Miracle-dz/UniFAR-FTUs huggingface dataset:
The Transactional Conflict Problem

The transactional conflict problem arises in transactional systems whenever two or more concurrent transactions clash on a data item. While the standard solution to such conflicts is to immediately abort one of the transactions, some practical systems consider the alternative of delaying conflict resolution for a short interval, which may allow one of the transactions to commit. The challenge in the transactional conflict problem is to choose the optimal length of this delay interval so as to minimize the overall running time penalty for the conflicting transactions. In this paper, we propose a family of optimal online algorithms for the transactional conflict problem. Specifically, we consider variants of this problem which arise in different implementations of transactional systems, namely "requestor wins'' and "requestor aborts'' implementations: in the former, the recipient of a coherence request is aborted, whereas in the latter, it is the requestor which has to abort. Both strategies are implemented by real systems. We show that the requestor aborts case can be reduced to a classic instance of the ski rental problem, while the requestor wins case leads to a new version of this classical problem, for which we derive optimal deterministic and randomized algorithms. Moreover, we prove that, under a simplified adversarial model, our algorithms are constant-competitive with the offline optimum in terms of throughput. We validate our algorithmic results empirically through a hardware simulation of hardware transactional memory (HTM), showing that our algorithms can lead to non-trivial performance improvements for classic concurrent data structures.
Perldoop2: A Big Data-Oriented Source-to-Source Perl-Java Compiler

Perl is one of the most important programming languages in many research areas. However, the most relevant Big Data frameworks, Apache Hadoop, Apache Spark and Apache Storm, do not support natively this language. To take advantage of these Big Data engines Perl programmers should port their applications to Java or Scala, which requires a huge effort, or use utilities as Hadoop Streaming with the corresponding degradation in the performance. For this reason we introduce Perldoop2, a Big Data-oriented Perl-Java source-to-source compiler. The compiler is able to generate Java code from Perl applications for sequential execution, but also for running on clusters taking advantage of Hadoop, Spark and Storm engines. Perl programmers only need to tag the source code in order to use the compiler. Experimental results demonstrate the benefits of Perldoop2 in terms of ease of use, performance and scalability.
Smartphone based lifelog with meaningful place detection

With the advancement of electronic techniques, current smartphones can record location information with the help of WIFI signal, base station signal and Global Positioning System (GPS) receivers embedded in them. A smartphone can record where we go and where we stay. However, all the locations are recorded using latitude and longitude, which makes it hard for users to have an intuitive feelings of these data. In this paper, we propose a way to find places where users stay for a while to perform some activities with the location data tracked by smartphone, and use a cascade classifier to infer users' different activities in different places. With places and related activities, we can build a lifelog for smartphone users.
Towards a Real Time Framework for Monitoring IoT Devices for Attack Detection: Vision Paper

As the Internet of Things (IoT) grows, the attacks to these devices continue to increase, both in number and impact. We identify the requirements for a real-time data processing framework that can be used to detect attacks while they occur, and propose an architecture that would implement these requirements. The proposed framework can be used by vendors, thirdparty organizations, and end-users to provide on-line monitoring and real-time attack mitigation. Finally, we identify the research challenges that we will face when implementing this architecture.
Sampled-Data Synchronization of Complex Networks With Partial Couplings and T–S Fuzzy Nodes

This paper is concerned with the synchronization of complex networks subject to partial couplings and Takagi–Sugeno (T-S) fuzzy nodes, by adopting the aperiodic sampled-data strategy. A decoupling method is utilized to handle the partial couplings among connected nodes, which enables us to investigate each channel of complex networks independently. On the basis of the input-delay approach, the hybrid system about each channel is reformulated to a continuous time-varying delay system. Then, the free-weighting matrix approach and a novel continuous Lyapunov functional are adopted to capture the information of sampling pattern. Sufficient conditions are obtained to ensure that the complex networks achieves synchronization with the target node. Furthermore, the proposed strategy is extended to more general case, in which there exist constant transmission time delays in the local interactions of connected nodes. Based on Wirtinger's inequality, a simplified and efficient synchronization strategy is proposed. Moreover, the corresponding optimization problem about the maximal sampling interval upper bound is addressed as well. Finally, the communication frequency reduction potential of the proposed synchronization strategy is well demonstrated via a numerical example.
Oral Ultramicronized Palmitoylethanolamide: Plasma and Tissue Levels and Spinal Anti-hyperalgesic Effect

