Miracle-dz/UniFAR-FTUs huggingface dataset:
Cache-oblivious planar orthogonal range searching and counting

We present the first cache-oblivious data structure for planar orthogonal range counting, and improve on previous results for cache-oblivious planar orthogonal range searching.Our range counting structure uses O(N log2 N) space and answers queries using O(logB N) memory transfers, where B is the block size of any memory level in a multilevel memory hierarchy. Using bit manipulation techniques, the space can be further reduced to O(N). The structure can also be modified to support more general semigroup range sum queries in O(logB N) memory transfers, using O(N log2 N) space for three-sided queries and O(N log22 N/log2 log2 N) space for four-sided queries.Based on the O(N log N) space range counting structure, we develop a data structure that uses O(N log2 N) space and answers three-sided range queries in O(logB N+T/B) memory transfers, where T is the number of reported points. Based on this structure, we present a general four-sided range searching structure that uses O(N log22 N/log2 log2 N) space and answers queries in O(logB N + T/B) memory transfers.
GPP vs DSP: A Performance/Energy Characterization and Evaluation of Video Decoding

Mobile devices such as smart-phones and tablets are increasingly becoming the most important channel for delivering end-user Internet traffic especially multimedia content. One of the most popular use of these terminals is video streaming. In this type of application, video decoding is considered as the most compute and energy intensive part. Some specific processing units, such as dedicated Digital Signal Processors (DSPs), are added to those devices in order to optimize the performance and energy consumption. In this context, the objective of this paper is to give a comprehensive and comparative study of the performance and energy consumption of video decoding application on embedded heterogeneous platforms containing a GPP and a DSP. To achieve this goal, a performance and energy characterization methodology for H.264/AVC video decoding is proposed. This methodology considers a large set of video coding parameters and operating clock frequencies to reflect different execution scenarios ranging from low-quality video decoding on low-end mobile phones to high-quality video decoding on tablets. The obtained results revealed that the best performance-energy trade-off highly depends on the required video bit-rate and resolution. For instance, the GPP can be the best choice in many cases due to a significant overhead in DSP decoding which may represent 30% of the total decoding energy in some cases. Some explanations about the obtained performance and overheads are given. Finally, guidelines on which processing element to choose according to video properties are also proposed.
Constrained clustering with Minkowski Weighted K-Means

In this paper we introduce the Constrained Minkowski Weighted K-Means. This algorithm calculates cluster specific feature weights that can be interpreted as feature rescaling factors thanks to the use of the Minkowski distance. Here, we use an small amount of labelled data to select a Minkowski exponent and to generate clustering constrains based on pair-wise must-link and cannot-link rules. We validate our new algorithm with a total of 12 datasets, most of which containing features with uniformly distributed noise. We have run the algorithm numerous times in each dataset. These experiments ratify the general superiority of using feature weighting in K-Means, particularly when applying the Minkowski distance. We have also found that the use of constrained clustering rules has little effect on the average proportion of correctly clustered entities. However, constrained clustering does improve considerably the maximum of such proportion.
Optimal design of hybrid FEC/ARQ schemes for real-time applications in wireless networks

Multimedia data transmission over wireless networks often suffers from delay, jitter and packet loss. The main problem to implement a wireless network is the high and variable bit error rate in the radio link, making it necessary to use an additional error control mechanism. The traditional techniques for error control are ARQ (Automatic Repeat Request) and FEC (Forward Error Correction). The ARQ technique works better than FEC, if the bit error rate in the channel is low, and worse, if the bit error rate is high. Thus, hybrid ARQ/FEC techniques can improve the performance. In this paper, a novel hybrid ARQ/FEC scheme is analyzed in an environment of real-time applications. We focus in a simple FEC scheme which has been standardized by IETF [11]. It consists in adding a low quality copy of the original packet n to the packet n + ø. If packet n is lost in the network, it can be correctly recovered if packet n +ø is correctly received. The effect of link layer (LL) parameters like the number of LL transmission attempts, offset ø , LL packet size, and FEC code rate on the TFRC (TCP Friendly Rate Control)and UDP performance is evaluated. We show that the combination of FEC and ARQ reduces the number of retransmissions and the time spent by a packet in the buffer at the output of the wireless link. We validate our analytical results using ns-2 [9]simulation
Children Learning in Groups, and Other Trends in Elementary ad Early Childhood Education.

