Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Achievable Latency and Cost SSD Latency Predictors (MittOS Model Inference)

—Cutting tail latencies at the millisecond level in internet services for good response times in data-parallel applications is possible by integrating MittOS, an OS/data center interface. Typically MittOS analyzes white-box information of the internals of devices such as SSD’s and decides if a given server can “fast reject” a service request. But commercial SSD’s have a black-box design, so MittOS researchers have developed machine learning models to determine if requests to commercial SSD’s can be rejected or not. When run on CPUs however, these models cannot predict in the time it takes an SSD to fully process a request, defeating MittOS’s fast-rejecting abilities. We demonstrate that ASICs such as the Efﬁcient Inference Engine (EIE) accelerate the prediction times of these MittOS models well within the time it takes an SSD to complete a request at minimal cost, cutting SSD tail latencies. EIE achieves 2.01 µ s inference latency while incurring minimal area costs (20.4 mm 2 ) and power costs (0.29 W). We show that integrating machine learning into the critical path of operating systems becomes cost-efﬁcient and within reason.
MICROPROPAGATION AND CELLULAR BEHAVIOUR CHANGES DURING in vitro FLOWERING OF Impatiens balsamina

In this study, the micropropagation of Impatiens balsamina was established from stem and shoot explants. The effects of GA3 and glutathione on the morphogenesis of this species were also investigated, in order to induce in vitro flowering. It was found that the optimum in vitro plant regeneration was achieved on MS medium supplemented with 1.0 mg L-1 GA3 and in vitro flowering was also obtained from the same medium after 4 weeks of culture. To understand cellular behavior during in vitro flowering, Mitotic Index (MI), chromosome counts, measurement of mean cell and nuclear areas, DNA measurements and ploidy levels were analyzed from in vivo plants, in vitro grown plants and plantlets that flowered in vitro. The chromosome count was the same for all, 2x=2n=14 or n=7. However, it was observed that in vitro flowering plants of Impatiens balsamina had the highest percentage of polyploid cells (30.7%), based on a histogram plotted by the AxioVision 4.7 software. It was found that plant growth regulators, especially GA3, increased the polyploidy level of the meristematic root cells.
Corticobasal Degeneration The Syndrome and the Disease

In 1967, Rebeiz, Kolodny, and Richardson described three patients with a progressive asymmetric akinetic-rigid syndrome and apraxia and labeled these cases as “corticodentatonigral degeneration with neuronal achromasia” (1,2). Additional reports on this disorder were almost nonexistent until the early 1990s. Over the past 10 yr, interest in this disorder has increased markedly. The nomenclature has also undergone evolution. The core clinical features that have been considered characteristic of the disorder include progressive asymmetric rigidity and apraxia, with other findings suggesting additional cortical (e.g., alien limb phenomena, cortical sensory loss, myoclonus, mirror movements) and basal ganglionic (e.g., bradykinesia, dystonia, tremor) dysfunction. The characteristic findings at autopsy have been asymmetric cortical atrophy, which is typically maximal in the frontoparietal regions, basal ganglia degeneration, and nigral degeneration. Microscopically, swollen neurons that do not stain with conventional hematoxylin/eosin (so-called ballooned, achromatic neurons) are found in the cortex. Abnormal accumulations of the microtubule-associated tau protein are found in both neurons and glia. In this chapter, the concepts and data relating to the corticobasal syndrome and corticobasal degeneration as presented in a recent review (3) are expanded.
WAVES 2019 , Vienna , Austria 1 Stochastic Models in Coordinate-Delay Synthetic Aperture Radar Imaging

We build stochastic models for synthetic aperture radar (SAR) imaging of targets that exhibit delayed scattering. Detection of scattering delay in SAR is hindered by the range-delay ambiguity, and the stochasticity of scattering adds uncertainty to the result. Using Monte-Carlo simulations, we obtain ensembles of coordinatedelay SAR images of instantaneous and delayed targets. Then, we explore the separation of likelihood-based metrics for those ensembles.
Special faster-RCNN for multi-objects detection