Palmitoylethanolamide (PEA) is a pleiotropic lipid mediator with established anti-inflammatory and anti-hyperalgesic activity. Ultramicronized PEA (PEA-um) has superior oral efficacy compared to naïve (non-micronized) PEA. The aim of the present study was two-fold: (1) to evaluate whether oral PEA-um has greater absorbability compared to naïve PEA, and its ability to reach peripheral and central tissues under healthy and local inflammatory conditions (carrageenan paw edema); (2) to better characterize the molecular pathways involved in PEA-um action, particularly at the spinal level. Rats were dosed with 30 mg/kg of [13C]4-PEA-um or naïve [13C]4-PEA by oral gavage, and [13C]4-PEA levels quantified, as a function of time, by liquid chromatography/atmospheric pressure chemical ionization/mass spectrometry. Overall plasma levels were higher in both healthy and carrageenan-injected rats administered [13C]4-PEA-um as compared to those receiving naïve [13C]4-PEA, indicating the greater absorbability of PEA-um. Furthermore, carrageenan injection markedly favored an increase in levels of [13C]4-PEA in plasma, paw and spinal cord. Oral treatment of carrageenan-injected rats with PEA-um (10 mg/kg) confirmed beneficial peripheral effects on paw inflammation, thermal hyperalgesia and tissue damage. Notably, PEA-um down-regulated distinct spinal inflammatory and oxidative pathways. These last findings instruct on spinal mechanisms involved in the anti-hyperalgesic effect of PEA-um in inflammatory pain.
Treatment of nephrotic syndrome with adrenocorticotropic hormone (ACTH) gel

Purpose: A synthetic adrenocorticotropin (ACTH) analog has shown efficacy in Europe as primary and secondary therapy for nephrotic syndrome, but there is no published experience using the natural, highly purified ACTH gel formulation, available in the United States, for nephrotic syndrome. We therefore investigated the use of ACTH gel for nephrotic syndrome in the United States. Patients and methods: Twenty-one patients with nephrotic syndrome treated with ACTH gel outside of research settings in the United States, with initiation of therapy by December 31, 2009, allowing a minimum 6 months follow-up. We defined complete remission as stable renal function with proteinuria falling to <500 mg/day, and partial remission as stable renal function with >50% reduction in proteinuria from 500 to 3500 mg/day. Results: Twenty-one patients with nephrotic syndrome were treated: 11 with idiopathic membranous nephropathy (iMN), 4 with membranoproliferative glomerulonephritis (MPGN), 1 with focal segmental glomerulosclerosis (FSGS), 1 with minimal change disease (MCD), 1 with immunoglobulin A (IgA) nephropathy, 1 with class V systemic lupus erythematosus (SLE) glomerulonephritis, 1 with monoclonal diffuse proliferative glomerulonephritis, and 1 with unbiopsied nephrotic syndrome. ACTH was used as primary therapy for 3 patients; the remaining patients had previously failed a mean 2.3 immunosuppressive regimens. Eleven patients achieved a complete or partial remission, with 4 (19%) in complete remission. Of the 11 patients who achieved remission, 9 had iMN, 1 had FSGS, and 1 had IgA nephropathy. Of the 11 patients with iMN, 3 (27%) achieved complete remission and 6 (55%) achieved partial remission despite having previously failed a mean 2.4 therapies. Five patients reported steroid-like adverse effects, but there were no severe infections. The limitations were retrospective data analysis with short-term follow-up. Conclusion: ACTH gel may be a viable treatment option for resistant nephrotic syndrome due to membranous nephropathy. Short-term data suggest that remission rates may approach 80%.
Longitudinal, transcranial measurement of functional activation in the rat brain by diffuse correlation spectroscopy

