Miracle-dz/UniFAR-FTUs huggingface dataset:
DBMS Fitting: Why should we learn what we already know?

Deep Neural Networks (DNNs) have successfully been used to replace classical DBMS components such as indexes or query optimizers with learned counterparts. However, commercial vendors are still hesitating to put DNNs into their DBMS stack since these models not only lack explainability but also have other significant downsides such as the requirement for high amounts of training data resulting from the need to learn all behavior from data. In this paper, we propose an alternative approach to learn DBMS components. Instead of relying on DNNs, we propose to leverage the idea of differentiable programming to fit DBMS components instead of learning their behavior from scratch. Differentiable programming is a recent shift in machine learning away from the direction taken by DNNs towards simpler models that take advantage of the problem structure. In a case study we analyze and discuss how to fit a model to estimate the cost of a query plan and present initial experimental results that show the potential of our approach.
Identification of Li-ion battery models through monotonic echo serial networks for coarse data

Monotone transformation models are extended to inaccurate data and are combined with recurrent neural networks in a new battery model that is able to ascertain the health of rechargeable batteries for automotive applications. The presented method exploits the information contained in the vehicle’s operational records better than other cutting-edge models and uses a minimum amount of human expert knowledge. The experimental validation of the technique includes a comparative analysis of batteries in different health conditions, comprising first-principles models and different machine learning procedures.
Topology-aware Parallel Data Processing: Models, Algorithms and Systems at Scale

The analysis of massive datasets requires a large number of processors. Prior research has largely assumed that tracking the actual data distribution and the underlying network structure of a cluster, which we collectively refer to as the topology, comes with a high cost and has little practical ben-eﬁt. As a result, theoretical models, algorithms and systems often assume a uniform topology; however this assumption rarely holds in practice. This necessitates an end-to-end investigation of how one can model, design and deploy topology-aware algorithms for fundamental data processing tasks at large scale. To achieve this goal, we ﬁrst develop a theoretical parallel model that can jointly capture the cost of computation and communication. Using this model, we explore algorithms with theoretical guarantees for three basic tasks: aggregation, join, and sorting. Finally, we consider the practical aspects of implementing topology-aware algorithms at scale, and show that they have the potential to be orders of magnitude faster than their topology-oblivious counterparts.
LEVELS OF VIGILANCE TRACK CHANGES IN FLOCK SIZE IN THE GREATER FLAMINGO (PHOENICOPTERUS RUBER RUBER)

Numerous empirical studies in birds have demonstrated a decrease in the proportion of time allocated to vigilance as flock size increases (reviewed by Elgar 1989, Roberts 1996, Treves 2000). The negative correlation between vigilance and group size is usually ascribed to a perceived reduction in predation risk in larger groups and/or to an increase in the intensity of feeding competition (Beauchamp 2003). Because investigations of the group-size effect are often correlational, inferences cannot be made about the causal link between changes in flock size and vigilance. In such cases, several extraneous factors may explain why vigilance decreases in large flocks. The most obvious factor is food density. If large flocks aggregate over areas of high food density, vigilance may be expected to decrease as more time can be allocated to feeding thus making the inverse relationship between flock size and vigilance causally spurious (Elgar 1989). One possible way to establish a stronger causal link between vigilance and flock size might be to match changes in vigilance with concurrent changes in flock size in the field during a continuous time sequence. Adjustments in vigilance due to the arrival or departure of companions is more likely to be caused by changes in flock size than by changes in extraneous factors, such as food density, since birds are foraging at the same location at nearly the same time. Among the few studies that have used such a matched design, vigilance has been shown to increase after the departure of companions and to decrease after the arrival of companions in foraging Downy Woodpeckers (Picoides pubescens) (Sullivan 1984) and in preening Crested Terns (Sterna bergii) (Roberts 1995). Here, we examine changes in vigilance
QuantHD: A Quantization Framework for Hyperdimensional Computing

Brain-inspired hyperdimensional (HD) computing models cognition by exploiting properties of high dimensional statistics—high-dimensional vectors, instead of working with numeric values used in contemporary processors. A fundamental weakness of existing HD computing algorithms is that they require to use floating point models in order to provide acceptable accuracy on realistic classification problems. However, working with floating point values significantly increases the HD computation cost. To address this issue, we proposed QuantHD, a novel framework for quantization of HD computing model during training. QuantHD enables HD computing to work with a low-cost quantized model (binary or ternary model) while providing a similar accuracy as the floating point model. We accordingly propose an FPGA implementation which accelerates HD computing in both training and inference phases. We evaluate QuantHD accuracy and efficiency on various real-world applications, and observe that QuantHD can achieve on average 17.2% accuracy improvement as compared to the existing binarized HD computing algorithms which provide a similar computation cost. In terms of efficiency, QuantHD FPGA implementation can achieve on average <inline-formula> <tex-math notation="LaTeX">$42.3\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$4.7\times $ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$34.1\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$4.1\times $ </tex-math></inline-formula>) energy efficiency improvement and speedup during inference (training) as compared to the state-of-the-art HD computing algorithms.
Dynamic interactions between the RNA chaperone Hfq, small regulatory RNAs and mRNAs in live bacterial cells

