Miracle-dz/UniFAR-FTUs huggingface dataset:
Why almost all satisfiable $k$-CNF formulas are easy

Finding a satisfying assignment for a $k$-CNF formula $(k \geq 3)$, assuming such exists, is a notoriously hard problem. In this work we consider the uniform distribution over satisfiable $k$-CNF formulas with a linear number of clauses (clause-variable ratio greater than some constant). We rigorously analyze the structure of the space of satisfying assignments of a random formula in that distribution, showing that basically all satisfying assignments are clustered in one cluster, and agree on all but a small, though linear, number of variables. This observation enables us to describe a polynomial time algorithm that finds $\textit{whp}$ a satisfying assignment for such formulas, thus asserting that most satisfiable $k$-CNF formulas are easy (whenever the clause-variable ratio is greater than some constant). This should be contrasted with the setting of very sparse $k$-CNF formulas (which are satisfiable $\textit{whp}$), where experimental results show some regime of clause density to be difficult for many SAT heuristics. One explanation for this phenomena, backed up by partially non-rigorous analytical tools from statistical physics, is the complicated clustering of the solution space at that regime, unlike the more "regular" structure that denser formulas possess. Thus in some sense, our result rigorously supports this explanation.
From Analyzing to Reflecting: Computational Supports for Frame Reflection

While numerous computational techniques have been developed for analyzing patterns of social media use, less work has explored incorporating such techniques into tools designed for social media users. Here, we argue for the value of such tools, particularly in the context of political discussion. Rather than being used for abstract analysis, these tools can draw attention to computationally identified patterns to encourage critical thinking and reflection about their meaning. This paper describes a prototype of one such tool, situating it theoretically in literature on framing, and discussing some of the issues such a tool might raise. Author Keywords Framing, frame reflection, political communication, computational analysis of social media.
Application-Aware Dynamic Fine-Grained Resource Provisioning in a Virtualized Cloud Data Center

A key factor of win–win cloud economy is how to trade off between the application performance from customers and the profit of cloud providers. Current researches on cloud resource allocation do not sufficiently address the issues of minimizing energy cost and maximizing revenue for various applications running in virtualized cloud data centers (VCDCs). This paper presents a new approach to optimize the profit of VCDC based on the service-level agreements (SLAs) between service providers and customers. A precise model of the external and internal request arrival rates is proposed for virtual machines at different service classes. An analytic probabilistic model is then developed for non-steady VCDC states. In addition, a smart controller is developed for fine-grained resource provisioning and sharing among multiple applications. Furthermore, a novel dynamic hybrid metaheuristic algorithm is developed for the formulated profit maximization problem, based on simulated annealing and particle swarm optimization. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. The advantage of the proposed approach is validated with trace-driven simulations.
Improving the Usability Evaluation Technique, Heuristic Evaluation, through the Use of Collaborative Software

This paper examines how to increase the productivity of the software usability evaluation technique called heuristic evaluation (HE) through the use of collaborative software. Building off of a brainstorming research, it is theorized that part of HE resembles brainstorming, in that it is a creative process that can benefit from anonymous, parallel production afforded by collaborative software. Approximately 230 participants were involved in a lab study to compare traditional HE to HE conducted in face-to-face and distributed settings using collaborative software. It is found that the addition of collaborative software significantly improves HE by eliminating unnecessary duplication work, increasing overall productivity, and by increasing group awareness and consensus earlier on in HE. It is also found that distributed groups do suffer some process losses due to lack of media richness.
A Unified Video Recommendation by Cross-Network User Modeling

