Miracle-dz/UniFAR-FTUs huggingface dataset:
An Ascending Multi-Item Auction with Financially Constrained Bidders

A number of heterogeneous items are to be sold to a group of potential bidders. Every bidder knows his own values over the items and his own budget privately. Due to budget constraint, bidders may not be able to pay up to their values. In such a market, a Walrasian equilibrium usually fails to exist and also the existing auctions might fail to allocate the items among the bidders. In this paper we first introduce a rationed equilibrium for a market situation with financially constrained bidders. Succeedingly we propose an ascending auction mechanism that always results in an equilibrium allocation and price system. By starting with the reservation price of each item, the auctioneer announces the current prices of the items in each step and the bidders respond with their demand sets at these prices. As long as there is overdemand, the auctioneer adjusts prices upwards for overdemanded items until a price system is reached at which either there is an underdemanded set, or there is neither overdemand nor underdemand anymore. In the latter case the auction stops. In the former case, precisely one item will be sold, the bidder buying the item leaves the auction and the auction continues with the remaining items and the remaining bidders. We prove that the auction finds a rationed equilibrium in a finite number of steps. In addition, we derive various properties of the allocation and price system obtained by the auction.
Nonparametric Forecasting in Time Series—A Comparative Study

The problem of predicting a future value of a time series is considered in this article. If the series follows a stationary Markov process, this can be done by nonparametric estimation of the autoregression function. Two forecasting algorithms are introduced. They only differ in the nonparametric kernel-type estimator used: the Nadaraya-Watson estimator and the local linear estimator. There are three major issues in the implementation of these algorithms: selection of the autoregressor variables, smoothing parameter selection, and computing prediction intervals. These have been tackled using recent techniques borrowed from the nonparametric regression estimation literature under dependence. The performance of these nonparametric algorithms has been studied by applying them to a collection of 43 well-known time series. Their results have been compared to those obtained using classical Box-Jenkins methods. Finally, the practical behavior of the methods is also illustrated by a detailed analysis of two data sets.
Characterization of liquid jet injection into tissue based on optical coherence tomography

We present the combination of high speed video monitoring, force measurement and Optical Coherence Tomography (OCT) as a new method for the characterization of liquid jets for needle-free tissue injection applications. It is well known that physical parameters, such as jet velocity and jet diameter determine the penetration characteristics of liquid micro-jets into a specific tissue [1]. The optimum injection parameters vary significantly for different types of tissues. Therefore, we have used high speed video monitoring and force sensor measurements to characterize the liquid jet. The penetration into the tissue is then analyzed by Optical Coherence Tomography (OCT) [2]. This enables the general description of the specific jet's physical properties as well as the penetration characteristics with respect to a specific tissue. In the experimental study, jets with diameters of 100 μm and jet lengths from 6 to 18 ms (~ 2 to 9 μl) have been injected into colon tissue from pigs.
Charge transport in a zinc–porphyrin single-molecule junction

We have investigated charge transport in ZnTPPdT–Pyr (TPPdT: 5,15-di(p-thiolphenyl)-10,20-di(p-tolyl)porphyrin) molecular junctions using the lithographic mechanically controllable break-junction (MCBJ) technique at room temperature and cryogenic temperature (6 K). We combined low-bias statistical measurements with spectroscopy of the molecular levels in the form of I(V) characteristics. This combination allows us to characterize the transport in a molecular junction in detail. This complex molecule can form different junction configurations, having an observable effect on the trace histograms and the current–voltage (I(V)) measurements. Both methods show that multiple, stable single-molecule junction configurations can be obtained by modulating the interelectrode distance. In addition we demonstrate that different ZnTPPdT–Pyr junction configurations can lead to completely different spectroscopic features with the same conductance values. We show that statistical low-bias conductance measurements should be interpreted with care, and that the combination with I(V) spectroscopy represents an essential tool for a more detailed characterization of the charge transport in a single molecule.
Ghosts from the high court's past: Evidence from computational linguistics for Dixon ghosting for Mctiernan and rich

Sir Edward McTiernan was a member of the High Court of Australia (hereafter High Court) from 1930 to 1976, making him the Court's longest serving Justice. Sir George Rich was a member of the High Court from 1913 to 1950. The High Court terms of both McTiernan and Rich overlapped to a considerable extent with that of Sir Owen Dixon, who was a Justice of the High Court from 1929 to 1952 and Chief Justice from 1952 to 1964. It is well known that McTiernan and Rich had a tendency to join in the judgments written by Dixon, or to write a short concurring judgment agreeing with him.1 Sir Hayden Starke considered McTiernan a 'parrot' because he often agreed with Dixon. Legg writes: 'Sir Edward McTiernan was not regarded with as much awe as his brethren on the Bench. On one occasion, much to everyone's amazement, Sir Owen Dixon concurred in one of his judgments. About 99 per cent of the time it was the other way around'.
PRODUCT ASPECT RANKING USING DOMAIN DEPENDENT AND DOMAIN INDEPENDENT REVIEW