This publication includes two papers by John Hollifield, three ERIC Digests and two ERIC resource lists on group learning, and a reprint of an ERIC search on cooperative learning in elementary and middle schools. The two papers are titled: (1) "Cooperative Learning in Elementary Schools: From Supplemental Instructional Practice to Schoolwide Restructuring"; and (2) "Trends in Elementary and Early Childhood Education. " The first paper briefly describes a number of cooperative learning processes and examines the increasing pattern of use of cooperative learning processes in elementary schools over the last decade, as exemplified by the Johns Hopkins University Student Team Learning Processes. The processes are described, the research concerning them is summarized, and the existing knowledge about the use of the processes in elementary schools is discussed. Preceeded by a discussion of the school reform movement and demographic factors delineating the population of children, the trends identified and briefly discussed in the second paper are those currently influencino kindergarten, preschool education, child care, and elementary education. Digests and resource lists on ability grouping, cooperative learning strategies in children, mixed-age grouping and cooperative learning, and classroom and group interaction, and an ERIC search reprint containing 39 citations and abstracts, are included. (RH) ***********************************M***************************fi***** Reproductions supplied by EDRS are the best that can be made from the original document. ***************************A******************************************* C11 0 Minor changes have been made to improve reproduction Quality. is points ot view or optnionsstated In this dabment do not necessarily represent official P°r'cYning in":(2) Children tEeR1 position 2 Groups U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL CENTER ( RESOURCES ERIC) INFORMATION document has been reproduced as ended from the person or organization originating it. And Other Trends in Elementary and Early Childhood Education
Context shifting for effective search over large knowledge bases

The problem of searching large knowledge bases is becoming an important facet of the current web of steadily proliferating semantic content. By pushing the notion of a context for partitioning large knowledge bases, performance of search is improved by narrowing the search space to a context of interest. On the other hand, by restricting the search only to a particular context, some answer can be missed, downgrading the search accuracy. In order to mitigate this drawback, we propose to extend the standard query algorithms with the operation of context shifting, i.e., the operation that allows switching to a close context, if the current context does not contain satisfactory information to answer a query. The paper provides a conceptual description of shifting in contextualized knowledge bases (CKB); and a prototypical implementation of a CKB that supports context shifting. For the conceptual description we adopt and extend the context-as-a-box paradigm introduced in [15]. In such a framework, a context is identified by a set of dimensions, whose values are taken from value-sets structured in hierarchies. Context shifting allows to switch from one context to another by changing the value of one or more dimensions along the corresponding hierarchies. For the prototypical implementation of a CKB we adopt and extend Sesame RDF store in order to support context shifting.
Stable long-term retrieval of tropospheric temperature time series from the Microwave Sounding Unit

Climatological time-series of tropospheric temperatures from the Microwave Sounding Unit (MSU) are potentially of great value in validating observations of climate change due to their excellent spatial and temporal coverage. However, the long-term trend signal is small relative to natural seasonal and inter-annual variability and inter-satellite biases, and is comparable to diurnal temperature drift arising from variation of the satellite local crossing time. Additional effects due to time-varying non-linear instrument response also contribute to instrument error. We discuss a consistent methodology for compensating for these factors and present an error model quantifying their significance in determination of long-term trends.
Increased MAPK Activation and Impaired Insulin Signaling in Subcutaneous Microvascular Endothelial Cells in Type 2 Diabetes: The Role of Endothelin-1

OBJECTIVE To establish a method for isolation and culture of subcutaneous microvascular endothelial cells (MVEC) from small human tissue biopsies to compare gene and protein expression of insulin signaling molecules in MVEC from insulin-resistant and healthy control subjects. RESEARCH DESIGN AND METHODS Stromavascular cells from subcutaneous needle biopsies of type 2 diabetic and control subjects were expanded in culture and the endothelial cells selected with magnetic immune separation. Western blots and RT-PCR were used for protein and gene expression assays. RESULTS At least 99% of the expanded primary MVEC could be characterized as endothelial cells. The expression of insulin receptors was low, but insulin increased tyrosine phosphorylation of both the insulin receptor and insulin receptor substrate (IRS)-1 and activated protein kinase B (PKB). The IRS-1 protein expression was reduced and the serine phosphorylation of PKB in response to insulin attenuated whereas basal and insulin-stimulated phosphorylation of extracellular signal–related kinase (ERK)1/2 was increased in type 2 diabetes MVEC. Endothelin (ET)-1 mRNA levels were significantly higher in type 2 diabetes cells. The addition of ET-1 increased the phosphorylation of mitogen-activated protein kinase (MAPK), an effect antagonized by the MEK-1 inhibitor PD98059. Furthermore, the endothelin ETA and ETB receptor antagonists BQ123 and BQ788 decreased basal MAPK activity in type 2 diabetes MVEC and prevented the ET-1–induced activation. CONCLUSIONS We developed a system for isolation and culture of human MVEC from small needle biopsies. Our observations support the concept of “selective” insulin resistance, involving IRS-1 and the PI3kinase pathway, as an underlying factor for a dysregulated microvascular endothelium in type 2 diabetes. Our data also support a role of ET-1 for the increased MAPK activity seen in nonstimulated type 2 diabetes MVEC.
Understanding the Effect of Tie Strength on Continuance Intention of Second-Generation Mobile Instant Messaging Services

