Miracle-dz/UniFAR-FTUs huggingface dataset:
Are Rindler Quanta Real? Inequivalent Particle Concepts in Quantum Field Theory

Philosophical reflection on quantum field theory has tended to focus on how it revises our conception of what a particle is. However, there has been relatively little discussion of the threat to the ‘reality’ of particles posed by the possibility of inequivalent quantizations of a classical field theory, i.e. inequivalent representations of the algebra of observables of the field in terms of operators on a Hilbert space. The threat is that each representation embodies its own distinctive conception of what a particle is, and how a ‘particle’ will respond to a suitably operated detector. Our main goal is to clarify the subtle relationship between inequivalent representations of a field theory and their associated particle concepts. We also have a particular interest in the Minkowski versus Rindler quantizations of a free Boson field, because they respectively entail two radically different descriptions of the particle content of the field in the very same region of spacetime. We shall defend the idea that these representations provide complementary descriptions of the same state of the field against the claim that they embody completely incommensurable theories of the field.
Application of Ionic Liquids in the Microwave-Assisted Extraction of Pectin from Lemon Peels

Microwave-assisted extraction of pectin from lemon peels by using ionic liquid as alternative solvent was investigated. The extracted pectin was detected by Fourier transform infrared spectra. The extraction conditions were optimized through the different experiments in conjunction with the response surface methodology. A pectin yield of 24.68 % was obtained under the optimal parameters: the extraction temperature of 88°C, the extraction time of 9.6 min, and a liquid-solid ratio of 22.7 ml · g−1. The structure of the pretreated lemon peel samples and the samples after microwave-assisted extraction were characterized by a field emission scanning electron microscope.
Automatic Labeling of Document Clusters

Automatically labeling document clusters with words which indicate their topics is difficult to do well. The most commonly used method, labeling with the most frequent words in the clusters, ends up using many words that are virtually void of descriptive power even after traditional stop words are removed. Another method, labeling with the most predictive words, often includes rather obscure words. We present two methods of labeling document clusters motivated by the model that words are generated by a hierarchy of mixture components of varying generality. The first method assumes existence of a document hierarchy (manually constructed or resulting from a hierarchical clustering algorithm) and uses a 2 test of significance to detect different word usage across categories in the hierarchy. The second method selects words which both occur frequently in a cluster and effectively discriminate the given cluster from the other clusters. We compare these methods on abstracts of documents selected from a subset of the hierarchy of the Cora search engine for computer science research papers. Labels produced by our methods showed superior results to the commonly employed methods.
Performance and Temperature Aware Floorplanning Optimization for 2D and 3D Microarchitectures

Dedicated to my family, who have always put up with me. iii ACKNOWLEDGEMENTS I would like to express my sincere gratitude to Professor Sung Kyu Lim for his guidance of my research and his patience during my studies at Georgia Tech. I would like to thank my thesis committee members, their valuable suggestions. I would like to thank all the members of GTCAD and CREST groups for their support and friendship, especially, S. Ballapuram, and Mario Vittes who helped me in many ways throughout my years at Georgia Tech. My family has always supported me in all my decisions and for that I express my deepest gratitude and love. I also would like to thank my roommate and friend Christopher Tillotson who put up with me during many stressful times.
Distributed Fault Detection and Isolation in Formation Flight of Satellites using Extended Kalman Filters

In this paper, a distributed fault detection and isolation (FDI) scheme for formation flight of satellites is developed. In our proposed approach each satellite attempts to detect actuator faults of its neighboring agents in addition to its own faults. The main challenges are on integrating and fusing the FDI information generated by all agents in order to reduce and minimize the effects of false positive and false negative alarms. The attitude dynamics of a rigid body satellite is described by a system of nonlinear equations. Therefore, an Extended Kalman Filter (EKF) scheme is utilized to generate residuals for the nonlinear formation system in presence of additive noise representing the dominant environmental disturbances in space. Every satellite in the formation estimates its neighbor’s states by using the EKF and based on the neighboring satellites dynamic model. The local or individual agent level FDI system produces residuals by using the state information of the neighbors that have been exchanged. Simulation results presented illustrate the effectiveness and capabilities of the proposed FDI design methodology for a network of formation flight satellites. Keywords-Formation flight of satellites; Extended Kalman Filter; Distributed fault diagnosis.
A NEW STREAM CIPHER BASED ON FIALKA M-125