A series of neural networks called RCNN are playing a vital role in objects detection, as the most perfect one, Faster RCNN achieved an end-to-end object detection and made the detection times comparatively low but with high accuracy. In this work, we propose the following two changes to the original Faster RCNN model for multi-object detection: The first, we give 1800 ROI(Regions of Interest) comes from RPN to the RCNN network as input instead of 300, all the 1800 ROI are used to training the softmax classification and Bounding-box regression. The second, we traverse all xml files of every training image to get the number of marked objects and calculate the value of IOU for every marked objects, then we set a dynamic loss function to evaluation and optimization the Faster RCNN model by the two values of an image.
Synthesis , Crystal Structure , and Electrical Properties of a New Molybdylarsenate LiNa 5 K 3 Mo 11 As 3 O 45

LiNa 5 K 3 Mo 11 As 3 O 45 is a new inorganic compound. It was synthesized by a solid state method. The crystal structure has been studied by single crystal X-ray analysis. The R-values reached 2.8%. The title compound crystallizes in the triclinic system, space group P-1, with a = 10.550 (2) Å, b = 11.723 (2) Å, c = 17.469 (3) Å, α = 102.35 (3), β = 87.61 (2), and γ = 111.03 (3). The anionic unit [Mo 11 As 3 O 45 ] is formed by nine MoO 6 octahedra, two MoO 5 trigonal bipyramids, and three AsO 4 tetrahedra. The association of [Mo 11 As 3 O 45 ] units, running along [010], leads to a one-dimensional framework. Li, K, and Na are located in the space surrounding the anionic ribbons. This material was characterized by SEM microscopy, IR spectroscopy, and powder X-ray diffraction.The electrical conductivity was investigated from 528K to 673K by impedance complex followed by DSC spectroscopy.
Speech Recognition with Linear and Non-linear Amplification in the Presence of Industrial Noise

An abstract of the thesis of Marcia Ann Olson for the Master of Science in Speech Communication: Speech and Hearing Science presented July 10, 1996. Title: Speech Recognition with Linear and Non-Linear Amplification in the Presence of Industrial Noise. In order to help reduce hearing loss, the Occupational Safety and Health Administration regulates noise levels in work environments. However, hearing aids are the primary rehabilitative service provided for individuals with an occupational hearing loss. Very little is being done to monitor hearing aid use in the work environment. Noise which may be safe to an unaided ear can amplified to levels that are damaging to the ear when a hearing aid is being worn. However, it is necessary for some individuals to wear amplification in these noisy environments for safety reasons. As a consequence it is important that these individuals be able to understand speech in the presence of industrial noise while wearing amplification. The purpose of this study was to determine if there is a significant difference in speech intelligibility between linear hearing aids and different types of non-linear hearing aids when they are used in the presence of industrial noise. Twenty-four normal hearing subjects were selected for this study. Each subject was ask to identify words in four CID W-22 lists which had been recorded through a linear hearing aid and two different non-linear hearing aids. Test results showed significantly better word· recognition for the linear in quiet condition over all other conditions. Significantly higher scores were obtained for the TILL condition than were obtained for the Linear in noise and the BILL condition. These preliminary results suggest that an individual wearing amplification in a noisy work environment would benefit with a TILL circuit. The TILL circuit would provide better speech intelligibility in this type of environment. Therefore, providing a safer work environment for the
Mining YouTube-Learning action classes from unlabeled videos