RNA binding proteins play myriad roles in controlling and regulating RNAs and RNA-mediated functions, often through simultaneous binding to other cellular factors. In bacteria, the RNA chaperone Hfq modulates post-transcriptional gene regulation. Absence of Hfq leads to the loss of fitness and compromises the virulence of bacterial pathogens. Using live-cell super-resolution imaging, we are able to distinguish Hfq binding to different sizes of cellular RNAs. We demonstrate that under normal growth conditions, Hfq exhibits widespread mRNA binding activity. Particularly, the distal face of Hfq contributes mostly to the mRNA binding in vivo. In addition, binding of Hfq to these mRNAs can recruit RNase E to promote turnover of these mRNAs in an sRNA-independent manner, providing one mechanism to release Hfq from the pre-bound mRNAs. Finally, our data indicate that sRNAs, once expressed, can either co-occupy Hfq with the mRNA or displace the mRNA from Hfq, suggesting mechanisms through which sRNAs rapidly access Hfq to induce sRNA-mediated gene regulation. Our data collectively demonstrate that Hfq dynamically changes its interactions with different RNAs in response to changes in cellular conditions.
ADAPTIVE SERVICE FUNCTION ASSIGNMENT WITH SOFTWARE DEFINED RADIO FOR COMPOSITE WIRELESS SYSTEMS

Services and applications provided over wireless systems are increasing every year. However, because the quality of service (QoS) differs from service to service, it is difficult to provide all services and applications using one wireless system alone. To solve this problem, we propose an adaptive service function assignment that efficiently uses wireless systems. We evaluated its performance in computer simulations and show the efficiency of the proposed scheme based on the simulation results. We also considered the network architecture of composite wireless systems that use the adaptive service function assignment method.
What is Wrong with Them?: Qualitative Analysis on ImageNet Failure Cases

In a recent decade, ImageNet [2] has become the most notable and powerful benchmark database in computer vision and machine learning community. With a large number of natural images and 1000+ annotated classes, ImageNet has initiated the large scale computer vision era and also has provided a chance for deep learning (DL) to achieve its golden age. Since the DL technology has been applied, the error rates for ImageNet classification task, previously exceeding 20%, has fallen to 10%. With the recent development of more advanced models such as ResNet [1], DL has achieved the error rates of 3% in 2016 ImageNet Challenge. With this success, ImageNet’s legacy is evolving eo apply DL technology into more diverse tasks, such as image captioning, visual question answering, and gaming. As ImageNet has emerged as a representative benchmark for evaluating the performance of novel DL models, its evaluation tends to include only quantitative measures such as error rate, rather than qualitative analysis. Recent DL models for ImageNet also has focused on how to innovate the network structure and improve the accuracy while designing a deeper and larger network. Thus, there are few studies that analyze the failure cases of DL models in ImageNet.In this abstract, we qualitatively analyze the failure cases of ImageNet classification results from recent DL model, and categorize these cases according to the certain image patterns. Through this failure analysis, we believe that it can be discovered what the final challenges are in ImageNet database, which the current DL model is still vulnerable to.
GENERALIZED ENTROPIES VIA FUNCTIONAL EQUATIONS AND DETERMINING MEAN CODEWORD LENGTHS

Usually in the literature of information theoretic measures, entropies are expressed as the weighted arithmetic mean of their generating functions, the findings of which are sometimes pseudo-additive entropies. In the present communication, we have expressed the generalized existing entropies in terms of mean values and determined the parametric mean codeword lengths using the additivity condition. Power mean and geometric mean have been determined using the concept of multiplicativity of means and it is shown that that multiplicative means serves as a lower and upper bound to the newly introduced additive mean codeword length.
Adhesive Retained Extra Oral Defect Maxillofacial Prosthesis

Rehabilitation of maxillofacial defect patients is a challenging task as there has been an increase in the acquired facial defects, resulting from cancer surgeries and automobile accidents leading to facial trauma. Nonetheless, the congenital facial defects also demand for facial rehabilitation. These defects may be restored by reconstructive surgery but many a times due to multiple surgical interventions and compromised esthetic outcome rehabilitation by prosthetic means is a chosen option. The most common prosthetic treatment problem with such patients is, getting adequate retention, stability, and support. In cases of maxillofacial defect, movement of the prosthesis is inevitable. The primary objectives in rehabilitating the maxillofacial defect patients are to restore the function of mastication, deglutition, speech, and to achieve normal orofacial appearance.
Generation of 3D Human Models and Animations Using Simple Sketches

