Miracle-dz/UniFAR-FTUs huggingface dataset:
eGPU for Monitoring Performance and Power Consumption on Multi-GPUs

The evaluation of performance and power consumption is a key step in the design of applications for large computing systems, such as supercomputers, clusters with nodes that have manycores and multi-GPUs. Researchers must design several experiments for workload characterization by observing the architectural implications of different combinations of parameters, such as problem size, number of cores per GPUs, number of process MPI, and observe the resulting clock frequency, memory usage, bandwidth, and power consumption, factors which determine the performance and energy efficiency of their workload implementation. The major problem in performance evaluation consists on the design space exploration given so many parameters. If the resulting data is not properly collected and the parameters of evaluation performance are not well organized, the design exploration may lead to wrong conclusions. This paper presents eGPU such as monitoring tool that can be used on evaluations of performance with many experiments which aim measure and understand the behavior of factors that determine the energy efficiency in nodes with multi-GPUS. eGPU is a monitor to centralize and automate the data captured in runtime while is executed in parallel with the Linpack Benchmark and displays information via sequenceplots, statistical tables, bar graph and shows results in terms of energy efficiency.
On the use of hierarchical subtrace mining for efficient local process model mining

Mining local patterns of process behavior is a vital tool for the analysis of event data that originates from flexible processes, for which it is generally not possible to describe the behavior of the process in a single process model without overgeneralizing the behavior allowed by the process. Several techniques for mining such local patterns have been developed throughout the years, including Local Process Model (LPM) mining and the hierarchical mining of frequent subtraces (i.e., subprocesses). These two techniques can be considered to be orthogonal, i.e., they provide different types of insights on the behavior observed in an event log. As a consequence, it is often useful to apply both techniques to the data. However, both techniques can be computationally intensive, hindering data analysis. In this work, we explore how the output of a subtrace mining approach can be used to mine LPMs more efficiently. We show on a collection of real-life event logs that exploiting the ordering constraints extracted from subtraces lowers the computation time needed for LPM mining compared to state-ofthe-art techniques, while at the same time mining higher quality LPMs. Additionally, by mining LPMs from subtraces, we can obtain a more structured and meaningful representation of subprocesses allowing for classic process-flow constructs such as parallel ordering, choices, and loops, besides the precedence relations shown by subtraces.
Modeling customer purchase behavior based on page transitions by latent class model for customer segmentation

Recently, it has become popular to purchase product items through E-commerce sites (EC sites), and the internet market scale has been expanding. Under this situation, many EC sites conduct various kinds of sales promotion activities by analyzing huge amount of customers' purchase histories, and customer segmentation is one of the most important tools in marketing. Particularly, modeling of customers purchase behavior based on probabilistic models such as the Aspect Model (AM) is an attractive way for customer segmentation. The AM focuses on pairs of a customer and an item and it assumes unobserved features such as customers' heterogeneity and items' similarity as latent classes. Although the original AM focuses on pairs of a customer and an item mainly, the data about customers’ browsing histories are also available on EC sites. If the model can take in the information of page transitions, it becomes possible to model customers' purchase behavior in detail and make better customer segments. In this paper, we propose a new latent class model that integrates browsing histories in addition to purchase histories by assuming that customers' page transitions can be described by a Markov process. An analysis of actual EC site data is demonstrated to clarify the effectiveness of our proposed model.
Deposited in DRO : 03 September 2019 Version of attached le : Peer-review status of attached

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Hierarchical Attention Fusion Network for Question Answering

[1] Seo, M. , Kembhavi, A. , Farhadi, A. , etc. (2016). Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603. [2] Kim, Y. , Jernite, Y. , Sontag, D. , etc. (2016). Character-aware neural language models. In 13th AAAI Conference on Artificial Intelligence. [3] Wang, W. , Yan, M. , Wu, C. (2018). Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering. arXiv preprint arXiv:1811.11934. ● Goal: Build a Question Answering model on Stanford Question Answering Dataset (SQuAD) ● Data: Default SQuAD train, dev, test set provided by TA ● Evaluation: F1, EM
A. U. T. Amah and A. Klein, ”Pair-Aware Transceive Beamforming for Non-Regenerative Multi-User Two-Way Relaying,” in Proc. IEEE International Conference on Acoustics, Speech,