Action recognition has become an important topic in academic and industrial research with most approaches so far relying on fully supervised training. Thus, for any action class to be trained, preclipped videos or a label with temporal information is needed. But the acquisition of such data is very time consuming as it needs some kind of human supervision, e.g. in form of Mechanical Turk workers. This puts a natural limit to the size of current benchmarks as well as to the transfer of current methods to real life application as collecting hand-annotated training data is often not feasible here. To address this issue, we propose a new benchmark focusing on the problem of learning actions from real-live videos without human supervision. To this end we follow the conventions of a real life setting and start with the target videos, a densely annotated set of 250 cooking videos with realistic action labels as they occur in the instructions given in the video or in textual recipes. The resulting annotations comprise 512 action classes with 10412 labeled action instances. As the annotation of a training set for this amount of classes would be very time consuming, we mine the respective training clips automatically. To this end, we collect a set of 191K samples from 21K videos by searching class related attributes on YouTube using the transcripts of the audio stream to generate weak labels from the training examples as e.g. shown in Figure 1. This approach follows and continues the idea of earlier approaches such as [5, 4, 1] and extends their ideas to large scale real live conditions. We benchmark the proposed dataset with respect to current features and architectures showing the challenging problems as well as the the benefits of this type of data.
Bornholmsk Natural Language Processing: Resources and Tools

This paper introduces language processing resources and tools for Bornholmsk, a language spoken on the island of Bornholm, with roots in Danish and closely related to Scanian. This presents an overview of the language and available data, and the first NLP models for this living, minority Nordic language. Sammenfattnijng på borrijnholmst: Dæjnna artikkelijn introduserer natursprågsresurser å varktoi for borrijnholmst, ed språg a dær snakkes på ön Borrijnholm me rødder i danst å i nær familia me skånst. Artikkelijn gjer ed âuersyn âuer språged å di datan som fijnnes, å di fosste NLP modællarna for dætta læwenes nordiska minnretâlsspråged.
Why Aren’t All Predictive Coding Networks Generative?

Predictive coding (PC) networks are bidirectional networks that have been shown to approximate backprop, and can be trained on discriminative tasks. But even though “predictive” is in the name, PC classifier networks have not been convincingly shown to generate input samples. Simply clamping the desired class vector and running the network to an equilibrium state does not generate a sensible state in the input layer. The problem is ill-posed; it has an infinite number of solutions. However, we show that a linear network can reconstruct sensible inputs by imposing a minimum-norm constraint, which can be done using simple weight decay. It also works on nonlinear networks, as demonstrated on MNIST.
Demonstration of Low Power Stream Processing Using a Variable Pipelined CGRA

VPCMA (Variable Pipelined Cool Mega Array) is a low power CGRA (Coarse-Grained Reconfigurable Architecture) which we previously proposed in [1]. CC-SOTB2 is a real chip implementation of the VPCMA using Renesas 65-nm SOTB technology [2]. In this demonstration, we will show the power consumption of the CC-SOTB2 while performing a real image processing.
Finite Differences Schemes for Pricing of European and American Options

Starting with the Black-Scholes model, we use finite differences schemes to value European and American options. Two approaches, resulting from transformations of the Black-Scholes equation, will be considered. An application to real data, using the Local Volatility Model to determine a volatility surface consistent with the Black-Scholes equation will be presented. Applying this local volatility surface to the finite difference method, it is possible to calibrate the Black-Scholes model, obtaining a reasonable approximation of the real market values.
BEYOND VERBAL SCALES: MEASUREMENT OF EMOTIONS IN ADVERTISING EFFECTIVENESS RESEARCH

Yearbook of Marketing and Consumer Research, Vol.6 (2008) GfK ABSTRACT Emotions are a major topic in marketing science and practice alike (Kroeber-Riel/ Weinberg/Gröppel-Klein 2008, p. 99). B2B, B2C, and retail brands need to be emotionally charged to ensure differentiation from competitor brands. But how can emotions be reliably measured? Only valid and meaningful measurement of emotions will allow the emotional impact of marketing activities to be optimized in the future. The Institute for Consumer and Behavioural Research at Saarland University together with brand and methodology specialists at GfK joined forces to develop a picturebased scale that is applicable at various brand touch points: it is called GfK EMO Sensor. This article summarizes the theoretical background and the development of the methodological approach. It concludes by presenting initial applications in advertising effectiveness research and their practical implications for marketing.
Efficient High Utility Itemset Mining using extended UP Growth on Educational Feedback Dataset