In today’s world, internet is the main source of information. There are many blogs and forum sites available where people discuss on different issues and also almost all ecommerce website provide facility to the users to express opinion about their product and services which is important information available on the internet . The problem with this information is that this reviews are mostly not organized therefore creating difficulty for knowledge acquisition. There are many solution exist to resolve this problem but the available existing methods depends on extracting product aspect only considering single domain relevant review corpus. To address this problem, a method is explored to identify product aspect from online review is by taking into account the difference in aspect statistical characteristic across different corpus. This paper shows need of automatically identifying important product aspects from available online customer review and an approach of aspect ranking. This paper also shows the related work on this domain. Our methodology confirmed product aspect which are less nonspecific in domain independent corpus and more domain specific. Then customer opinion expressed on these aspects is determined using sentiment classifier and finally ranking of product aspect is calculated using it’s ranking relevance score of each aspect .
Quadruplex DNA: sequence, topology and structure

G-quadruplexes are higher-order DNA and RNA structures formed from G-rich sequences that are built around tetrads of hydrogen-bonded guanine bases. Potential quadruplex sequences have been identified in G-rich eukaryotic telomeres, and more recently in non-telomeric genomic DNA, e.g. in nuclease-hypersensitive promoter regions. The natural role and biological validation of these structures is starting to be explored, and there is particular interest in them as targets for therapeutic intervention. This survey focuses on the folding and structural features on quadruplexes formed from telomeric and non-telomeric DNA sequences, and examines fundamental aspects of topology and the emerging relationships with sequence. Emphasis is placed on information from the high-resolution methods of X-ray crystallography and NMR, and their scope and current limitations are discussed. Such information, together with biological insights, will be important for the discovery of drugs targeting quadruplexes from particular genes.
On the Control Plane of a Self-service Cloud Platform

Self-service Cloud Computing (SSC) [7] is a recently-proposed model to improve the security and privacy of client data on public cloud platforms. It prevents cloud operators from snooping on or modifying client VMs and provides clients the flexibility to deploy security services, such as VM introspection tools, on their own VMs. SSC achieves these goals by modifying the hypervisor privilege model. This paper focuses on the unique challenges involved in building a control plane for an SSC-based cloud platform. The control plane is the layer that facilitates interaction between hosts in the cloud infrastructure as well as between the client and the cloud. We describe a number of novel features in SSC's control plane, such as its ability to allow specification of VM dependencies, flexible deployment of network middleboxes, and new VM migration protocols. We report on our design and implementation of SSC's control plane, and present experimental evaluation of services implemented atop the control plane.
Routing method with flow entry aggregation for software-defined networking

In recent years, software-defined networking (SDN), which performs centralized network management with software, has attracted much attention. In SDN, the data plane is separated from the control plane. Network administrators can dynamically change the setting of the data plane and control routing on the control plane through a control protocol such as OpenFlow. Although packets are transmitted based on flow entries in the data plane, the number of flow entries that data plane can have is limited. It is difficult to perform flexible routing control with a small number of flow entries. To overcome this difficulty, this paper proposes a routing method that reduces the number of flow entries, while reducing the maximum link utilization. The proposed method allocates common paths to flows that can be aggregated, considering the utilization of the links. Through numerical experiments, this paper shows the effectiveness of the proposed method.
Short day transcriptomic programming during induction of dormancy in grapevine