Generating 3D models from 2D images or sketches is a widely studied important problem in computer graphics. We describe the first method to generate a 3D human model from a single sketched stick figure. In contrast to the existing human modeling techniques, our method requires neither a statistical body shape model nor a rigged 3D character model. We exploit Variational Autoencoders to develop a novel framework capable of transitioning from a simple 2D stick figure sketch, to a corresponding 3D human model. Our network learns the mapping between the input sketch and the output 3D model. Furthermore, our model learns the embedding space around these models. We demonstrate that our network can generate not only 3D models, but also 3D animations through interpolation and extrapolation in the learned embedding space. Extensive experiments show that our model learns to generate reasonable 3D models and animations.
STIRRED SLIM REACTORS – PART II: MULTI

Based on the successful predictions of transient dr op sizes in the first part of this research (Maaß et al., Prediction of drop sizes for liquid-li qu d systems in stirred slim reactors-Part I: Single stage impellers. Chem. Eng. J., 162 (2010) 7 92-801), this part is a straight continuation and extension of the earlier work. The predictive c apabilities of the used population balance equation model are increased for single stage impel lers and transferred to scale-up procedures of such applications. Therefore different scale-up r les for liquid-liquid systems are tested experimentally and by simulations in two different sized, geometrically similar vessels. The multi stage impellers are tested against comparable single stage impellers in terms of power consumption, mixing time and minimum impeller speed . Especially for high aspect ratios (larger than three), multi stage impellers successf ully compete with the single stage ones. The measured drop size distributions in slim reactors w ith multi stage impellers showed no dependency on the local position, although the disper sion process is tedious due to the compartmentalization. The simulations are not able to reflect this initial phase of the dispersion process, but are in close agreement with the experi ments after complete dispersion is fulfilled. Based on these experiences the aspect ratio is incr eased up to five and the resulting drop size can be predicted with reasonable deviations (lower 10 than percent). The results of the scaleup of this multi stage impeller liquid-liquid syste m do not lead to a clear conclusion. Although the simulations recommend the use of constant power input, the experiments could not support this. None of the other traditional scale-up r le are supported by the experiments. Overall, the results of power consumption, mixing time and dispersion behavior show the great potential of multi stage impellers for process opti mization and intensification in slim reactors.
3D-Printed Atmospheric-Pressure Plasma Reactors

Recently, the application of 3D-printing technology has opened a new frontier in industry, material science, chemistry, and medicine. In the field of plasma engineering, a plasma reactor is a key piece of equipment to successfully produce plasma. The aim of this research is to verify and evaluate the feasibility of a novel plasma reactor prototype prepared using a 3D printer. Essentially, a non-thermal plasma reactor operated at atmospheric pressure consists of electrodes (metals) and support parts (insulators). The insulating parts are usually made of glass, ceramics, or plastics. In this study, a new approach for the design and fabrication of non-thermal plasma reactors used at atmospheric pressure is presented. The reactor is based on a coaxial cylindrical configuration. The insulating parts of the reactor were prepared using a 3D printer. The performance of the prototypes was experimentally evaluated by comparison with that of a normal plasma reactor. Keywords—3D printer, 3D data, plasma reactor, rapid prototyping, reactor optimization Corresponding author: Seiji Kanazawa e-mail address: skana@oita-u.ac. jp Presented at the 3rd Korea-Japan Conference on Plasma and Electrostatics Technologies, in November 2014 Fig. 1. 3D printing model reactor. (DBD plasma reactor with running water film) Kanazawa et al. 103
Diffraction problem due to water wave loads on a cylinder over a coaxial bottom-mounted barrier in a channel

In this work, we used the channel multipoles, eigenfunction expansion and separation of variables method to determine the diffracted potential of the wave for a floating solid cylinder in the free surface of the water over a coaxial bottom-mounted cylindrical barrier in a channel. Also, the mathematical formulation of the proposed model based on the linearized water wave theory. The forces due to diffraction acting on the floating cylinder are described here based on the Bernoulli’s equation of fluid flow. The effect of various parameters of that composite structure on the exciting force and overturning moment of the floating cylinder is investigated. The result shows that a significant influence of forces acting on the floating cylinder in the presence of a bottom-mounted barrier in the channel which gives an accuracy of the analytical results.
Cognitive Dimensions of Between-Table Context Support in Direct Manipulation Wrangling Interfaces