In non-regenerative two-way relaying, two communicating nodes transmit simultaneously to the Relay Station (RS). The RS processes the received signal and forwards it to both nodes simultaneously. In order to obtain its partner’s information, each node performs self-interference cancellation by subtracting its transmitted signal from its received signal. However, in multi-user two-way relaying, the interference at each node is not only the a priori self-interference, but also the interference from the other two-way pairs’ nodes. Using Zero Forcing (ZF) transceive beamforming, the RS spends unnecessary energy to cancel the interference that can be cancelled by the nodes. Therefore, we propose to apply pairaware transceive beamforming (PATB) at the RS to cancel only the other-pair interference and let each node cancels its self-interference. Two PATB schemes are proposed, namely, pair-aware (PA) matched filter (PA-MF) and PA semidefinite relaxation of maximising the minimum signal to noise ratio (PA-SDR). From sum rate analysis, PA-SDR outperforms PA-MF, and both outperform non-PA ZF.
SBM ANALYSIS OF THE INFLUENCE OF BONE-SHAPE IN THE TARGET STRENGTH OF A FISH

Existing works have addressed the target strength (TS) of a fish using different experimental and numerical strategies, and assessing different configurations. Here, we propose the application of a numerical model based on the Singular Boundary Method (SBM) to analyse the effect of considering a realistic irregular geometry for the fish bone in the TS evaluation. In particular, it is sought to understand the possible effect of the irregularities in the cancellation of coherent field effects that may occur when a regular cylindrical bone is considered.
Phylogenetic and morphological diversity of culturable cyanobacteria from Lake Magadi in Kenya

Lake Magadi is an alkaline saline Lake in Kenya, which lies on the Great Eastern Rift valley. Although the lake is characterized by extremes of salt, pH and temperature, it supports diverse groups of cyanobacteria. In this study, we used different media to isolate novel groups of cyanobacteria. We recovered 11 isolates affiliated to the orders Chroococcales, Oscillatoriales, Pleurocapsales and Nostocales. Isolates affiliated to Chroococcidiopsis species had similarity values below 90% to currently characterized taxa indicating that these could be completely new phylotypes. This taxon has not been isolated before from the soda lake indicating the power of molecular techniques in identifying novel cyanobacterial taxa. Only two of the recovered isolates had 99% similarity to known organisms. Previous studies have mainly relied on microscopic examination and identification, which can lead to misidentification and subsequent assignment of an organism to the wrong taxon. The recovered isolates are a useful resource of more studies on taxonomy and secondary metabolite production.
Residual Multiscale Based Single Image Deraining

Rain streaks deteriorate the performance of many computer vision algorithms. Previous methods represent rain streaks by different rain layers and then separate those layers from the background image. However, it is rather difﬁcult to decouple a rain image into rain and background layers due to the complexity of real-world rain, such as various shapes, directions, and densities of rain streaks. In this paper, we propose a residual multiscale pyramid based single image deraining method to alleviate the difﬁculty of rain image decomposition. In particular, we remove rain streaks in a coarse-to-ﬁne manner. In this fashion, the heavy rain can be signiﬁcantly removed in the coarse-resolution level of the pyramid ﬁrst, and the light rain will then be further removed in the high-resolution level. This allows us to avoid distinguishing the densities of rain streaks explicitly since the inaccurate classiﬁcation of rain densities may lead to over-or insufﬁcient-removal of rain. Furthermore, the residual between a recovered image and its corresponding rain image can provide vital clues of rain streaks. We therefore exploit such residual as an attention map for deraining in its consecutive ﬁner-level. Beneﬁting from the residual attention maps, rain layers can be better extracted from a higher-resolution input image. Extensive experimental results on synthetic and real datasets demonstrate that our method outperforms the state of the art signiﬁcantly.
Aalborg Universitet Statistical Multiplexing of Computations in CRAN with Tradeoffs in Latency and Energy

In the Cloud Radio Access Network (C-RAN) architecture, the baseband signals from multiple remote radio heads are processed in a centralized baseband unit (BBU) pool. This architecture allows network operators to adapt the BBU’s computational resources to the aggregate access load experienced at the BBU, which can change in every air-interface access frame. The degree of savings that can be achieved by adapting the resources is a tradeoff between savings, adaptation frequency, and increased queuing time. If the time scale for adaptation of the resource multiplexing is greater than the access frame duration, then this may result in additional access latency and limit the energy savings. In this paper we investigate the tradeoff by considering two extreme time-scales for the resource multiplexing: (i) long-term, where the computational resources are adapted over periods much larger than the access frame durations; (ii) short-term, where the adaption is below the access frame duration. We develop a general C-RAN queuing model that describes the access latency and show, for Poisson arrivals, that long-term multiplexing achieves savings comparable to shortterm multiplexing, while offering low implementation complexity.
Unsupervised Learning of Multiple Languages Using Recurrent Neural Networks

