Miracle-dz/UniFAR-FTUs huggingface dataset:
Key Instance Detection in Multi-Instance Learning

The goal of traditional multi-instance learning (MIL) is to predict the labels of the bags, whereas in many real applications, it is desirable to get the instance labels, especially the labels of key instances that trigger the bag labels, in addition to getting bag labels. Such a problem has been largely unexplored before. In this paper, we formulate the Key Instance Detection (KID) problem, and propose a voting framework (VF) solution to KID. The key of VF is to exploit the relationship among instances, represented by a citer kNN graph. This graph is dierent from commonly used nearest neighbor graphs, but is suitable for KID. Experiments validate the eectiveness of VF for KID. Additionally, VF also outperforms state-of-the-art MIL approaches on the performance of bag label prediction.
The effect of line of sight propagation on performance of amplify-and-forward relay networks

In this paper we analyze the effect of line of sight signal propagation on performance of amplify-and-forward relay system with fixed gain amplification. Communication channel typical for such propagation scenario is described with Ricean fading statistic. Since this type of communication scenario is not widely explored in the literature, despite the fact that it is common in practice, we develop complete analytical model for performance evaluation of the considered system. Appropriate simulation model is also developed, which is used to confirm the validity of the numerically obtained results. Finally, we determine the level of system performance improvement, or equivalently the signal-to-noise ratio gain, due to the existence of line of sight signal component between the network nodes.
The secret lives of ebooks: A paratextual analysis illuminates a veil of usage statistics

This study applies the method of paratextual analysis to six electronic books, or ebooks, in an academic library collection at a small liberal arts college. Two books are selected from each of three platforms: ebrary, EBSCO, and SpringerLink. The characteristics of each book are described, including design and readership, as well as 2 years of usage statistics from the specific library, and altmetrics where available. The paratextual study leads to a closer investigation of the usage statistics themselves and concludes that despite industry standards, they are not calculated consistently across vendor platforms and that while these data are invisible to researchers outside of the library, there are also essential elements that librarians mistakenly take at face value when comparing ebook usage from multiple vendors.
Neural Correlates of Associative Memory in the Elderly: A Resting-State Functional MRI Study

The neural correlates of associative memory in healthy older adults were investigated by examining the correlation of associative memory performance with spontaneous brain oscillations. Eighty healthy older adults underwent a resting-state functional MRI and took a paired-associative learning test (PALT). Correlations between the amplitude of low-frequency fluctuations (ALFF) as well as fractional ALFF (fALFF) in the whole brain and PALT scores were calculated. We found that spontaneous activity as indexed by both ALFF and fALFF in the parahippocampal gyrus (PHG) was significantly positively correlated with associative memory performance, suggesting that the PHG plays a critical role in associative memory in older people.
Genetic Association for Renal Traits among Participants of African Ancestry Reveals New Loci for Renal Function

Chronic kidney disease (CKD) is an increasing global public health concern, particularly among populations of African ancestry. We performed an interrogation of known renal loci, genome-wide association (GWA), and IBC candidate-gene SNP association analyses in African Americans from the CARe Renal Consortium. In up to 8,110 participants, we performed meta-analyses of GWA and IBC array data for estimated glomerular filtration rate (eGFR), CKD (eGFR <60 mL/min/1.73 m2), urinary albumin-to-creatinine ratio (UACR), and microalbuminuria (UACR >30 mg/g) and interrogated the 250 kb flanking region around 24 SNPs previously identified in European Ancestry renal GWAS analyses. Findings were replicated in up to 4,358 African Americans. To assess function, individually identified genes were knocked down in zebrafish embryos by morpholino antisense oligonucleotides. Expression of kidney-specific genes was assessed by in situ hybridization, and glomerular filtration was evaluated by dextran clearance. Overall, 23 of 24 previously identified SNPs had direction-consistent associations with eGFR in African Americans, 2 of which achieved nominal significance (UMOD, PIP5K1B). Interrogation of the flanking regions uncovered 24 new index SNPs in African Americans, 12 of which were replicated (UMOD, ANXA9, GCKR, TFDP2, DAB2, VEGFA, ATXN2, GATM, SLC22A2, TMEM60, SLC6A13, and BCAS3). In addition, we identified 3 suggestive loci at DOK6 (p-value = 5.3×10−7) and FNDC1 (p-value = 3.0×10−7) for UACR, and KCNQ1 with eGFR (p = 3.6×10−6). Morpholino knockdown of kcnq1 in the zebrafish resulted in abnormal kidney development and filtration capacity. We identified several SNPs in association with eGFR in African Ancestry individuals, as well as 3 suggestive loci for UACR and eGFR. Functional genetic studies support a role for kcnq1 in glomerular development in zebrafish.
NM2H: Design and Implementation of NoSQL Extension for HDFS Metadata Management