Abstract. Neural activity is an important biomarker for the presence of neurodegenerative diseases, cerebrovascular alterations, and brain trauma; furthermore, it is a surrogate marker for treatment effects. These pathologies may occur and evolve in a long time-period, thus, noninvasive, transcutaneous techniques are necessary to allow a longitudinal follow-up. In the present work, we have customized noninvasive, transcutaneous, diffuse correlation spectroscopy (DCS) to localize changes in cerebral blood flow (CBF) induced by neural activity. We were able to detect changes in CBF in the somatosensory cortex by using a model of electrical forepaw stimulation in rats. The suitability of DCS measurements for longitudinal monitoring was demonstrated by performing multiple sessions with the same animals at different ages (from 6 to 18 months). In addition, functional DCS has been cross-validated by comparison with functional magnetic resonance imaging (fMRI) in the same animals in a subset of the time-points. The overall results obtained with transcutaneous DCS demonstrates that it can be utilized in longitudinal studies safely and reproducibly to locate changes in CBF induced by neural activity in the small animal brain.
Model-Plant Mismatch Compensation Using Reinforcement Learning

Learning-based approaches are suitable for the control of systems with unknown dynamics. However, learning from scratch involves many trials with exploratory actions until a good control policy is discovered. Real robots usually cannot withstand the exploratory actions and suffer damage. This problem can be circumvented by combining learning with a model-based control. In this letter, we employ a nominal model-predictive controller that is impeded by the presence of an unknown model-plant mismatch. To compensate for the mismatch, we propose two approaches of combining reinforcement learning with the nominal controller. The first approach learns a compensatory control action that minimizes the same performance measure as is minimized by the nominal controller. The second approach learns a compensatory signal from a difference of a transition predicted by the internal model and an actual transition. We compare the approaches on a robot attached to the ground and performing a setpoint reaching task in simulations. We implement the better approach on the real robot and demonstrate successful learning results.
OMICS as Therapeutic Platform: Environmental Factors to Parkinson's Disease

Environmental pollutants and toxicants take many forms as they can be chemical, biological and physical in nature. Urbanization and industrialization have led to increasingly high amounts of waste production and discharge into the environment. Everyday living places people in constant contact with environmental toxicants through contaminated mediums of air, water and land. The health implications suffered include much of which is encountered through air pollution. Respiratory ailments such as asthma allergies, and eye and nasal passage irritation are common symptoms of exposure. The immune, endocrine and reproductive systems are susceptible to harm as well. It has been documented that exposure to particulates are associated with morbidity and mortality risks from many diseases, chronic illnesses and even cancer [1]. Increasing lines of evidence support that neurodegenerative disease such as Parkinson disease (PD) may be associated with multiple etiological components including genetic factors (i.e., NFE2L2, SNCA, LRRK2, Parkin, PINK, DJ-1, and ATP13A2) [2-6], and environmental factors (i.e., smoking, aging, pesticide, herbicide, infections, or various environmental chemicals) [7-10].
Adaptive Performance Optimization under Power Constraint in Multi-thread Applications with Diverse Scalability