We explore the capability of Recurrent Neural Networks (RNNs) to capture the statistics of hidden information on textual data. In this work, we focus on the identity of the text language as a hidden variable. We train different RNNs on a corpus containing text in different languages, without providing explicit information about the specific identity of the text. We compare RNNs and N-grams in terms of their prediction error, space complexity, and scalability to larger corpora. Keywords—Machine learning, connectionism and neural nets, text analysis, language generation, information theory, natural language modeling.
Optimum recovery time for cyclic compression tests on bovine brain tissue

In conducting mechanical tests on the brain tissue, it is preferred to perform multiple tests on the same sample. In this study we investigated the behavior of the bovine brain tissue in repeated compression tests with six recovery periods (10, 60, 120, 180, 240 and 300 s). Compression tests were performed on cylindrical samples with an average diameter and height of 18.0 mm and 15.0 mm respectively. Two testing protocols were employed: first protocol comprised of experiments with 5, 25 and 125 mm/min loading speed up to 33% strain and the second protocol consisted of tests with 25 and 125 mm/min loading speed up to 17% strain. Each experiment was conducted in two cycles separated by a specific recovery period. Stress-strain data from the first and second cycles were compared using three criteria, namely Normalized root-mean-square error (NRMSE), coefficient of variation (R2) and effective height ratio (EHR). The analysis suggests that the optimum recovery period for the first and second protocols are 120 s and 180 s respectively. Moreover, differences between the first and second cycles of medium and high speed tests were found to be smaller compared to the low-speed experiments.
Spatially Resolved Neutral Wind Response Times

17 It has previously been shown that in the high latitude thermosphere, sudden changes in 18 plasma velocity (such as those due to changes in interplanetary magnetic field) are not 19 immediately propagated into the neutral gas via the ion-drag force. This is due to the 20 neutral particles (O, O2 and N2) constituting the bulk mass of the thermospheric alti21 tude range, and thus holding on to residual inertia from a previous level of geomagnetic 22 forcing. This means that consistent forcing (or dragging) from the ionospheric plasma 23 is required, over a period of time long enough, for the neutrals to reach an equilibrium 24 with regards to ion-drag. Furthermore, mesoscale variations in the plasma convection 25 morphology, solar pressure gradients and other forces indicate that the thermosphere26 ionosphere coupling mechanism will also vary in strength across small spatial scales. Us27 ing data from the Super Dual Auroral Radar Network (SuperDARN) and a Scanning 28 Doppler Imager (SCANDI), a geomagnetically active event was identified which showed 29 plasma flows clearly imparting momentum to the neutrals. A cross-correlation analy30 sis determined that the average time for the neutral winds to accelerate fully into the 31 direction of ion-drag was 75 minutes, but crucially, this time varied by up to 30 minutes 32 (between 67 and 97 minutes) within a 1000 km field of view at an altitude of around 250 km. 33 It is clear from this that the mesoscale structure of both the plasma and neutrals have 34 a significant effect on ion neutral coupling strength, and thus energy transfer in the ther35 mosphere. 36
Evaluation Methods in Medical Informatics

Charles P Friedman, Jeremy C Wyatt: Springer, £40, pp 311 ISBN 3 540 94228 9 Computers have not been kind to physicians. They enable monitoring and pestering everywhere. None the less, there have been generations of computer enthusiasts in health care, including many doctors. The appeal seems obvious: we forget easily, the computer never forgets; we are slow to do simple things, the computer is fast; we tire, the machine is tireless. Even 30 years ago it seemed a natural partnership. With the computer to leverage our strengths and ease our frailties, we could all be Oslers. …
EFFECTIVENESS BY WHAT MEASURE ? INCENTIVES , INSTITUTIONS AND STRATEGIC NETWORKS IN CHINA

We investigate the interplay between an individual decision-maker’s incentive structure and characteristics of the institutional environment as drivers of inter-organizational network evolution. We use longitudinal data on new product development projects in the pharmaceutical industry in China between 1986 and 1994 to study the impact of key reforms on the size, scope and structural patterns of interactions among research organizations and manufacturers. In contrast to findings in developed market economies, network-level results in this context show clear year-on-year increases in all measures of network scale and scope, but the emergence of a distributed network structure. At the organization level, we find considerable homogeneity among actors’ network behaviors in terms of scale and scope, as well as consistency over time. We interpret these patterns in light of the changing institutional environment and incentive structure facing the top managers of these organizations. Furthermore, we evaluate the effectiveness of their network strategies in terms of the rationality of their choices given the specific incentive structure—the complex of motivations and constraints—facing these decision-makers.
Sensor-Independent Illumination Estimation for DNN Models

