Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel Repetition via Fortification: Analytic View and the Quantum Case

In a recent work, Moshkovitz [FOCS '14] presented a transformation on two-player games called "fortification", and gave an elementary proof of an (exponential decay) parallel repetition theorem for fortified two-player projection games. In this paper, we give an analytic reformulation of Moshkovitz's fortification framework, which was originally cast in combinatorial terms. This reformulation allows us to expand the scope of the fortification method to new settings. First, we show any game (not just projection games) can be fortified, and give a simple proof of parallel repetition for general fortified games. Then, we prove parallel repetition and fortification theorems for games with players sharing quantum entanglement, as well as games with more than two players. This gives a new gap amplification method for general games in the quantum and multiplayer settings, which has recently received much interest. An important component of our work is a variant of the fortification transformation, called "ordered fortification", that preserves the entangled value of a game. The original fortification of Moshkovitz does not in general preserve the entangled value of a game, and this was a barrier to extending the fortification framework to the quantum setting.
Novel Approaches to Examine Passage, Student, and Question Effects on Reading Comprehension

Reading comprehension is influenced by sources of variance associated with the reader and the task. To gain insight into the complex interplay of multiple sources of influence, we employed crossed random–effects item response models. These models allowed us to simultaneously examine the degree to which variables related to the type of passage and student characteristics influenced students’ (n = 94; mean age = 11.97 years) performance on two indicators of reading comprehension: different types of comprehension questions and passage fluency. We found that variables related to word recognition, language, and executive function were influential across various types of passages and comprehension questions and also predicted a reader's passage fluency. Further, an exploratory analysis of two–way interaction effects was conducted. Results suggest that understanding the relative influence of passage, question, and student variables has implications for identifying struggling readers and designing interventions to address their individual needs.
Statistical Leakage Estimation Based on Sequential Addition of Cell Leakage Currents

This paper presents a novel method for full-chip statistical leakage estimation that considers the impact of process variation. The proposed method considers the correlations among leakage currents in a chip and the state dependence of the leakage current of a cell for an accurate analysis. For an efficient addition of the cell leakage currents, we propose the virtual-cell approximation (VCA), which sums cell leakage currents sequentially by approximating their sum as the leakage current of a single virtual cell while preserving the correlations among leakage currents. By the use of the VCA, the proposed method efficiently calculates a full-chip leakage current. Experimental results using ISCAS benchmarks at various process variation levels showed that the proposed method provides an accurate result by demonstrating average leakage mean and standard deviation errors of 3.12% and 2.22%, respectively, when compared with the results of a Monte Carlo (MC) simulation-based leakage estimation. In efficiency, the proposed method also demonstrated to be 5000 times faster than MC simulation-based leakage estimations and 9000 times faster than the Wilkinson's method-based leakage estimation.
Polymorphisms of folate metabolism genes in patients with cirrhosis and hepatocellular carcinoma

