Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating In-Memory Hash Joins on Persistent Memory

Steady advances in processor and memory technologies have driven continuous tuning and redesigning of in-memory hash joins for decades. Over the years, research has shown advantages of both hardware-conscious radix joins and hardware-oblivious hash joins for di � erent workloads. In this paper, we evaluate both join types on persistent memory (PMem) as an emerging memory technology o � ering close-to-DRAM speed at signi � cantly higher capacities. We study the no partitioning join (NPO) and the parallel radix join (PRO) in PMem and analyze how their performance di � ers from DRAM-based execution. Our results show that the PRO is always at least as fast as the NPO in DRAM. However, in PMem, the NPO outperforms the PRO by up to 1.7 ⇥ . Based on our � ndings, we provide an outlook into crucial design choices for PMem-optimized hash join implementations.
Toward Browser-based Interventions to Tackle Misinformation Online

The spread of misinformation online and its impact on society have become a pressing issue for both the technology industry and democracies around the world. Developing new tools and methods to mitigate this threat is critical. In recent years, "fake news" has received increasing attention because the term covers intentionally false, deceptive stories as well as factual errors, satire, and stories a person does not like. Few users possess the digital media literacy skills necessary to navigate these challenges. In this workshop paper, we propose developing a mixed-initiative, crowd-powered system to (i) semi-automatically explore definitions of fake news online, (ii) curate datasets of new stories for machine learning applications, (iii) power browser-based tools that identify misinformation content in-situ , and (iv) deploy digital media literacy interventions and news recommendations that increase a user’s hardiness against misinformation threats on online news media platforms. Our objective here is to stimulate discussion around our prototype’s design, new mitigation approaches, and their applications.
A SURVEY ON SELENIUM AUTOMATION TESTING TOOL

Before any web application or software is made available to general public; it goes through a process called Testing. It checks for security, compatibility, functionality and performance of the Software or Web Application. Testing can be done manually as well as automatically. Automated testing is a technique that performs testing using special automated testing software tools whereas manual testing is done by a human where one has to carefully execute each and every test case. In software development cycles, often same test case has to be run multiple times. An Automation tool records the test case and then it can be used as often as it is required. In this study, Selenium as an automation testing tool has been discussed in depth as well as why the Selenium Web Driver is ideally equipped for automation testing. Through our project, we identified the benefits of automation testing over manual testing.
Knowledge Graphs: A Guided Tour (Invited Paper)

Much has been written about knowledge graphs in the past years by authors coming from diverse communities. The goal of these lecture notes is to provide a guided tour to the secondary and tertiary literature concerning knowledge graphs where the reader can learn more about particular topics. In particular, we collate together brief summaries of relevant books, book collections, book chapters, journal articles and other publications that provide introductions, primers, surveys and perspectives regarding: knowledge graphs in general; graph data models and query languages; semantics in the form of graph schemata, ontologies and rules; graph theory, algorithms and analytics; graph learning, in the form of knowledge graph embeddings and graph neural networks; and the knowledge graph life-cycle, which incorporates works on constructing, refining and publishing knowledge graphs. Where available, we highlight and provide direct links to open access literature.
Deep Learning Methods for Fault Detection and 2 Classification in MMC-HVDC systems

: As there is not much literature about deep learning-based fault diagnosis for modular 15 multilevel converters (MMCs) and comparison among deep learning methods used in fault 16 diagnosis for MMC, two deep learning methods, namely, Convolutional Neural Networks (CNN) and Auto Encoder based Deep Neural networks (AE-based DNN) as well as stand-alone Softmax 18 classifier are explored for the detection and classification of faults of MMC-based high voltage direct current converter (MMC-HVDC). Only AC-side three-phase current and the upper and lower 20 bridges’ currents of the MMCs are used directly by our proposed approaches without any explicit 21 feature extraction or feature subset selection. The two-terminal MMC-HVDC system is established 22 in PSCAD/EMTDC to verify and compare our methods. The simulation results indicate CNN, AE- 23 based DNN, and Softmax classifier can detect and classify faults with high detection accuracy and 24 classification accuracy. Compared with CNN and AE-based DNN, the Softmax classifier behaved 25 better in detection and classification accuracy as well as testing speed. The detection accuracy of 26 AE-based DNN is a little better than CNN, while CNN needs less training time than the AE-based 27 DNN and Softmax classifier.
Importance of Representation Learning for Off-Policy Fitted Q-Evaluation