Facilitated by the widespread adoption of smartphones, applications (apps) on smartphones such as WeChat and WhatsApp have seen rapid and explosive growth. These apps are generally referred to as second-generation mobile instant messaging (SMIM) services. Unlike first-generation mobile instant messaging (FMIM) services (e.g. Short Message Service), SMIM services typically support multimedia contents and are embedded within social networks, which may have a bearing on the postadoption behaviour of users in particular. However, prior studies on the post-adoption usage of SMIM services have a limited understanding of the effects of social network. Network tie strength, as a configuration of social network, has an important impact on users in SMIM services. In order to explore the effects of social network on users’ continued usage intention in SMIM services, we propose and empirically test an integrated model by identifying the antecedents such as tie strength, satisfaction, and perceived critical mass. This study contributes to existing IS post-adoption literature by understanding and capturing the role of social network (i.e. tie strength) in SMIM services. Implications for theory and practice are discussed.
Collective Reinforcement of First Impression Bias

We propose a simple model of attitude dynamics in which an agent tends to ignore the features which contradict its views. For instance, having received a first very negative feature, the agent may stop to consider any moderately positive feature. We call this phenomenon “first impression bias” (FIB). We consider a population of agents which are all in contact with a media, communicating randomly chosen features of an object. In some cases, we observe on simulations that FIB is significantly more frequent when the agents interact with each other than when they are only in contact with the media. We design an analytical aggregated model of the global agent-based model behaviour which helps to explain the higher number of FIB due to the interactions.
Performance analysis of object-oriented software

Many large scale object-oriented applications suffer from chronic performance problems. The many layers of generalised frameworks and libraries that these applications are typically assembled from leads to seemingly simple tasks requiring many thousands of operations to complete. Addressing these performance problems with traditional profiling tools is difficult because of the scale and complexity of the dynamic behaviour being exhibited. A wealth of detailed data is collected but costs are thinly distributed across thousands of methods leaving few easily identifiable performance optimisation opportunities. However we believe there are repeated patterns of method calls hidden within the profile data that represent performance critical sections of code. We plan to investigate new approaches to analysing typical performance data sets to identify these repeated patterns. Our initial work shows some promising results - being able to identify, within an application with over 64 thousand unique calling contexts, ten patterns that account for over 50% of the execution time.
Evaluating the importance of personal information attributes using graph mining technique

Personal information is used as a medium for service co-creation between consumer and service provider. The service provider now views personal information as a type of asset and tries to collects it as much as possible, whereas the consumer does not want to disclose it. The service provider has to find a strategy to collect personal information legally, maybe by providing a platform for trade-off between personal information and monetary rewards. There are several methods proposed to estimate the worth of personal information for the trade-off, but none of them studies the value of each specific attribute. Our survey shows that the value of each attribute varies from the users' point of view, as most regard an attribute such as National ID is more valuable than those of age or gender. We proposed here a technique to measure and compare the importance of each personal information attribute. Using the results from our survey, we constructed a network based on Bayes' formula. Then, we used a graph mining technique to find a relationship between the attributes. The results obtained from the graph mining technique indicate an interesting hierarchical relationship between the attributes. Hierarchy can be used for evaluating each attribute in the trading platform. Also, the results can be used for improving the effectiveness of privacy protection schemes, such as k-anonymity.
A hierarchical class-grouping approach, and a study of classification strategies for leaf classification

Fine-grained visual classification has been considered for image data in various domains of environmental importance such as birds, animals and plants. This work considers the classification problem of the latter, based on the leaf shape. Traditional works in such areas typically propose better features, or sophisticated classification frameworks. In this work, we ask a different question: Given simple and efficient features, and a well-known binary classifier such as support vector machine (SVM), among various strategies, what may be a good way to pose the multi-class classification problem as multiple binary classifications? In this respect, we compare three different strategies, all of which use the same set of features. From our results, we conclude that, one of these three approaches, based on hierarchical class-grouping, clearly outperforms the others, with high classification accuracy. This suggest that classification strategy is an important aspect for the given features and classifiers. To our knowledge, such a study in the fine-grained classification area (and particularly for the nascent area of leaf-classification), has not yet been explored.
Vision-based Control of Multi-fingered Robot Hands using Neural Networks

Most research so far on of multi-fingered robot control has assumed that the kinematics is known exactly. However, in many applications of multi-fingered robot hands, the kinematics is uncertain. In this paper, a vision based control problem for multi-fingered robot hands with uncertain kinematics, dynamics and camera model is addressed. Adaptive neural network control law is proposed and it is shown that the stability can be achieved in the presence of the uncertainties. Sufficient conditions for choosing the feedback gains are presented to guarantee the stability
Implementation of low power flash ADC by reducing comparators

