work-sharing mechanism uses the address of a word in the mark-bitmap as the key to stripe work among parallel task-threads, with only a subset of the task-threads working on each stripe. This filters out most of the contention during parallel marking with 20% improvements in performance. In case of concurrent and on-the-fly collector algorithms, mutator threads also generate marking-work for the marking task-threads. In these schemes, mutator threads are also provided with thread-local marking stacks where they collect references to potentially "gray" objects, i.e., objects that haven't been "marked-through" by the collector. We note that since this work is generated by mutators when they reference these objects, there is a high likelihood that these objects continue to be present in the processor cache. We describe and evaluate a scheme to distribute mutator generated marking work among the collector's task-threads that is cognizant of the processor and cache topology. We prototype both our algorithms within the C4 [28] collector that ships as part of an industrial strength JVM for the Linux-X86 platform.
Complexity and expressive power of logic programming

This paper surveys various complexity results on different forms of logic programming. The main focus is on decidable forms of logic programming, in particular propositional logic programming and datalog, but we also mention general logic programming with function symbols. Next to classical results on plain logic programming (pure Horn clause programs), more recent results on various important extensions of logic programming are surveyed. These include logic programming with different forms of negation, disjunctive logic programming, logic programming with equality, and constraint logic programming. The complexity of the unification problem is also addressed.
Defective Viral Genomes: Critical Danger Signals of Viral Infections

ABSTRACT Viruses efficiently block the host antiviral response in order to replicate and spread before host intervention. The mechanism initiating antiviral immunity during stealth viral replication is unknown, but recent data demonstrate that defective viral genomes generated at peak virus replication are critical for this process in vivo. This article summarizes the supporting evidence and highlights gaps in our understanding of the mechanisms and impact of immunostimulatory defective viral genomes generated during natural infections.
Economic analysis of biogas production from small scale anaerobic digestion systems for cattle manure

One of the major concerns of biogas plants can’t be operated economical and environmental sustainable manner. In order to produce biogas in rural areas, using anaerobic systems with high organic content of farm wastes can be able to remove these concerns. The production of biogas can then be used for cooking, lighting and heating. Biogas is a good alternative to kitchen gas cylinder. However, there is a lack of knowledge and experience available on design methods for these systems. Anaerobic digestion is generally economically applicable waste treatment systems for large farms (>100 cattle). However, majority of Turkey farms have less than 100 cattle, making this technology economically inaccessible to the vast majority farms. The objective of this study is to determine the economic viability of small scale anaerobic digester systems taking into account on domestic production conditions.
Fixation stability of comminuted humeral shaft fractures: locked intramedullary nailing versus plate fixation.

BACKGROUND This study compared the fixation stability of two treatments for humeral shaft fractures with segmental bone loss during cyclic, physiologic loading. METHODS Six matched pairs of human humeri received either a 10-hole broad dynamic compression plate or a locked antegrade inserted humeral nail applied to a humeral diaphyseal osteotomy with a 1.5-cm gap defect. The bone-implant humeral constructs were axially loaded for 10,000 cycles at 250 N and 500 N, with measurements of gap displacement and calculation of construct stiffness. The specimens were then loaded to failure. RESULTS Cyclic loading showed no difference between the two groups for average gap displacement or construct stiffness. The intramedullary nail constructs failed by humeral shaft splitting (n = 4) or head cut-out (n = 2) at an average of 958.3 N, whereas the plate constructs failed by humeral shaft splitting and screw pull-out (n = 3) or plate bending (n = 3) at an average of 641.7 N (p < 0.001). CONCLUSION Although both methods offer similar fixation stability under physiologic loads, the higher load to failure demonstrated by intramedullary nail fixation may have implications for the patient with multiple injuries for whom partial weightbearing on the injured upper extremity may be necessary.
Computation Emerges from Adaptive Synchronization of Networking Neurons

The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress) of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain.
Molecular mechanisms of cortical differentiation

During development, several populations of progenitor cells in the dorsal telencephalon generate a large variety of neurons which acquire distinct morphologies and physiological properties and serve distinct functions in the mammalian cortex. This paper reviews recent work that has identified (i) key molecules involved in the specification and differentiation of cortical neurons, (ii) novel genes which distinguish distinct subsets of cortical progenitors and may be involved in the diversification of cortical neurons present in different cortical layers, and (iii) mechanisms involved in the generation of different projection neuronal subtypes in the well‐studied model of layer 5 of the rodent cortex.
A Data Management Perspective on Vehicular Networks