High utility itemsets refer to the sets of items with high utility like profit in a database, and efficient mining of high utility itemsets plays an important role in many real life applications and is an important research issue in data mining area. In recent years, the problems of high utility pattern mining become one of the most important research areas in data mining. The existing high utility mining algorithm generates large number of candidate itemsets, which takes much time to find utility value of all candidate itemsets. In this paper we are implementing a data structure that stores the utility related to the item and using this data structure we are reducing time and space complexity of UP Growth and UP Growth+ Algorithms. Various Standard and synthetic datasets are used with Educational feedback data set. An algorithm is proposed to find set of high utility itemset which avoids the candidate itemsets generation. KeywordsUtility, Utility Information Record, Effective High Utility Itemset Mining.
Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation

Counterfactual reasoning is an important paradigm applicable in many fields, such as healthcare, economics, and education. In this work, we propose a novel method to address the issue of \textit{selection bias}. We learn two groups of latent random variables, where one group corresponds to variables that only cause selection bias, and the other group is relevant for outcome prediction. They are learned by an auto-encoder where an additional regularized loss based on Pearson Correlation Coefficient (PCC) encourages the de-correlation between the two groups of random variables. This allows for explicitly alleviating selection bias by only keeping the latent variables that are relevant for estimating individual treatment effects. Experimental results on a synthetic toy dataset and a benchmark dataset show that our algorithm is able to achieve state-of-the-art performance and improve the result of its counterpart that does not explicitly model the selection bias.
Development of Learning Media Based on Adobe Flash Cs3 Program at the Lesson “Introduction Economic and Business” in Public Vocational High School(SMKN) 5 Jambi City

Abstract: Learning in SMKN 5 Jambi city took place with conventional methods suspected causes of students tend to be passive in learning. For that sought the effort to change the method of learning that can make students active. The research aims to: 1) To study the procedure of developing instructional media based on adobe flash CS3 program. 2) To study adobe flash CS3 program-based learning media that is feasible to use. Research type is research and development with reference to Lee and Owens development model that is ADDIE (Analysis, Design, Develpoment, Impelementation and Evaluationi). The subjects of the study were the students of class X in SMKN 5 Jambi city totaling 22 students. Instruments used: Observation, questionnaires, evaluation of media experts, evaluation of materials experts, media evaluation by teachers of study, media evaluation by students, and pretest and posttest questions. Research findings obtained: first: the analysis phase. Student needs analysis on learning media, second: design stage, making of media design that is storyboard, third: development stage, manufacture of media product, in form of typing and revision from experts, fourth: implementation phase, product trial, fifth: evaluation phase, based on questionnaires of media experts, materials, teachers and students can be concluded that the learning media has decent criteria. Result of research that, 1) The development of instructional media based on adobe flash CS3 program on economic and business introduction subject, done systematically by fulfilling the development model of ADDIE (analysis, design, development, implementation, and evaluation). 2) Learning media based on CS3 developed adobe flash program is feasible to be used and can improve the effectiveness of student learning on economic and business introduction subjects. From result of validation test of instructional media conclude: 1) expert validation result that is validation of content or material obtained by 87% very decent category, 2) result of media expert validation obtained by 85% decent category, 3) result of teacher study field is obtained equal to 88 % category is very feasible, and 4) the results of the assessment of the students obtained by 90% very feasible category. Development of instructional media of adobe flash program is done systematic model of ADDIE and Development of learning media can improve result of learning post-test resultis 73%. Keywords: Learning media, adobe flash CS3 program.
Fast saccades toward numbers : Simple number comparisons

Visual psychophysicists have recently developed tools to measure the maximal speed at which the brain can accurately carry out different types of computations (H. Kirchner & S. J. Thorpe, 2006). We use this methodology to measure the maximal speed with which individuals can make magnitude comparisons between two single-digit numbers. We find that individuals make such comparisons with high accuracy in 306 ms on average and are able to perform above chance in as little as 230 ms. We also find that maximal speeds are similar for “larger than” and “smaller than” number comparisons and in a control task that simply requires subjects to identify the number in a number–letter pair. The results suggest that the brain contains dedicated processes involved in implementing basic number comparisons that can be deployed in parallel with processes involved in low-level visual processing.
CAPACITY AND SECURITY ENHANCEMENT IN AUDIO BASED STEGANOGRAPHY USING RANDOM LOCATION TECHNIQUE