The goal of ofﬂine policy evaluation (OPE) is to evaluate target policies based on logged data with a possibly much different distribution. One of the most popular empirical approaches to OPE is ﬁtted Q-evaluation (FQE). With linear function approximation, several works have found that FQE (and other OPE methods) exhibit exponential error ampliﬁcation in the problem horizon, except under very strong assumptions. Given the empirical success of deep FQE, in this work we examine the effect of implicit regularization through deep architectures and loss functions on the divergence and performance of FQE. We ﬁnd that divergence does occur with simple feed-forward architectures, but can be mitigated using various architectures and algorithmic techniques, such as ResNet architectures, learning a shared representation between multiple target policies, and hypermodels. Our results suggest interesting directions for future work, including analyzing the effect of architecture on stability of ﬁxed-point updates which are ubiquitous in modern reinforcement learning.
The geomorphological distribution of subaqueous tufa columns within a hypersaline lake : Mono Lake , USA

6 Understanding the flow of carbon through hyperalkaline lakes is a key means of 7 understanding their biogeochemistry, sedimentology and their palaeoenvironmental 8 and palaeoclimatic records. Furthermore, understanding how mineral precipitation is 9 regulated in these lakes can provide insights into how their carbon sequestration 10 behaviour can be managed. We report geophysical surveys of Mono Lake, California, 11 USA, which show unanticipated geomorphological control on the recent / 12 contemporary formation of lacustrine carbonate formations (“tufa”). Acquired shallow 13 penetration seismic data shows a fault zone below the lake floor, but despite the 14 regional evidence for geothermal waters rising up these fractures, we find no evidence 15 for tufa precipitation at the surface exposure of this structure, either in the seismic 16 data or in the swath bathymetry. However, we do find sub-lacustrine tufa columns in 17 this data elsewhere, which is the first time these have been reported directly. We find 18 and report on a strong link between column location and meteoric Ca supply to the 19 lake, with the latter sourced either through surface runoff or groundwater. For 20 example, a region close to a creek inlet has more frequent and larger tufa bodies, which 21 grow at a wider depth range, than another region far from an inlet but close to the 22 fault. This demonstrates the importance of meteoric water ingress in regulating 23 carbonate mineral formation in these basins, and raises the possibility that 24 Article Click here to access/download;Article;Mono revised2_clean. docx
Low Complexity, Software Based, High Rate DSD Modulator Using Vector Quantification

High rate Direct Stream Digital (DSD) is emerging as a format of choice for distribution of high-definition audio content. However, real-time encoding of such streams requires considerable computing resources due to their high sampling rate, constraining implementations to hardware based platforms. In this paper we disclose a new modulator topology allowing for reduction in computational load and making real-time high rate DSD encoding suitable for software based implementation on off-the-shelf Digital Signal Processors (DSPs). We first present the architecture of the proposed modulator and then show results from a practical real-time implementation.
Contribution of Constructed Wetlands for Reclaimed Water Production: A Review

Freshwater scarcity is a growing threat to sustainable development, which can be mitigated by adequate management of water resources. Agriculture and related activities consist in the main use of freshwater, but several other human activities present relevant contributions. Because most of the water uses imply the generation of resultant wastewater, the production and use of reclaimed water by appropriate technologies can be part of the solution to that issue. Considering that the use of constructed wetlands (CWs) can be a relevant contribution to the production of reclaimed water, as an eco-friendly alternative to costly advanced water treatment technologies, this work is a review of the last decade of literature on the use of CWs to produce reclaimed water. The results point to a usual focus on the production of reclaimed water for agriculture or urban spaces irrigation. In order to potentiate a broader application of CWs, some directions of future research and use of this green technology are proposed.
Detection and quantification of Aβ−3–40(APP669-711)in cerebrospinal fluid Hans-WolfgangKlafki1 |OliverWirths1 |BritMollenhauer2,3 |ThomasLiepold4 | PetraRieper1 |HermannEsselmann1 |JonathanVogelgsang1 |JensWiltfang1,5,6 | OlafJahn1,4

This is an open access article under the terms of the Creat ive Commo ns Attri bution-NonCo mmercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. © 2022 The Authors. Journal of Neurochemistry published by John Wiley & Sons Ltd on behalf of International Society for Neurochemistry. Jens Wiltfang and Olaf Jahn contributed equally. Abbreviations: AD, Alzheimer's disease; APP, amyloid precursor protein; AUC, area under the curve; Aβ, amyloidβ; BACE1, βsite APP cleaving enzyme 1; CHCA, 2cyano4hydroxycinnamic acid; CNS, central nervous system; CSF, cerebrospinal fluid; DDM, ndodecylβdmaltoside; DMP, dimethyl pimelimidate dihydrochloride; IP, immunoprecipitation; IPMS, immunoprecipitation followed by mass spectrometry; mAb, monoclonal antibody; MALDITOFMS, matrixassisted laser desorption/ionization timeofflight mass spectrometry; NPH, normal pressure hydrocephalus; NTM, nonylβdthiomaltoside; PET, positron emission tomography; pTau, phospho Tau; ROC curve, receiver operating characteristic curve; RRID, Research Resource Identifier (see scicrunch.org); tTau, total Tau. 1Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG), GeorgAugustUniversity, Goettingen, Germany 2Department of Neurology, University Medical Center Goettingen (UMG), GeorgAugustUniversity, Goettingen, Germany 3ParacelsusElenaKlinik, Kassel, Germany 4Max Planck Institute of Experimental Medicine, Proteomics Group, Goettingen, Germany 5German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany 6Neurosciences and Signaling Group, Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
Adaptive Security Support for Heterogeneous Memory on GPUs