The interest of intelligent transportation systems and vehicular ad hoc networks has increased in recent years. As a fundamental building block for the development of applications for vehicular networks, new techniques are needed to handle data appropriately in the vehicles. In this paper, we present a comprehensive overview of data management for vehicular networks, where the vehicle-to-vehicle communications play a key role. We describe the technological context of vehicular networks along with the different types of data managed in that environment, and we analyze several challenges, such as the evaluation of the relevance of data regarding the occurrence of events on the roads (e.g., accidents), the design of appropriate (effective and efficient) content-based data dissemination protocols, the competition in the access to physical resources (e.g., parking spaces), the development of suitable data aggregation techniques specifically adapted to the context of vehicular networks, and query processing. This paper provides an in-depth coverage of data management for vehicular networks but keeps, at the same time, a didactic orientation. Supported by an extensive collection of relevant references, we analyze the state of the art, identify some must-read references, outline research problems, and extract conclusions and lessons learned.
Run-time Management Policies for Data Intensive Web Sites

Web developers have been concerned with the issues of Web latency and Web data consistency for many years. These issues have become more important in our days since the accurate and imminent dissemination of information is vital to businesses and individuals that rely on the Web. In this paper, we evaluate different run-time management policies against real Web site data. We first define the meaning of data intensive Web sites and categorize them according to their hit patterns. Our research relies on real world Web data collected from various popular Web sites and proxy log files. We propose a Web site run-time management policy that may apply to various real Web site hit patterns and Web data update frequencies.
Adaptive Model Predictive Control of tokamak plasma unstable vertical position

The control system of a plasma unstable vertical position in the tokamak T-15 on the base of a Model Predictive Control (MPC) in discrete time for stationary and non-stationary plant models was synthesized and simulated. A comparison with a system containing a PID controller is presented. The predictive control showed better step response. The adaptive state observer was designed to estimate the unknown plant varying parameter on line which was used in the MPC algorithm to adapt it to the plant model.
Numbers Don't Lie, but Do They Tell the Whole Story?

Newly appointed Secretary of the U.S. Department of Health and Human Services, Kathleen Sebelius, recently reaffirmed that reducing social disparities in diabetes is a national priority (1). Lower educational attainment has been strongly linked to higher diabetes prevalence and worse diabetes control. In California, for example, diabetes is much more common among those with a 9th grade education or less (14%) compared with those with a college degree or higher (5%). Nationally, very large disparities in diabetes-related mortality also exist across education levels, with rates three- to fivefold higher among those with less than a high school degree versus a college degree. Educational disparities in both diabetes control (as measured by A1C) and mortality have widened over the last 20 years (2). Similarly, many racial and ethnic minority groups have higher diabetes prevalence and worse diabetes control. African Americans are much more likely than non-Hispanic white to have diabetes and to experience worse glycemic control, even in populations with similar access to care (3). Insofar as African Americans are less likely than non-Hispanic white to have received the benefits of higher education, understanding whether lower educational attainment is an explanatory factor for racial and ethnic disparities in diabetes is critical for practitioners and policy makers in public health and clinical medicine. It is in that spirit that Osborn et al. (4) have asked the question, “Can an assessment of performance strongly related to educational attainment (numeracy) help explain observed racial disparities in disease control, in a disease (diabetes) whose management demands a certain facility with numbers? ” Prior research in a sample of ethnically diverse, …
Society, Environment and Human Security in the Arctic Barents Region

The Arctic-Barents Region is facing numerous pressures from a variety of sources, including the effect of environmental changes and extractive industrial developments. The threats arising out of these pressures result in human security challenges. This book analyses the formation, and promotion, of societal security within the context of the Arctic-Barents Region. It applies the human security framework, which has increasingly gained currency at the UN level since 1994 (UNDP), as a tool to provide answers to many questions that face the Barents population today. The study explores human security dimensions such as environmental security, economic security, health, food, water, energy, communities, political security and digital security in order to assess the current challenges that the Barents population experiences today or may encounter in the future. In doing so, the book develops a comprehensive analysis of vulnerabilities, challenges and needs in the Barents Region and provides recommendations for new strategies to tackle insecurity and improve the wellbeing of both indigenous and local communities. This book will be a valuable tool for academics, policy-makers and students interested in environmental and human security, sustainable development, environmental studies and the Arctic and Barents Region in particular.
Service-Oriented Multi-tenancy (SO-MT): Enabling Multi-tenancy for Existing Service Composition Engines with Docker