ABSTRACT In 2010, a new cipher Hummingbird by [Engels, D. -Fan, X. - -Gong, G. -Hu, H. -Smith, E. M. Hummingbird: Ultra-Lightweight Cryptography for Resource-Constrained Devices, in: 1st International Workshop on Lightweight Cryptography for Resource-Constrained Devices. Tenerife, Canary Islands, Spain, January 2010] was proposed. It is a combination of both block and stream cipher and its design was inspired and motivated by the Enigma machine. The encryption process of the cipher can be considered as a continuous running of a rotor-cipher. Four block ciphers play the role of the rotors that apply the permutation to the 16-bit words. This cipher motivated us to investigate a new cipher design based on a Fialka cipher machine. Fialka M-125 is an Enigma based rotor-cipher machine used during the Cold War. It is considered one of the most secure cipher machines. Advantages of this cipher are based on the elimination of the Enigma’s known weaknesses. There are no known attacks on this cipher. In this paper we introduce a new cipher based on the Fialka machine. We transform the Fialka encryption algorithm to a modern stream cipher. The rotors are represented as S-boxes and shift registers are used to provide the rotor clocking. We propose three different versions of the cipher and investigate the statistical properties of their outputs. In the article we also provide basic implementation details and basic performance analysis.
Functional Analysis of the p53 Pathway in Neuroblastoma Cells Using the Small-Molecule MDM2 Antagonist Nutlin-3

Suppression of p53 activity is essential for proliferation and survival of tumor cells. A direct p53-activating compound, nutlin-3, was used in this study, together with p53 mutation analysis, to characterize p53 pathway defects in a set of 34 human neuroblastoma cell lines. We identified 9 cell lines (26%) with a p53 loss-of-function mutation, including 6 missense mutations, 1 nonsense mutation, 1 in-frame deletion, and 1 homozygous deletion of the 3′ end of the p53 gene. Sensitivity to nutlin-3 was highly predictive of absence of p53 mutation. Signaling pathways downstream of p53 were functionally intact in 23 of 25 cell lines with wild-type p53. Knockdown and overexpression experiments revealed a potentiating effect of p14ARF expression on the response of neuroblastoma cells to nutlin-3. Our findings shed light on the spectrum of p53 pathway lesions in neuroblastoma cells, indicate that defects in effector molecules downstream of p53 are remarkably rare in neuroblastoma, and identify p14ARF as a determinant of the outcome of the response to MDM2 inhibition. These insights may prove useful for the clinical translation of evolving strategies aimed at p53 reactivation and for the development of new therapeutic approaches. Mol Cancer Ther; 10(6); 983–93. ©2011 AACR.
Personality pathology factors predict recurrent major depressive disorder in emerging adults.

OBJECTIVE Prior investigations consistently indicate that personality pathology is a risk factor for recurrence of major depressive disorder (MDD). Lack of emipircal support, however, for the Diagnostic and Statistical Manual of Mental Disorders (DSM) Fourth Edition organization of Axis II disorders supports the investigation of empirically derived factors of personality pathology as predictors of recurrence. METHOD A sample of 130 previously depressed emerging adults (80% female; aged 18 to 21 years) were assessed for personality disorder symptoms at baseline. Participants were then followed for 18 months to identify MDD recurrence during the first 2 years of college. RESULTS Based on a previous factor analysis of DSM personality disorder criteria, eight personality pathology factors were examined as predictors of MDD recurrence. Survival analysis indicated that factors of interpersonal hypersensitivity, antisocial conduct, and social anxiety were associated with increased risk of MDD recurrence. CONCLUSIONS These findings suggest that an empirically based approach to personality pathology organization may yield useful predictors of MDD recurrence during emerging adulthood.
Model Selection for Ranking SVM Using Regularization Path

This chapter deals with supervised learning problems under the ranking framework. Ranking algorithms are typically introduced as a tool for personalizing the order in which document recommendations or search results in the web, for example are presented. That is, the more important a result is to the user, the earlier it should be listed. To this end, two possible settings can be considered : i. the algorithm tries to interactively rearrange the results of one search such that relevant results come the closer to the top the more (implicit) feedback the user provides, ii. the algorithm tries to generalize over several queries and presents the results of one search in an order depending on the feedback obtained from previous searches. The first setting deals with an active learning while the second setting deals with a passive supervised learning. This kind of problems have gain major attention given the nowadays amount of available informations. This is without doubt a challenging task in the medium and large scale context. Several methods have been proposed to solve these problems. For the passive setting, the Rankboost algorithm (Freund et al. (2003)) is an adaptation from the Adaboost algorithm to the ranking problem. This is a boosting algorithm which works by iteratively building a linear combination of several “weak” algorithms to form a more accurate algorithm. The Pranking algorithm (Crammer & Singer (2001)) is an online version of the weighted algorithm. The SVRank and RankSVMalgorithms are the adaptation of the Support Vector machines for classification and regression, respectively, while the MPRank (Cortes et al. (2007)) is a magnitude-preserving algorithm, which searches not only to keep the relative position of each sample but also to preserve the distance given by the correct ordering. This last algorithm has as well the form of a regularization problem as the two previous with a different cost function. Later, the Ranking SVM (RankSVM) algorithm was proposed by Herbrich et al. (2000) and Joachims (2002) as an optimization problem with constraints given by the induced graph of the ordered queries’ results. This algorithm forms part of the family of kernel algorithms of the SVM type (Boser et al. (1992); Scholkopf & Smola (2002)). Kernel methods like the SVM or the ranking SVM solve optimization problems of the form O pe n A cc es s D at ab as e w w w . in te ch w eb . o rg
Submodular data selection with acoustic and phonetic features for automatic speech recognition