The wide use of accelerators such as GPUs necessities their security support Recent works [17], [33], [34] pointed out that directly adopting the CPU secure memory design to GPUs could incur significant performance overheads due to the memory bandwidth contention between regular data and security metadata. In this paper, we analyze the security guarantees that used to defend against physical attacks, and make the observation that heterogeneous GPU memory system may not always need all the security mechanisms to achieve the security guarantees. Based on the memory types as well as memory access patterns either explicitly specified in the GPU programming model or implicitly detected at run time, we propose adaptive security memory support for heterogeneous memory on GPUs. Specifically, we first identify the read-only data and propose to only use MAC (Message Authentication Code) to protect their integrity. By eliminating the freshness checks on read-only data, we can use an on-chip shared counter for such data regions and remove the corresponding parts in the Bonsai Merkel Tree (BMT), thereby reducing the traffic due to encryption counters and the BMT. Second, we detect the common streaming data access pattern and propose coarse- grain MACs for such stream data to reduce the MAC access bandwidth. With the hardware-based detection of memory type (read-only or not) and memory access patterns (streaming or not), our proposed approach adapts the security support to significantly reduce the performance overhead without sacrificing the security guarantees. Our evaluation shows that our scheme can achieve secure memory on GPUs with low overheads for memory-intensive workloads. Among the fifteen memory-intensive workloads in our evaluation, our design reduces the performance overheads of secure GPU memory from 53.9% to 8.09% on average. Compared to the state-of- the-art secure memory designs for GPU [17], [33], our scheme outperforms PSSM by up to 41.63% and 9.5% on average and outperforms Common counters by 84.04% on average for memory-intensive workloads. We further propose to use the L2 cache as a victim cache for security metadata when the L2 is either underutilized or suffers from very high miss rates, which further reduces the overheads by up to 4% and 0.65% on average.
Virtual Business Incubators: A Support for Entrepreneurship in Rural Areas?

This chapter examines the lack of entrepreneurial resources for venture development in rural regions and presents a study of how virtual business incubators (VBIs) can fill this gap and support rural entrepreneurial activities. The authors employ a single case study approach in which the empirical data builds on six interviews conducted with rural entrepreneurs and managers of VBIs. The data collection and data analysis follow the grounded theory approach set forth by Charmaz (2014). The main contribution of this research is the application of the VBI concept in the rural entrepreneurship context, with the aim of overcoming the challenges inherent to this context while conserving its benefits. 1 Jörg Freiling, Leon Marquardt, Tatevik Reit University of Bremen, Bremen, Germany E-Mail: Leon.marquardt@uni-bremen.de
Providing Real-time Driver Advisories in Connected Vehicles: A Data-Driven Approach Supported by Field Experimentation

Approximately 94% of the annual transportation crashes in the U.S. involve driver errors and violations contributing to the $1 Trillion losses in the economy. Recent V2X communication technologies enabled by Dedicated Short Range Communication (DSRC) and CellularV2X (C-V2X) can provide cost effective solutions for many of these transportation safety applications and help reduce crashes up to 85%. This research aims towards two primary goals. First, understanding the feasibility of deploying V2V-based safety critical applications under the constraints of limited communication ranges and adverse roadway conditions. Second, to develop a prototype application for providing real-time advisories for hazardous driving behaviors and to notify neighboring vehicles using available wireless communication platform. Towards accomplishing the first goal, we have developed a mathematical model to quantify V2V communication parameters and constraints pertaining to a DSRC-based “Safe pass advisory” application and validated the theoretical model using field experiments by measuring the communication ranges between two oncoming vehicles. We also investigated the impacts of varying altitudes, vehicle-interior obstacles, and OBU placement on V2V communication reliability and its implications. Along the direction of the second goal, we derived a data-driven model to characterize the acceleration/deceleration profile of a regular passenger vehicle with respect to speed and throttle position. As a proof of concept demonstration, we implemented an IoT-based communication architecture for disseminating the hazardous driving alerts to vulnerable drivers through cellular and/or V2X communication infrastructure.
Implementation of evidence-based nursing care for hospital patients with community-acquired pneumonia