Despite many commercially available tools that support or are designed explicitly for data wrangling, there exists no systematic evaluation of the strengths and weaknesses of these tools. This analysis project for CPSC 547 Information Visualization evaluates two popular and activelydeveloped wrangling tools, OpenRefine and Dataprep. By reproducing the wrangling processes conducted by journalists originally using idiosyncratic scripts written in Python and R on real-world data identified in prior work, this project is able to compare and contrast the usability of both applications in the context of realworld data. This usability analysis is based on the cognitive dimensions of notation framework, a user-interface independent set of tools designed explicitly to facilitate such comparisons. In the end, this report finds that OpenRefine and Dataprep share much of the same core functionality; although, Dataprep’s use of visualization leads to less error-proneness in the overall process and higher quality data its conclusion.
Optical research tools for real-time cell and tissue culture monitoring

Rapid technological development in recent years has led to the development of new tools for on-line monitoring of in vitro cell/tissue culture experiments in real time. Optical research tools in particular have seen important advances. Examples of this are the development of two-photon microscopy and the broad application of flow cytometry in cell culture research. A third approach that is gaining more attention is evanescent waveguide sensing, due in part to the advances in the (micro)fluidic components of these systems. In this work, we review the current application of these three optical technologies to real-time monitoring of cell and tissue models, to better understand their strengths and limitations. Furthermore, this review serves as a guideline to help define the method and parameters to be monitored for specific experimental goals in such research. To assess the applicability of the optical tools in in vitro research, we reviewed the following specifications: sample preparation (necessity of labelling, demand for material extraction), time of analysis, possibility of real-time monitoring, and sensitivity. Taking these specifications into consideration allows one to reliably select an optical method for monitoring cell cultures for a given application, as well as giving a perspective on future developments in this field. Finally, we point out possible directions of development for the technologies under consideration, which are influenced by the trend towards miniaturization in laboratory equipment. One important factor contributing to this trend is microfluidics, which offers solutions for current limitations in real-time cell/tissue culture monitoring with respect to engineering cellular microenvironments and controlling experimental conditions.
Diagnosis of Childhood Apraxia of Speech: A Systematic Review

This review employed to establish frequency usage of clinical markers and diagnostic tests for childhood apraxia of speech. following databases were searched for published papers between 1980 and 2015 looking for clinical studies used the six introduced tests for motor speech performance; Web of Science, Scopus, CINAHL and Pubmed. The review considered articles which for children at or below elementary school age with diagnosis of childhood apraxia of speech. Fifty seven articles met the inclusion criteria. Studies included other developmental motor speech disorders as well as childhood apraxia of speech were excluded. Twenty four articles excluded from the study and thirty three studies were reviewed. Twenty articles were in diagnostic tests and thirteen articles were in clinical markers. The Verbal Motor Production Assessment for Children is the most reliable diagnostic test for diagnosis of childhood apraxia of speech, and Inconsistency as well as vowel errors are the most frequent clinical markers.
Dialogue attributes that inform depth and quality of participation in course discussion forums

This paper describes work in progress to answer the question of how we can identify and model the depth and quality of student participation in class discussion forums using the content of the discussion forum messages. We look at two widely-studied frameworks for assessing critical discourse and cognitive engagement: the ICAP and Community of Inquiry (CoI) frameworks. Our goal is to discover where they agree and where they offer complementary perspectives on learning. In this study, we train predictive classifiers for both frameworks on the same data set in order to discover which attributes are most predictive and how those correlate with the framework labels. We find that greater depth and quality of participation is associated with longer and more complex messages in both frameworks, and that the threaded reply structure matters more than temporal order. We find some important differences as well, particularly in the treatment of messages of affirmation.
Skilled migration and sending economies. Testing brain drain and brain gain theories∗

Traditional models of brain drain stress its negative impact on the welfare and growth of sending economies, while new models introduce the possibility of brain gain through several channels (human capital, remittances, return migration or FDI and trade linkages). We test all these theories by estimating cross-country individual regressions for each channel, and a system of equations to assess the overall effect of brain drain on economic growth. Results suggest a negative effect on human capital stock, negligible on remittances (controlling for total migration) and a positive effect on trade and FDI. The net impact of skilled migration on economic growth remains ambiguous.
Construction of Amharic-arabic Parallel Text Corpus for Neural Machine Translation

Many automatic translation works have been addressed between major European language pairs, by taking advantage of large scale parallel corpora, but very few research works are conducted on the Amharic-Arabic language pair due to its parallel data scarcity. However, there is no benchmark parallel Amharic-Arabic text corpora available for Machine Translation task. Therefore, a small parallel Quranic text corpus is constructed by modifying the existing monolingual Arabic text and its equivalent translation of Amharic language text corpora available on Tanzile. Experiments are carried out on Two Long ShortTerm Memory (LSTM) and Gated Recurrent Units (GRU) based Neural Machine Translation (NMT) using Attention-based Encoder-Decoder architecture which is adapted from the open-source OpenNMT system. LSTM and GRU based NMT models and Google Translation system are compared and found that LSTM based OpenNMT outperforms GRU based OpenNMT and Google Translation system, with a BLEU score of 12%, 11%, and 6% respectively.