Bud dormancy in grapevine is an adaptive strategy for the survival of drought, high and low temperatures and freeze dehydration stress that limit the range of cultivar adaptation. Therefore, development of a comprehensive understanding of the biological mechanisms involved in bud dormancy is needed to promote advances in selection and breeding, and to develop improved cultural practices for existing grape cultivars. The seasonally indeterminate grapevine, which continuously develops compound axillary buds during the growing season, provides an excellent system for dissecting dormancy, because the grapevine does not transition through terminal bud development prior to dormancy. This study used gene expression patterns and targeted metabolite analysis of two grapevine genotypes that are short photoperiod responsive (Vitis riparia) and non-responsive (V. hybrid, Seyval) for dormancy development to determine differences between bud maturation and dormancy commitment. Grapevine gene expression and metabolites were monitored at seven time points under long (LD, 15 h) and short (SD, 13 h) day treatments. The use of age-matched buds and a small (2 h) photoperiod difference minimized developmental differences and allowed us to separate general photoperiod from dormancy specific gene responses. Gene expression profiles indicated three distinct phases (perception, induction and dormancy) in SD-induced dormancy development in V. riparia. Different genes from the NAC DOMAIN CONTAINING PROTEIN 19 and WRKY families of transcription factors were differentially expressed in each phase of dormancy. Metabolite and transcriptome analyses indicated ABA, trehalose, raffinose and resveratrol compounds have a potential role in dormancy commitment. Finally, a comparison between V. riparia compound axillary bud dormancy and dormancy responses in other species emphasized the relationship between dormancy and the expression of RESVERATROL SYNTHASE and genes associated with C3HC4-TYPE RING FINGER and NAC DOMAIN CONTAINING PROTEIN 19 transcription factors.
Ultrasensitive densitometry detection of cytokines with nanoparticle-modified aptamers.

BACKGROUND Aptamers mimic properties of antibodies and sometimes turn out to be even better than antibodies as reagents for assays. We describe the establishment of an ultrasensitive densitometry method for cytokine detection by nanoparticle (NP)-modified aptamers. METHODS The assay simultaneously uses a gold NP-modified aptamer and a biotin-modified aptamer to bind to the target protein, forming a sandwich complex. The absorbance signal generated by the aptamer-protein complex is amplified and detected with a microplate reader. RESULTS The assay for platelet-derived growth factor B-chain homodimer (PDGF-BB) was linear from 1 fmol/L to 100 pmol/L (R(2) = 0.9869). The analytical detection limit was 83 amol/L. The intraassay and interassay imprecision (CVs) was < or =7.5%. Serum concentrations of PDGF-BB determined with the gold NP-modified aptamer assay and with ELISA were not significantly different. CONCLUSIONS The gold NP-modified aptamer assay provides a fast, convenient method for cytokine detection and improves the detection range and the detection limit compared with ELISA.
A meta-analysis of the relationship between social support and well-being.

This study focuses upon the relationship between social support and well-being and the effect size of social support on well-being. Meta-analysis was used to synthesize the primary research studies and a computer search was used in the preliminary examination of the literature. After this preliminary screening and narrowing of the search, approximately 150 research articles published after 1985 were reviewed. Totally, 21 primary studies met the inclusion criteria of this meta-analysis. This study indicated that 41 outcome variables were used in the 21 primary studies. Of these, seven outcome variables were categorized and named. Among these seven outcome variables, social support was significantly correlated with positive mood state, negative mood state, depression, level of functioning, and quality of life. Social support was not found to have a significant correlation at the . 05 level of significance with perceived health status and physical symptoms. These findings have three implications: they facilitate external validity and generalization of research findings of the primary studies; they provide information on the magnitude of sample size for achieving statistical significance between social support and an outcome variable for future studies; and they provide information about social support intervention and enhancing the effect of social support on well-being.
Hardware Operator for Simultaneous Sine and Cosine Evaluation

This work deals with hardware evaluation of the sine and cosine functions for the same argument simultaneously. The proposed method uses trigonometric identities, small lookup tables and low-degree polynomial approximations with very sparse coefficients. Most of the multiplications are replaced by a small number of additions or subtractions, this leads to small and fast circuits
Random Forest Construction With Robust Semisupervised Node Splitting

Random forest (RF) is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing of RFs with a small size of labeled data and find that the performance bottleneck is located in the node splitting procedures; hence, existing solutions fail to properly partition the feature space if there are insufficient training data. To achieve robust node splitting with insufficient data, we present semisupervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and abundant unlabeled data. In particular, an accurate quality measure of node splitting is obtained by carrying out the kernel-based density estimation, whereby a multiclass version of asymptotic mean integrated squared error criterion is proposed to adaptively select the optimal bandwidth of the kernel. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. Our algorithm efficiently avoids overfitting caused by bad initialization and local maxima when compared with conventional margin maximization-based semisupervised methods. We demonstrate the effectiveness of the proposed algorithm by comparing it with state-of-the-art supervised and semisupervised algorithms for typical computer vision applications, such as object categorization, face recognition, and image segmentation, on publicly available data sets.
Introduction to the special issue