Energy consumption has become a core concern in computing systems. In this context, power capping is an approach that aims at ensuring that the power consumption of a system does not overcome a predefined threshold. Although various power capping techniques exist in the literature, they do not fit well the nature of multi-threaded workloads with shared data accesses and non-minimal thread-level concurrency. For these workloads, scalability may be limited by thread contention on hardware resources and/or data, to the point that performance may even decrease while increasing the thread-level parallelism, indicating scarce ability to exploit the actual computing power available in highly parallel hardware. In this paper, we consider the problem of maximizing the performance of multi-thread applications under a power cap by dynamically tuning the thread-level parallelism and the power state of CPU-cores in combination. Based on experimental observations, we design a technique that adaptively identifies, in linear time within a bi-dimensional space, the optimal parallelism and power state setting. We evaluated the proposed technique with different benchmark applications, and using different methods for synchronizing threads when accessing shared data, and we compared it with other state-of-the-art power capping techniques.
Real-Time Simultaneous 3D Reconstruction and Optical Flow Estimation

We present an alternative method for solving the motion stereo problem for two views in a variational framework. Instead of directly solving for the depth, we simultaneously estimate the optical flow and the 3D structure by minimizing a joint energy function consisting of an optical flow constraint and a 3D constraint. Compared to stereo methods, we impose the epipolar geometry as a soft constraint which gives the search space more flexibility instead of näývely following the epipolar lines, resulting in a correspondence that is more robust to small errors in pose estimation. This approach also allows us to use fast dense matching methods for handling large displacement as well as shape-based smoothness constraint on the 3D surface. We show in the results that, in terms of accuracy, our method outperforms the state-of-the-art method in two-frame variational depth estimation and comparable results to existing optical flow estimation methods. With our implementation, we are able to achieve real-time performance using modern GPUs.
Reliability-Aware Data Placement for Heterogeneous Memory Architecture

System reliability is a first-class concern as technology continues to shrink, resulting in increased vulnerability to traditional sources of errors such as single event upsets. By tracking access counts and the Architectural Vulnerability Factor (AVF), application data can be partitioned into groups based on how frequently it is accessed (its "hotness") and its likelihood to cause program execution error (its "risk"). This is particularly useful for memory systems which exhibit heterogeneity in their performance and reliability such as Heterogeneous Memory Architectures – with a typical configuration combining slow, highly reliable memory with faster, less reliable memory. This work demonstrates that current state of the art, performance-focused data placement techniques affect reliability adversely. It shows that page risk is not necessarily correlated with its hotness; this makes it possible to identify pages that are both hot and low risk, enabling page placement strategies that can find a good balance of performance and reliability. This work explores heuristics to identify and monitor both hotness and risk at run-time, and further proposes static, dynamic, and program annotation-based reliability-aware data placement techniques. This enables an architect to choose among available memories with diverse performance and reliability characteristics. The proposed heuristic-based reliability-aware data placement improves reliability by a factor of 1.6x compared to performance-focused static placement while limiting the performance degradation to 1%. A dynamic reliability-aware migration scheme, which does not require prior knowledge about the application, improves reliability by a factor of 1.5x on average while limiting the performance loss to 4.9%. Finally, program annotation-based data placement improves the reliability by 1.3x at a performance cost of 1.1%.
Proactive cyber security response by utilizing passive monitoring technologies

Recently, variety of cyber-attacks such as DDoS, Information Leakage, Illegal Access, Spam, Business E-mail Compromise, Phishing, Advanced Persistent Threats (APT), Man-in-the-Middle attacks are frequently recognized even in the consumer's environment. These cyber-attacks are often triggered by “malwares” and have been maliciously evolving and sometimes hidden from our monitoring countermeasures (FW, IDS/IPS). For proactively responding cyber-attacks, utilizing passive monitoring technologies should be reconsidered as possible security supportive solutions. In this talk, after introduction of latest cyber-attacks to share the current cyber threats landscape, passive monitoring technologies such as darknet and honeypot/sandbox will be explained with practical use-cases to accurately observe and monitor ongoing threats (cyber-attacks). The use-cases may include detection of malware-infected IoT devices by means of darknet and honeypot monitoring. Furthermore, detection of cyber-attacks by passive monitoring can be utilized for cyber security proactive response as practical solutions. Finally, future security considerations will be given for utilizing extendible passive monitoring technologies to proactively respond against cyber-attacks under smarter city and connected environments.
Structure and resilience of bald eagle roost networks