Online video sharing sites are increasingly encouraging their users to connect to the social network venues such as Facebook and Twitter, with goals to boost user interaction and better disseminate the high-quality video content. This in turn provides huge possibilities to conduct cross-network collaboration for personalized video recommendation. However, very few efforts have been devoted to leveraging users’ social media profiles in the auxiliary network to capture and personalize their video preferences, so as to recommend videos of interest. In this article, we propose a unified YouTube video recommendation solution by transferring and integrating users’ rich social and content information in Twitter network. While general recommender systems often suffer from typical problems like cold-start and data sparsity, our proposed recommendation solution is able to effectively learn from users’ abundant auxiliary information on Twitter for enhanced user modeling and well address the typical problems in a unified framework. In this framework, two stages are mainly involved: (1) auxiliary-network data transfer, where user preferences are transferred from an auxiliary network by learning cross-network knowledge associations; and (2) cross-network data integration, where transferred user preferences are integrated with the observed behaviors on a target network in an adaptive fashion. Experimental results show that the proposed cross-network collaborative solution achieves superior performance not only in terms of accuracy, but also in improving the diversity and novelty of the recommended videos.
A Deenition of Learner Uncertainty

The paper considers the quantiication of inductive bias in concept learning. It argues that some of the best known measures of bias (eg. Vapnik-Chervonenkis dimension) are context-free in the sense that they measure the eeectiveness of bias in general, rather than with respect to a particular problem. This approach has many advantages but in cases where we want to evaluate the performance of a particular algorithm on a particular problem we need a context-sensitive measure, ie. a way of measuring bias that takes into account the characteristics of the relevant problem. The paper describes a generalization of the ID3ìnformation-needed' concept that provides such a measure. It also shows how the measure can be used to predict learning performance in simple concept learning experiments.
CHM-049 New Strategies Of Differential Evolution For Optimization Of Extraction Process

In recent years, evolutionary algorithms (EAs) have been applied to the solution of non-convex problems in many engineering applications. EAs differ from the conventional algorithms since, in general, only the information regarding the objective function is required. In the present work, a test problem on ‘optimization of extraction process’ is solved using Differential Evolution (DE) and two new DE strategies. The objective of the present study is to maximize the total extraction rate at constant disk rotation speed subject to the inequality constraints. In 1980, scientist used a modified gradient-projection technique and in 1989 GRG (generalized reduced gradient method) was used to solve this problem. Apart from the well known seventh strategy (DE-7) i.e., DE/rand/1/bin, the two new strategies (NS-1 & NS-2) have been applied. A comparison of DE-7 with the proposed two new DE strategies is presented. Experimental Simulations are carried out by running the code for all possible combination of the DE key parameters F & CR (0.0<F<=1; and 0.0<CR<=1.0), where F is scaling factor & CR is crossover constant. The proposed two new DE strategies found to be better and robust ensuring 100% convergence to the global optimum.
Action learning : a strategy for change.

The National Staff Development Committee of the Vocational Education and Training Sector is promoting action learning as a preferred professional development strategy to support the implementation of key competencies. This paper reports on an investigation of action learning as trialed across five training areas within the Department of Employment, Training and Further Education in South Australia. Two semi-structured hour long interviews were conducted with participating staff, one at the beginning, the other at the end of the project, and two questionnaires were administered: the Stages of Concern Questionnaire and the Myers-Briggs Type Indicator. This paper focuses on the two interviews and these results are discussed in relation to the effectiveness of action learning as a change strategy that can move an organisation for learning toward becoming a learning organisation.
Wedge sampling for computing clustering coefficients and triangle counts on large graphs †

Graphs are used to model interactions in a variety of contexts, and there is a growing need to quickly assess the structure of such graphs. Some of the most useful graph metrics are based on triangles, such as those measuring social cohesion. Algorithms to compute them can be extremely expensive, even for moderately sized graphs with only millions of edges. Previous work has considered node and edge sampling; in contrast, we consider wedge sampling, which provides faster and more accurate approximations than competing techniques. Additionally, wedge sampling enables estimating local clustering coefficients, degree‐wise clustering coefficients, uniform triangle sampling, and directed triangle counts. Our methods come with provable and practical probabilistic error estimates for all computations. We provide extensive results that show our methods are both more accurate and faster than state‐of‐the‐art alternatives.
Multi dimensional drive system and their applications