Welcome to the special issue of Political Analysis dedicated to Bayesian methods. We hope that you enjoy the varied and interesting contributions herein featuring Bayesian statistical methods. For many people in empirical political science, Bayesian statistics seems like a weird offshoot of probability that surfaces occasionally in journals and books but does not occupy a particularly central role. This perception appears to be changing. In fact, it appears to be changing quite rapidly. The purpose of this issue is to support and accelerate this momentum by further demonstrating the full flexibility and power of Bayesian methodology. So why this change? Why are people suddenly more interested in developing Bayesian models in their own research? The first apparent reason for this change is that the Bayesian model specifications have distinct advantages over traditional alternatives, such as probabilistic descriptions of model results and the systematic incorporation of prior information. The second reason for this change has to do with computing. Prior to 1990, when the watershed review article by Gelfand and Smith appeared, statisticians working outside of statistical physics and image restoration (i.e., the vast majority) were unaware of a flexible set of estimation tools based on Markov chains. Two primary tools were described in that article: the Metropolis-Hastings algorithm from the 1953 article by Metropolis et al. (although curiously only the Hastings 1970 paper is directly cited, p. 400), and the Gibbs sampler from the 1984 article by Geman and Geman. These were relatively inaccessible pieces published in the Journal of Chemical Physics and IEEE Transactions on Pattern Analysis and Machine Intelligence, respectively. What Gelfand and Smith demonstrated was that these are actually very powerful general tools for describing posterior distributions of interest and subsequently producing inferential statements when standard analytical methods are difficult or impossible. As a result, we see an explosion of Bayesian work by previously frustrated researchers in the leading statistics journals of the 1990s who apply Markov chain Monte Carlo (MCMC) to otherwise intractable problems. Now we come to the social sciences, and political science in particular. Gradually over the last five years or so Bayesian applications (generally using MCMC) have appeared. Through the work of Gelman and King (1994), Bartels (1996, 1997), Smith (1998, 1999), Western (1998), Quinn et al. (1999), Jackman (2000a, 2000b, 2001), Clarke (2001), Hill and Kriesi (2001), Martin and Quinn (2002), and others, we have seen a steady increase in awareness among general readers. An underlying theme in this work so far is that
Regulation of bone sialoprotein mRNA by steroid hormones

In this report we demonstrate an increase in the steady-state level of bone sialoprotein (BSP) mRNA in rat calvaria and a rat osteosarcoma cell line (ROS 17/2.8) after treatment with the synthetic glucocorticoid, dexamethasone. In contrast, 1.25-dihydroxyvitamin D3 reduced the amount of BSP mRNA in calvaria and inhibited the dexamethasone induction in ROS 17/2.8 cells. The increase in BSP mRNA is most likely due to an increase in the transcriptional rate. The stability of mRNA was unchanged after dexamethasone treatment with a half-life of approximately 5 h. Nuclear transcription experiments with nuclei isolated from ROS 17/2.8 cells showed an increased BSP mRNA synthesis in cells treated with dexamethasone.
Real-Time Semi-Automatic Segmentation Using a Bayesian Network

This paper presents a semi-automatic segmentation technique called Bayesian cut that formulates object boundary detection as the most probable explanation (MPE) of a Bayesian network’s joint probability distribution. A two-layer Bayesian network structure is formulated from a planar graph representing a watershed segmentation of an image. The network’s prior probabilities encode the confidence that an edge in the planar graph belongs to an object boundary while the conditional probability tables (CPTs) enforce global contour properties of closure and simplicity (i.e., no self-intersections). Evidence, in the form of one or more connected boundary points, allows the network to compute the MPE with minimal user guidance. The constraints imposed by CPTs also permit a linear-time algorithm to compute the MPE, which in turn allows for interactive segmentation where every mouse movement recomputes the MPE based on the current cursor position and displays the corresponding segmentation.
Organizational resilience: A conceptual integrative framework

Abstract Increasingly chaotic business environments of today demand organizations to be more resilient. While the concept of resilience is widely discussed in disaster (e.g., Wildavsky, 1991) and crisis management literatures (e.g., Manyena, 2006), the literature on organizational resilience is developing disjointedly in organizational studies. The literature review suggests that some factors that are suggested in the literature as components of organizational resilience are sources contributing to the emergence of resilience in organizations. This study proposes an integrative framework for organizational resilience and introduces a new outcome concept of organizational evolvability, emphasizing the heightened sensitivity and increased wisdom of the post-event organization. In this model, sources of organizational resilience are categorized as perceptual stance, contextual integrity, strategic capacity and strategic acting, and organizational resilience leads to organizational evolvability as its outcome. The proposed organizational resilience framework attempts to provide a synthesis of the divergent literature on resilience and aims to strengthen organizational resilience research for richer theoretical and empirical progress.