As a distributed MapReduce framework, Hadoop has been widely adopted in big data processing, in which HDFS (Hadoop Distributed File System) is mostly used for data storage. Though the single master architecture of HDFS simplifies the design and implementation, it suffers from issues such as SPOF (Single Point Of Failure) and scalability, which further may become performance bottleneck. To address these problems, this paper proposes NM2H, a NoSQL based metadata management approach for HDFS. NM2H separates the storage and query of metadata in contrast to the traditional architecture which mixed them up, and manages to keep the interfaces among the metadata service, Data Nodes and clients unchanged through a novel mapping mechanism between the original metadata structures to NoSQL documents. Therefore, the new approach can not only take advantages of NoSQL's better scalability and fault tolerance, but also deliver transparency to client applications, in which way existing programs can run on the new architecture without any modification. The prototype of NM2H was designed and implemented with widely adopted NoSQL system MongoDB. Extensive performance evaluation was conducted and the experimental results indicated the improvement of NM2H, while the overhead introduced was acceptable.
Neuro-remodeling via Backpropagation of Utility

Backpropagation of utility is one of the many methods for neuro-control. Its goal is to provide a series of control signals to maximize a utility function over time. In this paper, we demonstrate how to use the basic backpropagation and backpropagation through time algorithms as fundamental building blocks for backpropagation of utility. We also explore the parallel implementation of the algorithm on Intel's Paragon computer. The backpropagation of utility algorithm is composed of three subnetworks, the Action network, Model network, and an Utility network or function. Each of these networks includes a feedforward and a feedback component. A ow chart for the interaction of these components is included. To further illustrate the algorithm, we use backpropagation of utility for the control of a simple one-dimensional planar robot. We found that the success of the algorithm hinges upon a suucient emulation of the dynamic system by the Model network. Furthermore, the execution time can be improved through pattern-partitioning on multiple processors.
Robust Deep-Learning-Based Road-Prediction for Augmented Reality Navigation Systems at Night

This paper proposes an approach that predicts the road course from camera sensors lever-aging deep learning techniques. Road pixels are identified by training a multi-scale convolutional neural network on a large number of full-scene-labeled night-time road images including adverse weather conditions. A framework is presented that applies the proposed approach to longer distance road course estimation, which is the basis for an augmented reality navigation application. In this framework long range sensor data (radar) and data from a map database are fused with short range sensor data (camera) to produce a precise longitudinal and lateral localization and road course estimation. The proposed approach reliably detects roads with and without lane markings and thus increases the robustness and availability of road course estimations and augmented reality navigation. Evaluations on an extensive set of high precision ground truth data taken from a differential GPS and an inertial measurement unit show that the proposed approach reaches state-of-the-art performance without the limitation of requiring existing lane markings.
Performance of an Improved MC-CDMA System Based on DT-OCWDM and Quasi-Orthogonal Matrix