In this paper, we propose to use acoustic feature based submodular function optimization to select a subset of untranscribed data for manual transcription, and retrain the initial acoustic model with the additional transcribed data. The acoustic features are obtained from an unsupervised Gaussian mixture model. We also integrate the acoustic features with the phonetic features, which are obtained from an initial ASR system, in the submodular function. Submodular function optimization has been theoretically shown its near-optimal guarantee. We performed the experiments on 1000 hours of Mandarin mobile phone speech, in which 300 hours of initial data was for the training of an initial acoustic model. The experimental results show that the acoustic feature based approach, which does not rely on an initial ASR system, performs as well as the phonetic feature based approach. Moreover, there is complementary effect between the acoustic feature based and the phonetic feature based data selection. The submodular function with the combined features provides a relative 4.8% character error rate (CER) reduction over the corresponding ASR system using random selection. We also include the desired feature distribution obtained from a development set in a generalized function, but the improvement is insignificant.
On Inferring AS-Level Connecti vity from BGP Routing Tables

Recent studies concerning the Internet connectivity at the AS level have attracted considerable attention. These studies have exclusively relied on BGP measurements collected by the National Laboratory for Applied Network Research (NLANR) in deriving some of the unexpected and intriguing results. The NLANR data sets reflect AS peering relationships, as reported by BGP, seen from a handful of vantage points in the global Internet. The possibility that these data sets may provide only a very sketchy picture of the complete inter-AS connections that exist in the actual I nternet has received surprisingly little scrutiny. By augmenting the NLANR data sets with BGP summary information from a large number of Internet Looking Glass sites and with routing policy information from Internet Routing Registry (IRR) databases, we find that (i) a significant number of existing AS connections remain hidden from most BGP routing tables, (ii) the AS connections to Tier-1 ASs are in general more easily observed than those to non-Tier-1 ASs, and (iii) there are at least about 25–50% more AS connections in the Internet than what recently considered BGP-derived AS maps contain (but only about 2% more ASs). These findings point out the need for an increased awareness of and a more critical attitude toward the applicability and completeness of given data sets at hand when establishing the generality of any particular observations about the Internet. Keywords—Internet topology, BGP routing tables This project is funded in part by NSF grant number ANI-008228 7 and by ONR grant number N000140110617. Sugih Jamin is furthe r supported by the NSF CAREER Award ANI-9734145, the Presiden t al Early Career Award for Scientists and Engineers (PECASE) 19 99, and the Alfred P. Sloan Foundation Research Fellowship. Additi onal funding is provided by AT&T Research, and by equipment grants fro m Sun Microsystems Inc. and Compaq Corp. Correspondence to hschang@eecs.umich. edu.
Study of Evolving Co-Authorship Network: Identification of Growth Patterns of Collaboration Using SNA Measures

In the recent past, macro-level measures of the network have become more popular within complex social networks such as detection of patterns of growth in social network, a community structure or a Heavy-tailed degree distribution. These measures have been reinvented to gain more insight into structural properties due to the fact that many mathematical models do not show these features. This article has studied complex co-authorship network via same classic approach for finding the corresponding properties like authorship patterns, trends of collaboration, the evolution of core component and ranking authors. In this article, we have also analyzed network diameter, clustering coefficient and degree distribution in time span of 50 years and suggested that these measures can be useful indicators for the patterns of connectivity, identifying small world phenomena and identifying the scale-free property of network.
Unsupervised context discovery based on hierarchical fusion of heterogeneous features in real smart living environments

The development of Internet of Things (IoT) has enhanced smart living environments with great support by heterogeneous sensors for many human-centric purposes, among which an important one is to recognize potential contexts through Big Data analytics. In the related researches, the analytics is usually conducted on activity data from well-designed smart environments, where sensors are distributed in a balance manner for specific activity patterns predefined by domain knowledge. However, in a real living environment, sensor deployment is usually ad hoc customized without considering specific activity patterns in advance. For large quantity of data, it would be desirable to conduct unsupervised data-driven analytics for context discovery. Moreover, features from heterogeneous sensors may conflict with one another, and conventional methods often ignore the background context, which usually exists in reality and compromises the discovery of minor contexts. Therefore, in this paper, we propose an unsupervised analytics framework to discover the potential daily contexts for real smart living environments based on hierarchical fusion of features from heterogeneous sensors. The experimental results show that the contexts discovered by our proposed work present a more reasonable way to describe our daily lives.
Automatic verification of probabilistic concurrent finite state programs