The need for a high speed and low power ADC is very essential for various applications. Flash ADCs are always the architecture choice where maximum sample rate and moderate resolution is needed. Even though flash ADC is the fastest type available it takes enormous amount of IC real estate to implement. The main disadvantage of flash ADC is that it need large area and dissipate large amount of power. To overcome this complexity number of comparators are reducing by using multiplexers. Here the multiplexers are used to generate reference voltages. A 4-bit CMOS based flash ADC is presenting, which uses reduced comparator and multiplexer based architecture. Here both the analog and the digital parts of the proposed ADC are completely modified. This architecture uses only 4 comparators for a 4 bit ADC. This 4-bit ADC is designed and simulated in Hspice with 1.2 V supply voltage.
Paper-based printed circuit boards

We report a simple low-cost paper-based electronic circuit that it is stable electric characteristics comparable to copper pattering by Gerber format-based etching processing onto FR-4 substrate. This electrode was designed to catch the electrical signals through electrodes during biological assay. Therefore, the combination of colorimetric bioassay and electrical signal are likely to lead to more precise analysis in point-of-care applications.
Consistent Approval-Based Multi-Winner Rules

This paper is an axiomatic study of consistent approval-based multi-winner rules, i.e., voting rules that select a fixed-size group of candidates based on approval ballots. We introduce the class of counting rules, provide an axiomatic characterization of this class and, in particular, show that counting rules are consistent. Building upon this result, we axiomatically characterize three important consistent multi-winner rules: Proportional Approval Voting, Multi-Winner Approval Voting and the Approval Chamberlin--Courant rule. Our results demonstrate the variety of multi-winner rules and illustrate three different, orthogonal principles that multi-winner voting rules may represent: individual excellence, diversity, and proportionality.
Encouraging Word of Mouth : Free Contracts , Referral Programs , or Both ?

In the presence of positive externalities from using a product together, a seller has two tools to encourage word of mouth (WoM): She can implement a referral program, where senders of WoM are paid for referrals, or she can increase the expected externalities that the sender receives by offering free contracts so that more receivers start using the product. Augmenting a classic contracting (product line design) problem with an initial WoM stage, we examine conditions under which one, both, or neither tools are optimal. In particular, our model explains why free contracts are particularly attractive for a seller that expects to have many “free users. ” ∗Kamada: Haas School of Business, University of California, Berkeley, Berkeley, CA 94720, e-mail: y.cam. 24@gmail.com; Öry: School of Management, Yale University, New Haven, CT 06511, e-mail: aniko.oery@yale.edu. We are grateful to Juan Escobar, Johannes Hörner, Fuhito Kojima, Vineet Kumar, Dina Mayzlin, Takeshi Murooka, Motty Perry, Klaus Schmidt, Jiwoong Shin, Philipp Strack, Steve Tadelis, Juuso Välimäki, Miguel Villas-Boas, Alex Wolitzky, Jidong Zhou, and seminar participants at the 13th Summer Institute in Competitive Strategy, Osaka University, the University of Munich (LMU) and Yokohama National University for helpful comments. Jovian Chen, Don Hampton, Yi (Michelle) Lu, Valeree Simon, and Tawney Warren provided excellent research assistance.
Biomarkers for Type 2 Diabetes and Impaired Fasting Glucose Using a Nontargeted Metabolomics Approach

Using a nontargeted metabolomics approach of 447 fasting plasma metabolites, we searched for novel molecular markers that arise before and after hyperglycemia in a large population-based cohort of 2,204 females (115 type 2 diabetic [T2D] case subjects, 192 individuals with impaired fasting glucose [IFG], and 1,897 control subjects) from TwinsUK. Forty-two metabolites from three major fuel sources (carbohydrates, lipids, and proteins) were found to significantly correlate with T2D after adjusting for multiple testing; of these, 22 were previously reported as associated with T2D or insulin resistance. Fourteen metabolites were found to be associated with IFG. Among the metabolites identified, the branched-chain keto-acid metabolite 3-methyl-2-oxovalerate was the strongest predictive biomarker for IFG after glucose (odds ratio [OR] 1.65 [95% CI 1.39–1.95], P = 8.46 × 10−9) and was moderately heritable (h2 = 0.20). The association was replicated in an independent population (n = 720, OR 1.68 [ 1.34–2.11], P = 6.52 × 10−6) and validated in 189 twins with urine metabolomics taken at the same time as plasma (OR 1.87 [1.27–2.75], P = 1 × 10−3). Results confirm an important role for catabolism of branched-chain amino acids in T2D and IFG. In conclusion, this T2D-IFG biomarker study has surveyed the broadest panel of nontargeted metabolites to date, revealing both novel and known associated metabolites and providing potential novel targets for clinical prediction and a deeper understanding of causal mechanisms.