AIM To evaluated the association of the risk factors and polymorphisms in MTHFR C677T, MTHFR A1298C, MTR A2756G and MTRR A66G genes. METHODS Patients with cirrhosis (n = 116), hepatocellular carcinoma (HCC) (n = 71) and controls (n = 356) were included. Polymerase chain reaction followed by enzymatic digestion and allelic discrimination technique real-time PCR techniques were used for analysis. MINITAB-14.0 and SNPstats were utilized for statistical analysis. RESULTS Showed that age ≥ 46 years (OR = 10.31; 95%CI: 5.66-18.76; P < 0.001) and smoking (OR = 0.47; 95%CI: 0.28-0.78; P = 0.003) were associated with cirrhosis. Age ≥ 46 years (OR = 16.36; 95%CI: 6.68-40.05; P < 0.001) and alcohol habit (OR = 2.01; 95%CI: 1.03-3.89; P = 0.039) were associated with HCC. MTHFR A1298C in codominant model (OR = 3.37; 95%CI: 1.52-7.50; P = 0.014), recessive model (OR = 3.04; 95%CI: 1.43-6.47; P = 0.0051) and additive model (OR = 1.71; 95%CI: 1.16-2.52; P = 0.0072) was associated with HCC, as well as MTR A2756G in the additive model (OR = 1.68; 95%CI: 1.01-2.77; P = 0.047), and MTRR A66G in the codominant model (OR = 3.26; 95%CI: 1.54-6.87; P < 0.001), dominant model (OR = 2.55; 95%CI: 1.24-5.25; P = 0.007) and overdominant model (OR = 3.05; 95%CI: 1.66-5.62; P < 0.001). MTR A2756G in the additive model (OR = 1.54; 95%CI: 1.02-2.33; P = 0.042) and smokers who presented at least one polymorphic allele for MTRR A66G (OR = 1.71; 95%CI: 0.77-3.82; P = 0.0051) showed increased risk for cirrhosis. There was no association between clinical parameters and polymorphisms. CONCLUSION Age ≥ 46 years, alcohol habit and MTR A2756G, MTHFR A1298C and MTRR A66G polymorphisms are associated with an increased risk of HCC development; age ≥ 46 years, tobacco habit and the MTR A2756G polymorphism are associated with cirrhosis.
A context-aware authentication framework for smart homes

There has been a recent rapid increase in the number of Internet of Things (IoT) devices, providing a wide range of services for smart homes such as surveillance cameras, smart lighting, and door locks that can be remotely accessed and controlled. User mobility makes static security mechanisms, such as usernames and passwords, tedious to use. In this paper, we introduce a context-aware authentication framework for smart homes that utilizes contextual information such as the user's location, profile, calendar, request time and access behavior patterns to enable access to home devices. Such contextual information enables our framework to make more informed decisions about whether to accept or deny access requests. The implementation and evaluation demonstrate that the proposed framework provides security in a flexible manner without requiring any additional user intervention.
Experiments in Single and Multi-Document Summarization Using MEAD

In this paper, we describe four experiments in text summarization. The first experiment involves the automatic creation of 120 multi-document summaries and 308 single-document summaries from a set of 30 clusters of related documents. We present official results from a multi-site manual evaluation of the quality of the summaries. The second experiment is about the identification by human subjects of cross-document structural relationships such as identity, paraphrase, elaboration, and fulfillment. The third experiment focuses on a particular cross-document structural relationship, namely subsumption. The last experiment asks human judges to determine which of the input articles in a given cluster were used to produce individual sentences of a manual summary. We present numerical evaluations of all four experiments. All automatic summaries have been produced by MEAD, a flexible summarization system under development at the University of Michigan.
History of Toxic Pfiesteria in North Carolina Estuaries from 1991 to the Present