In Web Services-based SOAs, BPEL 2.0 is the choice for defining services by composing existing ones. BPEL-based services can be directly executed on BPEL engines. With the rise of the cloud, companies aim to outsource service hosting to cloud providers. To achieve economies of scale, a cloud provider must host such services with minimal resources and keep the services from different tenants isolated. But BPEL engines typically do not support multi-tenancy. As a result, a vendor either has to extend the engine himself or host every service on its own (virtual) machine, thereby using either costly development or computing resources. Our approach solves this problem by hosting each service in its own isolated and minimal container which exposes the management functionality through APIs enabling complex multi-tenancy features or horizontal scaling on top. We aim to implement this by a) leveraging Docker container to isolate the services from each other, b) removing unused software from the container, c) starting the BPEL engine with minimal resources, and d) extracting management functionality while keeping management APIs. Our case study with Apache ODE shows that we can save significant runtime resources with only minor development effort. While this approach helps in moving SOAs into the cloud, it can also be leveraged to build resource efficient BPEL-based microservices cheaper.
A new fluid factor and its application using a deep learning approach

Amplitude interpretation for hydrocarbon prediction is an important task in the oil and gas industry. Seismic amplitude is dominated by porosity, the volume of clay, pore‐filled fluid type and lithology. A few seismic attributes are proposed to predict the existence of hydrocarbon. This paper proposes a new fluid factor by adding a correct item based on the J attribute. The algorithm is verified through stochastic Monte Carlo modelling that contains various rock physical properties of sand and shale. Both gas and oil responses are separated by the new fluid factor. Furthermore, an approach based on the neural network model is trained using the deep learning method to predict the new fluid factor. The confusion matrix shows that this model performs well. This model allows the application of the new fluid factor in the seismic data. In this study, the Marmousi II data set is used to examine the performance of the new fluid factor, and the result is good. Most hydrocarbon reservoirs are identified in the shale–sandstone sequences. The combination of deep learning and the new fluid factor provides a more accurate way for hydrocarbon prediction.
The International Association for the Study of Lung Cancer Lung Cancer Staging Project: Proposals Regarding the Clinical Staging of Small Cell Lung Cancer in the Forthcoming (Seventh) Edition of the Tumor, Node, Metastasis Classification for Lung Cancer

Background: Small cell lung cancer (SCLC) is usually classified using the limited and extensive definition. The tumor, node, metastasis (TNM) classification should also be applicable to SCLC, but it has only been reported in small surgical series. The current analysis looks to the impact of the TNM system on the clinical staging of SCLC and of the new International Association for the study of Lung Cancer (IASLC) proposals. Methods: Using the IASLC database, survival analyses were performed for clinically staged patients. Prognostic groups were compared, and the new IASLC TNM proposals were applied to this population and to the Surveillance, Epidemiology, and End Results (SEER) database. Results: The IASLC database contained 12,620 eligible cases of small cell histology. TNM staging was available for 8088 patients. Survival was directly correlated to both T and N category. Differences were more pronounced in patients without mediastinal or supraclavicular nodal involvement. Stage grouping using the sixth edition of TNM also differentiates survival except between IA and IB. Patients with pleural effusion regardless of the cytology have an intermediate prognosis between limited and extensive disease. The IASLC proposals for the seventh edition of the TNM classification also apply to this series of SCLC and to the SEER database. Conclusion: TNM staging is recommended for SCLC, and stratification by stage I-III should be incorporated in clinical trials of early-stage disease. Further studies are needed to clarify the impact of pleural effusion and the extent of N3 disease.
Approximate Steepest Coordinate Descent