The recognition that communal roosts are important elements within the life cycle of bald eagles (Haliaeetus leucocephalus) led to their protection under the “disturb” clause of the Bald and Golden Eagle Protection Act. The regular roost-switching movements of bald eagles imply that roosts are part of an interactive network where roosts represent nodes linked by birds moving between them. Network analysis holds promise for informing management decisions by assessing the effect of roost removal on the resilience of the broader network. We tracked nonbreeding bald eagles (n1⁄4 56) within the upper Chesapeake Bay (2008–2013), USA, to evaluate roost characteristics and network structure. We used midnight locations (n1⁄4 14,464) to assess the use of communal roosts (n1⁄4 212) and movement of birds among roosts (n1⁄4 2,634) on successive roost nights to evaluate the pattern and strength of connections. We performed a sensitivity analysis to assess the response of the roost network to roost loss. Structure of the roost network approximated that of a scale-free network where the distribution of connections follows a power law of the form P(k)1⁄4Ak g and g1⁄4 1.1. Unlike random networks, connections within scale-free networks are concentrated within a few highly connected nodes (hubs). These hub roosts serve as bridges between large numbers of other roosts, have the shortest travel times to other roosts and greatest overall influence on network functioning. The effect of roost removal on overall network function was directly proportional to the connectivity of the roost being removed. The targeted removal of the majority (>90%) of roosts had very little effect on the network. Network sensitivity was high in response to the loss of roosts within the highest 10% of connectivity. This small (n1⁄4 18) subset of roosts makes a disproportionate contribution to network function and the protection of these roosts should be a stated management objective with high priority. Network analysis represents a powerful tool with the potential to inform management decisions. 2018 TheWildlife Society.
Toward reliable validation of HPC network simulation models

While the high performance computing (HPC) community is relying on simulations increasingly to co-design and optimize HPC interconnects, the simulation community lacks a coherent set of practices to be followed when validating the simulators and network models. Validation of HPC network simulation models is a multi-step process starting with the selection of representative communication patterns, configuring the network model, followed by designing the set of experiments, and finally, documenting the outcome for reproducibility. In this paper, we present a set of recommended practices for each of these steps in the validation process. If the recommendations are followed, the end result should be a validated network model that can make reasonably accurate predictions and convince the community about the correctness of the model.
Numerical modelling of the laser surface processing of magnesia partially stabilized zirconia by the means of three-dimensional transient finite element analysis

A numerical technique has been employed to use the ABAQUS finite element analysis (FEA) package in order to simulate the CO2 laser surface processing of a magnesia partially stabilized zirconia (MgO-PSZ) bioinert ceramic. The transient FEA takes into account the heat radiation, heat convection and phase change during the laser processing. The heat source has been modelled as a stepwise moving laser source with small steps in the scanning direction to approximate continuous movement. It further extends and validates numerical methods by comparing experimental data of surface temperature for laser surface processing of the MgO-PSZ to the solution from the FEA model. Experiments involving CO2 laser surface melting of the MgO-PSZ were also carried out using various laser process parameters, and the measured melt width and depth of laser-treated tracks were used to evaluate the validity of the models. In order to prevent the crack formation in the laser processing, pre- and post-heating were proposed by using the scanning of laser beam with the lower power before and after laser processing with high power to lower the thermal gradient.
Bifurcation behaviour in parallel‐connected boost converters

This paper describes the bifurcation phenomena of a system of parallel‐connected d.c./d.c. boost converters. The results provide important information for the design of stable current sharing in a master–slave configuration. Computer simulations and experiments are performed to capture the effects of variation of some chosen parameters on the qualitative behaviour of the system. In particular, it is found that variation of some parameters leads to Neimark–Sacker bifurcation. Analysis is presented to establish the possibility of the bifurcation phenomena. Copyright © 2001 John Wiley & Sons, Ltd.