T fish kills for which Pfiesteria became well known began in North Carolina, when very little research had been conducted on this unusual dinoflagellate. North Carolina’s Albemarle-Pamlico Estuarine System, the epicenter of toxic Pfiesteria outbreaks, is the second largest estuary in area on the US mainland and the most important fish nursery ground on the US Atlantic Coast (Burkholder and Glasgow 1997, Mallin et al. 2000). As a conservative estimate, the state had sustained 48 toxic Pfiesteria outbreaks by 1997, involving more than a billion fish in an area more than 100 km2 (Burkholder et al. 2001a). These events had occurred nearly every summer beginning in 1991, when the organism was first recognized as an estuarine, fish-killing agent (Burkholder et al. 1992, 2001a, Burkholder and Glasgow 1997). In 1997, 50,000 fish died in a small area of the Chesapeake Bay from toxic Pfiesteria , and press coverage of the event was like an explosion. In the same summer, 1.2 million fish died during toxic Pfiesteria outbreaks in Albemarle-Pamlico estuaries, a 4-hour drive south of Washington, DC; those deaths went virtually unmentioned. North Carolina was the first state to encounter toxic Pfiesteria, and the knowledge gained there, especially about toxic Pfiesteria outbreaks and health impacts on laboratory workers (Burkholder et al. 1992, 1995, Burkholder and Glasgow 1995, 1997, Glasgow et al. 1995), benefited Maryland officials, who were challenged to act quickly and decisively. They evaluated and rapidly verified the role of toxic Pfiesteria in the Chesapeake Bay outbreak (MDNR 1998). Maryland was the first state from which people who reported neurocognitive, respiratory, and other symptoms from environmental exposure were clinically evaluated within a short period (1–3 weeks) after being exposed (Grattan et al. 1998). Maryland was also the first state to address the Pfiesteria problem by making significant advances in legislation for protection of water quality (State of Maryland 1998). Congressional attention following the toxic Pfiesteria outbreaks in Maryland led to the appropriation of many millions of dollars to federal agencies to research and manage toxic Pfiesteria outbreaks, resulting in excellent progress in some areas and setbacks in others. Here we examine the history of toxic Pfiesteria outbreaks before the four in Chesapeake Bay and a few aspects of the aftermath, from the perspective and experience of our research in the Albemarle-Pamlico Estuarine System with high toxic Pfiesteria activity. An important part of the Pfiesteria story is how North Carolina—with 98% of the Pfiesteria problem—subsequently moved to strengthen water resource protection, environmental education, and support of Pfiesteria research, actions that would not have been possible without the events that unfolded in Chesapeake Bay.
A NEW CHAIN CODING BASED METHOD FOR BINARY IMAGE COMPRESSION AND RECONSTRUCTION

In this paper, we present a hybrid chain coding based scheme for contours and binary image compression and reconstruction. The proposed scheme comprises of a lossless and a lossy parts. This scheme is designed in such a way to generate extensive number of replicate links in the contours that can be assembled according to our (n10, 5) rule that can be highly compressed. Furthermore, for the lossy part, we introduce a new line processing technique to smooth the contours while maintaining high image quality. The experimental results show that the proposed method surpasses all published chain coding methods including FCC, DCC, DCC-8, VCC, CRCC, and L&Z. In addition, this scheme produced significantly higher compression ratio than WinZip, G3, G4, JBIG1, and JBIG2 standards.
Predictions on the go: Prevalence of spontaneous spending predictions

The present research examines the prevalence of predictions in daily life. Specifically we examine whether spending predictions for specific purchases occur spontaneously in life outside of a laboratory setting. Across community samples and student samples, overall self-report and diary reports, three studies suggest that people make spending predictions for about two-thirds of purchases in everyday life. In addition, we examine factors that increase the likelihood of spending predictions: the size of purchase, payment form, time pressure, personality variables, and purchase decisions. Spending predictions were more likely for larger, more exceptional purchases and for item and project predictions rather than time periods.
Effects of Practical Rechargeability Constraints on Perpetual RF Harvesting Sensor Network Operation

Green perpetual sensor network operation is the need of the hour for critical applications, such as surveillance, military, and environment monitoring. Mobile integrated data collection and recharging is a promising approach to meet this requirement by routinely visiting the field nodes for collecting the sensed data and supplying energy via radio frequency (RF) energy transfer. Practical constraints, such as self-discharge and aging effects of the energy storage element (supercapacitor), significantly impact the renewable energy cycle (REC) and, hence, strongly influence the performance of RF energy harvesting networks. To account for the nonidealities in practical supercapacitors, in this paper, a circuit model for REC is proposed, and corresponding RF charging time and node lifetime expressions are derived. Hardware experiments are performed to validate the proposed REC model. REC for complicated supercapacitor models is characterized using duality principle and a generic simulation model. Using the developed analytical models for practical supercapacitors, the size of network for perpetual operation is estimated, which is demonstrated to be significantly less than that predicted by considering ideal supercapacitor behavior. For example, with three-branch supercapacitor model, the estimated sustainable network size is shown to be nearly 52% less than that offered by the ideal supercapacitor model.
A Branch-Heterogeneous Model of Protein Evolution for Efficient Inference of Ancestral Sequences