In this paper, we propose a new improved MC-CDMA system which combines spreading codes from Quasi-Orthogonal Matrix with Orthogonal Complex Wavelet Division Multiplexing (OCWDM). The system is implemented by complex wavelet filters which are able to lower computational complexity and increase flexibility. Novel spreading codes from Quasi-Orthogonal Matrix can be expanded randomly. It can increase user number hugely in system. Improved MC-CDMA named OCWDM-CDMA has much higher frequency spectrum efficiency and high data rate than conventional MC-CDMA. The system also has better BER performance in Gaussian channel than conventional system for much more users.
Smad-Dependent Recruitment of a Histone Deacetylase/Sin3A Complex Modulates the Bone Morphogenetic Protein-Dependent Transcriptional Repressor Activity of Nkx3.2

ABSTRACT We have previously shown that Nkx3.2, a transcriptional repressor that is expressed in the sclerotome and developing cartilage, can activate the chondrocyte differentiation program in somitic mesoderm in a bone morphogenetic protein (BMP)-dependent manner. In this work, we elucidate how BMP signaling modulates the transcriptional repressor activity of Nkx3.2. We have found that Nkx3.2 forms a complex, in vivo, with histone deacetylase 1 (HDAC1) and Smad1 and -4 in a BMP-dependent manner. The homeodomain and NK domain of Nkx3.2 support the interaction of this transcription factor with HDAC1 and Smad1, respectively, and both of these domains are required for the transcriptional repressor activity of Nkx3.2. Furthermore, the recruitment of an HDAC/Sin3A complex to Nkx3.2 requires that Nkx3.2 interact with Smad1 and -4. Indeed, Nkx3.2 both fails to associate with the HDAC/Sin3A complex and represses target gene transcription in a cell line lacking Smad4, but it performs these functions if exogenous Smad4 is added to these cells. While prior work has indicated that BMP-dependent Smads can support transcriptional activation, our findings indicate that BMP-dependent Smads can also potentiate transcriptional repression, depending upon the identity of the Smad-interacting transcription factor.
Gaining 10x in energy efficiency in the next decade in consumer products

In the last two decades, Semiconductors have successively enabled disruptive market expansions in the PC, consumer and Mobility markets. Electronics have now become an integral part of consumer lives where new product introductions are cadenced by recurring yearly events (CES, IBC, …). This cadence drives the complete electronics industry to renew product portfolios at the same impressive rate. Microelectronics has been the key driver of this evolution, by tracking Moore's law for more than 30 years [1]. In the next two decades, Semiconductors will enable further discontinuities of high societal and economic value. A major roadblock for our industry to enable these next decades of innovation, is to identify and implement perennial solutions towards the ultimate energy efficiency of consumer products [2]. Several paths are to be considered jointly, and will be briefly discussed in the following paragraphs [3].
Pedestrian Detection for UAVs Using Cascade Classifiers with Meanshift

In this paper, we propose an algorithm for pedestrian detection focusing on UAV applications. Our proposal is based on a combination of Haar-LBP features with Adaboost for the training process, and Meanshift for improving the performance of the pedestrian detector. We mount a dataset with images captured from surveillance cameras. Our dataset and algorithm are evaluated and compared with other approaches from the literature.
Oral Contraceptive Use May Attenuate Menstrual Cycle-Induced Ventilatory Changes in Endurance Trained Runners

The relationship between two menstrual cycle phases and cardio respiratory fitness in athletes on oral contraceptives and not on oral contraceptives was assessed in a pilot study. Twelve endurance-trained college cross country runners underwent maximal oxygen consumption treadmill testing, during the late follicular/ovulatory phase and late luteal/premenstrual phase of their menstrual cycle. No significant differences were detected in maximal oxygen consumption between the oral contraceptive group and non-oral contraceptive group or between the two phases. There was a non-significant phase effect in the non-oral contraceptive group with maximal ventilation averaging 90.5±14.6 L/min during the late follicular/ovulatory phase and decreasing to 79.1±9.8 L/min during the late luteal/premenstrual phase (p=0.102). Maximal ventilation was significantly lower by 15.6% between oral contraceptive and non-oral contraceptive users during the late luteal/premenstrual phase (93.7+7.2 L/min vs 79.1+9.8 L/min, p=0.014, respectively). There was no significant phase effect in the oral contraceptive group for maximal ventilation. While oral contraceptive use was associated with an attenuation of the negative effect of the late luteal/premenstrual phase on ventilator performance, there was no effect on maximal oxygen consumption.
LIRICS Deliverable <D1.3> <Quality Assessment>