8 Abbreviations . 9 Original papers . 10 Introduction . 11 Background . 12 Nursing care in a hospital setting . 12 Community-acquired pneumonia . 13 Evidence-based guidelines for CAP . 14 Adherence to EBGs in clinical practice . 15 Clinical pathway (CPW) . 16 Implementation.17 Rationale . 18 Aims . 21 Methods . 22 Applied theoretical model and frameworks . 22 Implementation Model of Change . 22 Theoretical Domains Framework . 24 Intervention Mapping Framework . 24 Behaviour Change Technique Taxonomy . 25 Implementation Outcomes Framework . 26 Design . 26 Research setting and sample . 27 Data collection.29 Study I . 29 Study II . 31 Study III . 32 Study IV . 32 Data analysis . 33 Content analysis.33 Descriptive statistics . 34 Ethical considerations . 34 Principles of respect for autonomy . 34 Principles of non-maleficence and beneficence . 35 Principles of justice . 35 Main results . 36 Principal findings . 36 Gaps in nursing care . 37 Barriers and facilitators influencing nursing care . 37 Design and development of an implementation strategy . 39 Evaluation of the strategy . 42 Discussion . 44 Gaps in nursing care . 44 Barriers and facilitators influencing nursing care . 46 Strategy for implementation of EBNC . 48 Changing nursing practice . 49 Methodological considerations . 51 Validity . 52 Credibility and confirmability . 53 Dependability and transferability . 54 Reflections regarding the theoretical frameworks . 55 Conclusion and clinical implications . 57 Further research . 58 Summary in Danish . 59 Acknowledgement . 61 References . 63 Appendix I-II . 80 Paper I-IV.83
Time to abandon the loss of dispersal ability hypothesis in island plants: A comment on Garc (cid:1) a-Verdugo, towards a better of in evolution.

Garc (cid:1) ı a-Verdugo et al. (2017) recently tested the loss of dispersal ability hypothesis in a wind-dispersed shrub from Southern Europe. Although the hypothesis has guided research for over 150 years, Garc (cid:1) ı a-Verdugo et al. (2017) results failed to substantiate its central prediction — the loss of seed dispersal potential in island populations. Here, I highlight several additional limitations of the hypothesis. First, Garc (cid:1) ı a-Verdugo et al. (2017) results are not unusual. Empirical support for the hypothesis is equivocal. Second, when reduced dispersal potential is documented, it may often evolve as a passive by-product of selection for large seeds, for reasons that are wholly unrelated to their dispersal. Third, the hypothesis does not readily apply to all plant dispersal modes, particularly plants that produce fleshy fruits. These issues advocate a fresh approach to the study of how selection shapes the evolution of dispersal potential on islands. When asked to explain the existence of flightless animals on isolated islands, Darwin (1859) responded with a famous analogy involving shipwrecked sailors (Lomolino, 2010). He argued there were two strategies for sailors to survive a shipwreck. The first was to be a strong enough swimmer to reach the shore. The second was to be a poor swimmer and cling to the wreck. This second strategy has played in the minds of island biogeographers ever since and ulti-mately led to a long-standing hypothesis of how dispersal evolves on oceanic islands. The loss of dispersal ability hypothesis predicts that islands are initially colonized by individuals with relatively good powers of dispersal, but through time, island populations evolve poorer disperser ability to avoid being swept out to sea (see Cody & Overton, 1996).
Risk Models Based on Non-Cognitive Measures May Identify Presymptomatic Alzheimer's Disease.

BACKGROUND Alzheimer's disease (AD) is a progressive disorder without a cure. Develop risk prediction models for detecting presymptomatic AD using non-cognitive measures is necessary to enable early interventions. OBJECTIVE Examine if non-cognitive metrics alone can be used to construct risk models to identify adults at risk for AD dementia and cognitive impairment. METHODS Clinical data from older adults without dementia from the Memory and Aging Project (MAP, n = 1,179) and Religious Orders Study (ROS, n = 1,103) were analyzed using Cox proportional hazard models to develop risk prediction models for AD dementia and cognitive impairment. Models using only non-cognitive covariates were compared to models that added cognitive covariates. All models were trained in MAP, tested in ROS, and evaluated by the AUC of ROC curve. RESULTS Models based on non-cognitive covariates alone achieved AUC (0.800,0.785) for predicting AD dementia (3.5) years from baseline. Including additional cognitive covariates improved AUC to (0.916,0.881). A model with a single covariate of composite cognition score achieved AUC (0.905,0.863). Models based on non-cognitive covariates alone achieved AUC (0.717,0.714) for predicting cognitive impairment (3.5) years from baseline. Including additional cognitive covariates improved AUC to (0.783,0.770). A model with a single covariate of composite cognition score achieved AUC (0.754,0.730). CONCLUSION Risk models based on non-cognitive metrics predict both AD dementia and cognitive impairment. However, non-cognitive covariates do not provide incremental predictivity for models that include cognitive metrics in predicting AD dementia, but do in models predicting cognitive impairment. Further improved risk prediction models for cognitive impairment are needed.