The verification problem for probabilistic concurrent finite-state program is to decide whether such a program satisfies its linear temporal logic specification. We describe an automata-theoretic approach, whereby probabilistic quantification over sets of computations is reduced to standard quantification over individual computations. Using new determinization construction for ω-automata, we manage to improve the time complexity of the algorithm by two exponentials. The time complexity of the final algorithm is polynomial in the size of the program and doubly exponential in the size of the specification.
Dual Burden of Underweight and Overweight among Women in Bangladesh: Patterns, Prevalence, and Sociodemographic Correlates

ABSTRACT The discourse of dual burden caused through underweight and overweight is well-documented globally but this issue and its connection with women's health in Bangladesh is yet to be explored widely. To enrich the current debate, this study, in the context of Bangladesh, examines the patterns, prevalence, and socioeconomic factors influencing the ever-married women of being underweight and overweight over normal weight. Data used in this study have been extracted from the most recent 2011 Bangladesh Demographic and Health Survey. To achieve results connected with the research objectives, both bivariate and multivariate statistical analyses have been employed. In bivariate analysis, we used seven categories of BMI cutoff points for Asian countries as prescribed by World Health Organization (WHO). Multinomial logistic regression model was constructed to investigate the net effect of socioeconomic factors on underweight, pre-overweight, and overweight over normal weight. The results confirm the co-existence of underweight and overweight among women as we found the prevalence of underweight, normal weight, pre-overweight, overweight, and obesity to be 24.1%, 46.7%, 12.8%, 13.5%, and 2.9% respectively. Compared to the richest, the women from the poorest households were significantly (p<0.001) most likely to be underweight (OR=2.75, 95% CI 2.27-3.35) and least likely to be overweight (OR=0.15, 95% CI 0.12-0.19) over normal weight. The urban women, compared to their rural counterparts, were significantly (p<0.001) less likely to be underweight (OR=0.80, 95% CI 0.71-0.91) and more likely to be overweight (OR=1.33, 95% CI 1.18-1.51) than normal weight. The other socioeconomic grades that were most marked to be underweight and overweight are age, women's education, marital status, age at first childbirth, parity, number of children aged ≤5 years at the household, and food security. The findings confirm the dual burden of both under- and overweight. Systematic and regular monitoring and surveillance of the social trajectory of nutritional status of women and men in Bangladesh is crucial to develop apposite strategy that addresses the persistent and chronic problem of underweight and the emerging problem of overweight. The dual existence of both types of malnutrition among women in Bangladesh must be taken into consideration so that public health interventions may be adopted through appropriate policy.
Evidence and consensus recommendations for the pharmacological management of pain in India

Despite enormous progress in the field of pain management over the recent years, pain continues to be a highly prevalent medical condition worldwide. In the developing countries, pain is often an undertreated and neglected aspect of treatment. Awareness issues and several misconceptions associated with the use of analgesics, fear of adverse events – particularly with opioids and surgical methods of analgesia – are major factors contributing to suboptimal treatment of pain. Untreated pain, as a consequence, is associated with disability, loss of income, unemployment and considerable mortality; besides contributing majorly to the economic burden on the society and the health care system in general. Available guidelines suggest that a strategic treatment approach may be helpful for physicians in managing pain in real-world settings. The aim of this manuscript is to propose treatment recommendations for the management of different types of pain, based on the available evidence. Evidence search was performed by using MEDLINE (by PubMed) and Cochrane databases. The types of articles included in this review were based on randomized control studies, case–control or cohort studies, prospective and retrospective studies, systematic reviews, meta-analyses, clinical practice guidelines and evidence-based consensus recommendations. Articles were reviewed by a multidisciplinary expert panel and recommendations were developed. A stepwise treatment algorithm-based approach based on a careful diagnosis and evaluation of the underlying disease, associated comorbidities and type/duration of pain is proposed to assist general practitioners, physicians and pain specialists in clinical decision making.
Modeling and Predicting the Helpfulness of Online Reviews

Online reviews provide a valuable resource for potential customers to make purchase decisions. However, the sheer volume of available reviews as well as the large variations in the review quality present a big impediment to the effective use of the reviews, as the most helpful reviews may be buried in the large amount of low quality reviews. The goal of this paper is to develop models and algorithms for predicting the helpfulness of reviews, which provides the basis for discovering the most helpful reviews for given products. We first show that the helpfulness of a review depends on three important factors: the reviewerpsilas expertise, the writing style of the review, and the timeliness of the review. Based on the analysis of those factors, we present a nonlinear regression model for helpfulness prediction. Our empirical study on the IMDB movie reviews dataset demonstrates that the proposed approach is highly effective.