Audio steganography has attracted a great attention owing to secure communication for commercial and military purposes due to larger size of their files. Among these, audio based steganography has more potential to conceal information because audio files are larger in size as compare to other media. For audio steganography, mostly Least Significant Bit (LSB) techniques are used to hide the secrete data. LSB techniques hide data in limited capacity. Also, detection of data in lower bits is much easier, thus these techniques has less security. In this paper, audio based steganography technique is suggested. The proposed technique conceals data at the random location of audio signal by using chaotic based technique. The proposed technique enables us to hide the data in lower as well as higher significant bits of the audio data; that increases its capacity. It also enhances the security of the secrete data by placing at random location of the audio data. It believes that the given technique can be useful for secure communication of large amount of secrete data.
Entropy as a Proxy for Gap Complexity in Open Cloze Tests

This paper presents a pilot study of entropy as a measure of gap complexity in open cloze tests aimed at learners of English. Entropy is used to quantify the information content in each gap, which can be used to estimate complexity. Our study shows that average gap entropy correlates positively with proficiency levels while individual gap entropy can capture contextual complexity. To the best of our knowledge, this is the first unsupervised information-theoretical approach to evaluating the quality of cloze tests.
Is the future of work childless? Self-employment and fertility

The growth of self-employment and in particular gig work may explain part of the declining fertility rates observed in many countries. This study examines this question drawing on longitudinal data to compare women’s fertility, proxied by maternity leave uptake, when self-employed or wage workers. It considers the case of Portugal, which allows to focus on structural aspects of work types, as fertility-related social protection there does not discriminate between self-employment and wage work. Results indicate that there are no statistically significant differences in fertility between employees and self-employed women. These findings highlight the importance of social protection for the self-employed, at least as far as their fertility is concerned.
Retrieval of Relevant Graphs 1

Graph mining is one of the novel approaches for mining the graph structure. There are different graph mining approaches which can be used to solve the graph retrieval problem. The graph retrieval is the process of finding relevant graphs, according to the user requests, from a collection of graphs. A swarm intelligence approach, which is a form of artificial intelligence based on the collective behavior of decentralized and self-organized systems, can be used in the graph retrieval problem. Many complex and real problems can be solved using swarm optimization, which is a bio-inspired method. The power of graph mining techniques can be exploited for extracting relevant knowledge, which can be used later by the swarms. Then, the swarms can use the extracted knowledge to explore the solution’s space.The performance mainly depends on the run-time and the quality of returned graphs.
Summarization Evaluation meets Short-Answer Grading

Summarization Evaluation and Short-Answer Grading share the challenge of automatically evaluating content quality. Therefore, we explore the use of ROUGE, a well-known Summarization Evaluation method, for Short-Answer Grading. We ﬁnd a reliable ROUGE parametrization that is robust across corpora and languages and produces scores that are signiﬁcantly correlated with human short-answer grades. ROUGE adds no information to Short-Answer Grading NLP-based machine learning features in a by-corpus evaluation. However, on a question-by-question basis, we ﬁnd that the ROUGE Recall score may outperform standard NLP features. We therefore suggest to use ROUGE within a framework for per-question feature selection or as a reliable and reproducible baseline for SAG.
OmniPush : accurate , diverse , real-world dataset of pushing dynamics with RGBD images

Large and diverse datasets have driven recent progress in fields such as computer vision and natural language processing. We currently lack the equivalent richness for physical understanding. There are several large datasets (Yu et al., 2016; Levine et al., 2016; Calli et al., 2015), but they either only contain raw annotated video under highly varying conditions or only involve a small set of objects.