Deliverable <D1.3> <Quality Assessment> Project reference number e-Content-22236-LIRICS Project acronym LIRICS Project full title Linguistic Infrastructure for Interoperable Resource and Systems Project contact point Laurent Romary, INRIA-Loria 615, rue du jardin botanique BP101. 54602 Villers lès Nancy (France) romary@loria.fr Project web site http://lirics.loria.fr EC project officer Erwin Valentini
Single Supply, Micropower Toxic Gas Detector Using an Electrochemical Sensor

Rev. A Circuits from the LabTM circuits from Analog Devices have been designed and built by Analog Devices engineers. Standard engineering practices have been employed in the design and construction of each circuit, and their function and performance have been tested and verified in a lab environment at room temperature. However, you are solely responsible for testing the circuit and determining its suitability and applicability for your use and application. Accordingly, in no event shall Analog Devices be liable for direct, indirect, special, incidental, consequential or punitive damages due to any cause whatsoever connected to the use of any Circuits from the Lab circuits. (Continued on last page) One Technology Way, P.O. Box 9106, Norwood, MA 02062-9106, U.S.A. Tel: 781.329. 4700 www.analog. com Fax: 781.461. 3113 ©2012–2013 Analog Devices, Inc. All rights reserved. EVALUATION AND DESIGN SUPPORT
DOCUMENTATION AND ANALYSIS OF CULTURAL HERITAGE BY PHOTOGRAMETRIC METHODS AND GIS: A CASE STUDY

Turkey is one of the rare countries with a rich cultural heritage. We have been left with a historic heritage covering the centuries from pre-historic to modern times, which have left the marks of their civilizations on the lands of Anatolia. Therefore these riches should be preserved and left to the next generations with the care such treasures deserve. We have to be aware of the responsibility of leaving these treasures to the next generations as well as displaying them to the tourists. The saving methods of these treasures are supported by modern technology. Using these methods enables us to increase the efficiency and impact of the saving process. Using the advanced technologies in the disciplines other than architecture and by cooperation among the different fields, the future of cultural heritage can be guaranteed in order to save the historical objects and areas. It is necessary to organize the plans and works covering the necessary technical documents of the objects and lands to preserve these objects and lands. With the help of these documents, it will be possible to reach the desired information for the works carried on these objects and lands. The information system generated by the use of digital photogrammetry and Geographical Information System together will enable any person interested in saving the cultural heritage to reach the necessary data. As mentioned above, integration of photogrammetry and GIS leads to the efficient use of data, analysis and presentation opportunities, which are very important for saving the cultural heritage. The aim of this study is to use this technology for the photogrammetric documentation of the cultural heritage and to enable the use of the documentation by using GIS.
Intelligent Content-Based Privacy Assistant for Facebook

Although most online social networks now offer fine-grained controls of information sharing, these are rarely used, both because their use imposes additional burden on the user and because there are too many control settings for an average user to handle. To mitigate this problem, we have developed an Intelligent Privacy Assistant for Face book that partially automates the assignment of sharing permissions, taking into account the content of the information published and user's high-level sharing policies. The Assistant uses a novel social web privacy language, employs named entity recognition algorithms to annotate sensitive parts of published information and an answer set programming system to evaluate user's privacy policies and determine the list of safe recipients. On a test scenario, the Assistant reached 73.8% and 95.2% performance in correctly determining safe and unsafe recipients, respectively.
Sound Event Detection in Multisource Environments Using Source Separation