We propose a new selection rule for the coordinate selection in coordinate descent methods for huge-scale optimization. The efficiency of this novel scheme is provably better than the efficiency of uniformly random selection, and can reach the efficiency of steepest coordinate descent (SCD), enabling an acceleration of a factor of up to $n$, the number of coordinates. In many practical applications, our scheme can be implemented at no extra cost and computational efficiency very close to the faster uniform selection. Numerical experiments with Lasso and Ridge regression show promising improvements, in line with our theoretical guarantees.
The Effects of Inverse Ratio Ventilation with PEEP on Respiratory Function and Inflammatory Cytokines in Patients during One-lung Ventilation

Background: Hypoxemia is the most common complication during one-lung ventilation (OLV). Inverse ratio ventilation (IRV) may lead to intrinsic positive end-expiratory pressure (PEEP) and improve oxygenation in acute respiratory distress syndrome, so we investigated whether volume-controlled IRV with external PEEP could improve hypoxemia, reduce the risk of acute lung injury during OLV. Methods: Sixty patients undergoing one-lung ventilation for open thoracoctomy were randomly divided into IRV group and control group (n = 30). All patients were initially ventilated with a tidal volume of 8 mL/kg, an inspiratory: expiratory (I:E) ratio of 1:2 and a respiratory rate of 12 breaths/min in 100% oxygen without PEEP. During OLV, lungs were ventilated either with I:E of 2:1 (IRV group) or 1:2 (control group) with an actual tidal volume (VT) 7 mL/kg, respiratory rate 12 breaths/min, external PEEP of 5 cm H2O. Arterial blood was collected respectively to analyze blood gas before and during OLV. Meanwhile, hemodynamic and respiratory mechanics were monitored. The concentrations of IL-1β, IL-6 and IL-8 in bronchoalveolar lavage fluid (BALF) were measured before and during OLV. Results: Compared to the control group, partial pressure of arterial oxygen (PaO2 ), mean airway pressure and dynamic compliance (CL) were significantly higher in IRV group during OLV (P < 0.05). However, plateau pressure (Pplat) and levels of IL-1β, IL-6 and IL-8 in BALF were lower in IRV group than those in control group (P < 0.05). Conclusion: IRV (I:E = 2:1) applying PEEP could improve hypoxemia, promote oxygenation, and improve dynamic compliance of respiratory system, moreover reduce Pplat and the release of inflammatory cytokines in patients during one-lung ventilation. It is superior to conventional ventilation with PEEP during one-lung ventilation.
Interference Cancellation Based DFT-Precoded CDMA for Hybrid OFCDMA Multiple Access

A novel hybrid OFDMA/CDMA multiple access system for uplink communications in evolving 4G wireless systems is developed. Specifically, a DFT-precoded CDMA technique is proposed for the CDMA part of the multiple access. We elaborate on different configurations of precoded CDMA operation, and develop advanced receiver techniques, using frequency-domain interference cancellation. Detailed simulation results showing the performance of this multiple access approach is presented. Finally, we describe how the proposed approach has been incorporated in the currently developing Ultra Mobile Broadband (UMB) system, which is an evolution from the cdma2000 evolution standards family.
Active Representations: A Primitive for Analogical Computing in the Brain

The static nature of representations (or symbols) makes them require an active interpretational (or computational) mechanism to render them useful or meaningful. To avoid the problems of infinite hierarchy of interpreters, more active approaches have been proposed. These are called active representations (Hofstadter 1985; Mitchell 2001) or active schemas (Narayanan 1999), and neurons can perform analogical or metaphorical tasks. Surprisingly, such active units are very much alike the neurons in our brains, and they can indeed perform analogical tasks. In this paper, a detailed neural mechanism that may be implementing such a function is proposed, and the implications of this new connection between analogy and the neural substrate in building intelligent agents and in understanding the brain function will be discussed.
Incremental Construction of Structured Hidden Markov Models

This paper presents an algorithm for inferring a Structured Hidden Markov Model (S-HMM) from a set of sequences. The S-HMMs are a subclass of the Hierarchical Hidden Markov Models and are well suited to problems of process/user profiling. The learning algorithm is unsupervised, and follows a mixed bottom-up/top-down strategy, in which elementary facts in the sequences (motifs) are progressively grouped, thus building up the abstraction hierarchy of a S-HMM, layer after layer. The algorithm is validated on a suite of artificial datasets, where the challenge for the learning algorithm is to reconstruct the model that generated the data. Then, an application to a real problem of molecular biology is briefly described.