While modern deep neural networks (DNNs) achieve state-of-the-art results for illuminant estimation, it is currently necessary to train a separate DNN for each type of camera sensor. This means when a camera manufacturer uses a new sensor, it is necessary to retrain an existing DNN model with training images captured by the new sensor. This paper addresses this problem by introducing a novel sensor-independent illuminant estimation framework. Our method learns a sensor-independent working space that can be used to canonicalize the RGB values of any arbitrary camera sensor. Our learned space retains the linear property of the original sensor raw-RGB space and allows unseen camera sensors to be used on a single DNN model trained on this working space. We demonstrate the effectiveness of this approach on several different camera sensors and show it provides performance on par with state-of-the-art methods that were trained per sensor.
The Aggregate E ff ects of Trade and Migration : Evidence from OECD countries

Two large but separate bodies of literature analyze the economic effects of international trade and immigration. Given that several factors are important determinants of both trade and migration flows, the previous studies are vulnerable to a potentially serious omitted-variables bias, questioning the validity of existing estimates of the effects of trade and immigration on income. This paper provides estimates of the effects of trade and immigration on income in a unified framework. We also provide a useful decomposition of the channels at work in terms of the employment rate, the capital intensity, and total factor productivity of the receiving economy. We assemble panel data on immigration flows, output, employment and capital stocks for thirty OECD countries over the period 1980-2007. In order to identify the causal effects of trade and immigration on economic outcomes we adopt and extend the gravity-based approach in Frankel and Romer (1999). Our predictors for trade and immigration flows are based on geography and the demographic trends of each country’s trade and migration partners. We find that immigration has a large, positive effect on the employment rate of the receiving country. However, it leaves income per capita unaffected because of an offsetting negative effect on TFP. In contrast, trade flows appear to increase income per capita, mainly through TFP growth, and have no impact on the employment rate. The positive employment effect of immigration is the most robust of all the effects identified in this paper.
Question Answering on SQuAD 2.0

In this project, we use SQuAD 2.0 dataset and build several end-to-end systems to perform automated question answering base on the given context. In this paper we present an end-to-end model which combines idea from BiDAF, QANet, R-NET and an ensemble of 9 models achieve EM 65.65, F1 68.79 in the development set.
le Transcutaneous Electrical Nerve Stimulation at the Acupuncture Points to Relieve Pain of Patients Under Mechanical Ventilation : A Randomized Controlled Study

1 Department of Anesthesia, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran 2 Department of Nursing, School of Nursing, Kermanshah University of Medical Sciences, Kermanshah, Iran 3 School of Allied Medical Sciences, Kermanshah University of Medical Sciences, Kermanshah, Iran 4 Department of Anesthesia, School of Allied Medical Sciences, Kermanshah University of Medical Sciences, Kermanshah, Iran Available online 20 July 2018
POSTPRINT 1 : Coordinating Knowledge Work in Multiteam Programs : Findings From a Large-Scale Agile Development Program

Software development projects have undergone remarkable changes with the arrival of agile development approaches. Although intended for small, self-managing teams, these approaches are used today for large development programs. A major challenge of such programs is coordinating many teams. This case study describes the coordination of knowledge work in a large-scale agile development program with 12 teams. The findings highlight coordination modes based on feedback, the use of a number of mechanisms, and how coordination practices change over time. The findings can improve the outcomes of large knowledge-based development programs by tailoring coordination practices to needs over time.
HorrifAI : Using AI to Generate Two-Sentence Horror

Natural language generation is a rapidly evolving field that has already been applied to various domains, from the practical like summarization to the more whimsical like recipe creation; however, one area in which research has been fairly limited is that of horror fiction. In this project, we address the task of generating two-sentence horror, a form of flash fiction in which a self-contained horror story is told in only two sentences. Using models from the OpenNMT toolkit [1], we demonstrate the efficacy of current text generation approaches in a new domain with significant constraints, such as a distinctive style as well as a full narrative arc in a limited scope. In addition, we explore a two-pronged approach to the evaluation of our generated text by addressing language fluency and story cohesiveness separately. We find that standard Seq2Seq models used as retrieval models manage to achieve high-levels of readability in terms of grammar and spelling but often struggle with generating endings that are relevant to the beginning of the story.
HEAVY METAL REMOVAL USING BOUND MACRO CYCLES

Aza macrocycles bound to silica gel beads were found to selectively remove heavy metal cations such as Pb'+, Cd'+, Ag+, and Hg'+ from aqueous solutions. These bound macrocycles have a virtually infinite selectivity of binding with heavy metals over alkali and alkaline earth cations. The material is very stable and can be reused hundreds of times. Columns may be regenerated using an acidic eluent solution. Equilibrium constants were determined by column tests. Removal of heavy metals was demonstrated in bench scale tests using a synthetic contaminated water and in pilot scale tests using a naturally contaminated river water. Heavy metal concentrations could be effectively reduced to the I'g/L level using the process.