This paper proposes a sound event detection system for natural multisource environments, using a sound source separation front-end. The recognizer aims at detecting sound events from various everyday contexts. The audio is preprocessed using non-negative matrix factorization and separated into four individual signals. Each sound event class is represented by a Hidden Markov Model trained using mel frequency cepstral coefficients extracted from the audio. Each separated signal is used individually for feature extraction and then segmentation and classification of sound events using the Viterbi algorithm. The separation allows detection of a maximum of four overlapping events.The proposed system shows a significant increase in event detection accuracy compared to a system able to output a single sequence of events. Index Terms: sound event detection, sound source separation, non-negative matrix factorization
Multicomponent decompositions for a sample of S0 galaxies

We have estimated the bulge-to-total (B/T) light ratios in the Ks band for a sample of 24 SO, S0/a and Sa galaxies by applying a two-dimensional multicomponent decomposition method. For the disc an exponential function is used, the bulges are fitted by a Sersic R 1/n function and the bars and ovals are described either by a Sersic or a Ferrers function. In order to avoid non-physical solutions, preliminary characterization of the structural components is made by inspecting the radial profiles of the orientation parameters and the low azimuthal wavenumber Fourier amplitudes and phases. In order to identify also the inner structures, unsharp masks were created: previously undetected inner spiral arms were found in NGC 1415 and marginally in NGC 3941. Most importantly, we found that S0s have a mean (B/T) K ratio of 0.24 ± 0.11, which is significantly smaller than the mean (B/T) R = 0.6 generally reported in the literature. Also, the surface brightness profiles of the bulges in S0s were found to be more exponential-like than generally assumed, the mean shape parameter of the bulge being = 2.1 ± 0.7. We did not find examples of barred S0s lacking the disc component, but we found some galaxies (NGC 718, 1452 and 4608) having a non-exponential disc in the bar region. To our knowledge, our study is the first attempt to apply a multicomponent decomposition method for a moderately sized sample of early-type disc galaxies.
WP-AD 2009-04 Employee t ypes and e ndogenous o rganizational d esign : An e xperiment *

When managers are sufficiently guided by social preferences, incentive provision through an organizational mode based on informal implicit contracts may provide a cost-effective alternative to a more formal mode based on explicit contracts and monitoring. This paper reports the results from a laboratory experiment designed to test whether organizations make full effective use of the available preference types within their work force when drafting their organizational design. Our main finding is that they do not do so; although the importance of social preferences is recognized by those choosing the organizational mode, the significant impact managers' preferences have on the behavior of workers in the organization seems to be overlooked. *****
Towards Estimating Users’ Opinion Strength in Forum Texts Regarding Governmental Decisions

The growth of Web 2.0 has facilitated interactive information sharing and interoperability on the World Wide Web, allowing users not only to retrieve information, but also the opportunity to interact or articulate their opinions on different topics. Numerous works on various social and political objectives devoted to the formation of open Internet sources of information monitoring systems, in order to prepare digests and reports on keywords as well as thematic queries regarding opinions on government decisions. Moreover, analysis of rubrics associations as well as primary semantic and statistical interpretation of the texts is usually carried out. Therefore experts can accomplish an intellectual data analysis and forecast. On the other hand, it is rather difficult to get punctual predicts and estimate sufficiency forum users’ opinion strength. In this work we present a methodology in order to automatically estimate the strength of users’ opinions on text forums regarding governmental decisions. According to our methodology, quantitative features are automatically extracted from forum posts and then passed to a Support Vector Machine based classifier where the users’ opinion strength is estimated. The proposed methodology has been validated in real data. The initial experimental results are presented. Our schema could also be used in a wide range of applications such as: scenario analysis of important events for political, social, and commercial tasks, public opinion monitoring, forecasting of tendencies of public opinion and marketing research.