Most models of nucleotide or amino acid substitution used in phylogenetic studies assume that the evolutionary process has been homogeneous across lineages and that composition of nucleotides or amino acids has remained the same throughout the tree. These oversimplified assumptions are refuted by the observation that compositional variability characterizes extant biological sequences. Branch-heterogeneous models of protein evolution that account for compositional variability have been developed, but are not yet in common use because of the large number of parameters required, leading to high computational costs and potential overparameterization. Here, we present a new branch-nonhomogeneous and nonstationary model of protein evolution that captures more accurately the high complexity of sequence evolution. This model, henceforth called Correspondence and likelihood analysis (COaLA), makes use of a correspondence analysis to reduce the number of parameters to be optimized through maximum likelihood, focusing on most of the compositional variation observed in the data. The model was thoroughly tested on both simulated and biological data sets to show its high performance in terms of data fitting and CPU time. COaLA efficiently estimates ancestral amino acid frequencies and sequences, making it relevant for studies aiming at reconstructing and resurrecting ancestral amino acid sequences. Finally, we applied COaLA on a concatenate of universal amino acid sequences to confirm previous results obtained with a nonhomogeneous Bayesian model regarding the early pattern of adaptation to optimal growth temperature, supporting the mesophilic nature of the Last Universal Common Ancestor. [Ancestral sequence reconstruction; nonhomogeneous model; optimal growth temperature; phylogenomics; phylogeny. ]
Toward a Comparative Institutional Analysis

Markets are one of the most salient institutions produced by humans, and economists have traditionally analyzed the workings of the market mechanism. Recently, however, economists and others have begun to appreciate the many institution-related events and phenomena that have a significant impact on economic performance. Examples include the demise of the communist states, the emergence of Silicon Valley and e-commerce, the European currency unification, and the East Asian financial crises. In this book Masahiko Aoki uses modern game theory to develop a conceptual and analytical framework for understanding issues related to economic institutions. The wide-ranging discussion considers how institutions evolve, why their overall arrangements are robust and diverse across economies, and why they do or do not change in response to environmental factors such as technological progress, global market integration, and demographic change.
Hydroxyurea-induced partial mushroom body ablation does not affect acquisition and retention of olfactory differential conditioning in honeybees.

The mushroom bodies (MBs), a paired structure in the insect brain, play a major role in storing and retrieving olfactory memories. We tested whether olfactory learning and odor processing is impaired in honeybees in which MB subunits were partially ablated. Using hydroxyurea (HU) to selectively kill proliferating cells, we created honeybees with varying degrees of MB lesions. Three-dimensional reconstructions of brains were generated to analyze the drug-induced morphological changes. These reconstructions show that, with few exceptions, only the MBs were affected by the drug, while other brain areas remained morphometrically intact. Typically, lesions affected only the MB in one hemisphere of the brain. To preclude HU-induced physiologic deficits in the antennal lobe (AL) affecting olfactory learning, we measured the responses to odors in the AL using an in vivo calcium imaging approach. The response patterns did not differ between the AL of intact versus ablated brain sides within respective specimens. We, therefore, carried out side-specific classical discriminative olfactory conditioning of the proboscis extension reflex (PER) with control bees and with HU-treated bees with or without MB ablations. All experimental groups learned equally to discriminate and respond to a rewarded (CS+) but not to an unrewarded (CS-) conditioned stimulus during acquisition and retention tests. Thus, our results indicate that partial MB lesions do not affect this form of elemental olfactory learning.
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis

This paper considers the problem of canonical-correlation analysis (CCA) (Hotelling, 1936) and, more broadly, the generalized eigenvector problem for a pair of symmetric matrices. These are two fundamental problems in data analysis and scientific computing with numerous applications in machine learning and statistics (Shi and Malik, 2000; Hardoon et al., 2004; Witten et al., 2009). We provide simple iterative algorithms, with improved runtimes, for solving these problems that are globally linearly convergent with moderate dependencies on the condition numbers and eigenvalue gaps of the matrices involved. We obtain our results by reducing CCA to the top-$k$ generalized eigenvector problem. We solve this problem through a general framework that simply requires black box access to an approximate linear system solver. Instantiating this framework with accelerated gradient descent we obtain a running time of $O(\frac{z k \sqrt{\kappa}}{\rho} \log(1/\epsilon) \log \left(k\kappa/\rho\right))$ where $z$ is the total number of nonzero entries, $\kappa$ is the condition number and $\rho$ is the relative eigenvalue gap of the appropriate matrices. Our algorithm is linear in the input size and the number of components $k$ up to a $\log(k)$ factor. This is essential for handling large-scale matrices that appear in practice. To the best of our knowledge this is the first such algorithm with global linear convergence. We hope that our results prompt further research and ultimately improve the practical running time for performing these important data analysis procedures on large data sets.
Video Classification via Spatial-Temporal Subspace Learning

A robust video classification method based on spatial-temporal low rank representation learning is proposed in this paper. The proposed algorithm utilizes the spatial manifold structure and the temporal continuity of video data. On one hand, the global spatial subspace structure will be exploited by an improved version of low rank representation, which will be more robust to occlusions. On the other hand, the temporal information of the videos will be penalized by column sparse constraint in representation space. Compared with traditional methods, the proposed method in this paper is more suitable to pursue the global structure of the data space while maintain the temporal consistency of the video, and the performance of the proposed method is more robust when the video is contaminated by occlusions and outliers. An efficient solution based on linearized alternating direction method with adaptive penalty (LADMAP) is built for proposed method. Finally, we evaluate the performance of the proposed algorithm on two standard database, i.e., HONDA and YaleB. The experimental results show that our method outperforms several state-of-art algorithms.
Resources-Events-Agents Design Theory: A Revolutionary Approach to Enterprise System Design

Enterprise systems typically include constructs such as ledgers and journals with debit and credit entries as central pillars of the systems’ architecture due in part to accountants and auditors who demand those constructs. At best, structuring systems with such constructs as base objects results in the storing the same data at multiple levels of aggregation, which creates inefficiencies in the database. At worst, basing systems on such constructs destroys details that are unnecessary for accounting but that may facilitate decision making by other enterprise functional areas. McCarthy (1982) proposed the resources-events-agents (REA) framework as an alternative structure for a shared data environment more than thirty years ago, and scholars have further developed it such that it is now a robust design theory. Despite this legacy, the broad IS community has not widely researched REA. In this paper, we discuss REA’s genesis and primary constructs, provide a history of REA research, discuss REA’s impact on practice, and speculate as to what the future may hold for REA-based enterprise systems. We invite IS researchers to consider integrating REA constructs with other theories and various emerging technologies to help advance the future of information systems and business research.
THE EFFECT OF LENGTH OF PRE-TASK PLANNING TIME ON DISCOURSE-ANALYTIC MEASURES AND ANALYTIC RATINGS IN L2 WRITTEN NARRATIVES

The favorable gains gleaned from the provision of pre-task planning time (PTP) have struck a chord with SLA researchers as they try to manipulate task features to promote language production and development. In a similar vein, the present study is a two-fold attempt to first compare the effect of the length of pre-task planning time on discourse-analytic measures in narrative written production of the Iranian intermediate EFL learners under (a) no planning, (b) three-minute PTP, and (c) ten-minute PTP c nditions and, second, to explore the impact of manipulation of time on scores assigned to learners’ output. Subsequently, discourse-analytic measures of accuracy, syntactic complexity, lexical complexity, and fluency were estimated. In addition, the subjects’ output was scored. The results showed that both groups of planners outperformed noplanners with regard to accuracy and fluency, but not complexity. However, the difference between the three-minute planners and ten-minute planners was not statistically significant with regard to the two production areas. Additionally, scores of the subjects in the three groups did not differ significantly. The fact that three-minute planners and their ten-minute planner counterparts did equally well in terms of all measures can indicate that in future studies, three-minute PTP can be allocated in lieu of the orthodox ten-minute PTP.