This paper reports on the recent development and application of the multi dimensional system. At first, classify the multi dimensional drive system by the drive principle. The advantages of the multi dimensional drive systems are shown. The problems of the multi dimensional drive system are discussed. Researches of the multi dimensional drive systems are reviewed briefly. Finally, the recent and future applications of the multi dimensional drives are presented
Detection of Faulty Measurements in WBANs using Gaussian Mixture Model and Ant Colony

Wireless sensor networks are subject to different types of faults and interferences after their deployment. Abnormal values reported by sensors should be separated from faulty or injected measurements to ensure reliable monitoring operation. The aim of this paper is to propose a lightweight approach for the detection and suppression of faulty measurements in medical wireless sensor networks. The proposed approach is based on the combination of statistical model and machine learning algorithm. The authors begin by collecting physiological data and then they cluster the data collected during the first few minutes using the Gaussian mixture decomposition. They use the resulted labeled data as the input for the Ant Colony algorithm to derive classification rules in the central base station. Afterward, the derived rules are transmitted and installed in each associated sensor to detect abnormal values in distributed manner, and notify anomalies to the base station. Finally, the authors exploit the spatial and temporal correlations between monitored attributes to differentiate between faulty sensor readings and clinical emergency. They evaluate their approach with real and synthetic patient datasets. The experimental results demonstrate that their proposed approach achieves a high rate of detection accuracy for clinical emergency with reduced false alarm rate when compared to robust Mahalanobis distance.
Foreign direct investment and technology spillovers in sub-Saharan Africa

Foreign direct investment (FDI) is an effective conduit for technology transfer through technology spillovers to domestically owned firms in the host country. This study analyses the significance of productivity externalities of FDI to local firms, in terms of both intra-industry and inter-industry spillovers, using firm-level data from Kenya, Tanzania and Zimbabwe. The results show evidences in support of intra- and inter-industry productivity spillovers from FDI for Kenya and Zimbabwe.
Molecular Gas and Nuclear Activity in Radio Galaxies Detected by IRAS

This paper reports the latest results from a millimeter-wave (CO) spectroscopic survey of IRAS-detected radio galaxies with L1.4 GHz ~ 1023-1028 W Hz-1 in the redshift range z ~ 0.02-0.15. The IRAS flux-limited sample contains 33 radio galaxies with different radio morphologies and a broad range of infrared luminosities (LIR = 109-1012 L☉), allowing for an investigation of (1) whether low-z radio-selected active galactic nuclei (AGNs) reside in molecular gas-rich host galaxies and (2) whether the CO properties are correlated with the properties of the host galaxy or the AGN. All of the radio galaxies in Mazzarella et al. and Mirabel et al. have been reobserved. Three new CO detections have been made, raising the total number of CO detections to nine and setting the survey detection rate at ~25%. Many of the CO lines have double-peaked profiles, and the CO line widths are broad (average ΔvFWHM ~ 500 ± 130 km s-1), exceeding the average CO widths of both ultraluminous infrared galaxies (300 ± 90 km s-1) and Palomar-Green QSOs (260 ± 160 km s-1), and thus being indicative of massive host galaxies. The CO luminosities translate into molecular gas masses of ~(0.4-7) × 109 M☉, however, the 3 σ CO upper limits for nondetections do not rule out a molecular gas mass as high as that of the Milky Way (~3 × 109 M☉). Optical images of eight out of nine molecular gas-rich radio galaxies show evidence of close companions and/or tidal features. Finally, there is no obvious correlation between radio power and molecular gas mass. However, it is notable that only one F-R II galaxy out of 12 is detected in this CO survey; the remaining detections are of galaxies hosting F-R I and compact radio jets.
An Extensible Interactor Toolkitfor Enhancing Information AwarenessD

The typical computer application follows a set pattern: it is executed by a user, it provides responses to commands issued by the user, and it is terminated by the user when it is no longer needed. However, the advent of networked computers and the WorldWide Web has changed the way many applications work. Agents and similar programs constantly access online information databases and identify items that may be of interest to their users. The problem lies in communicating the collected information. Most existing widgets cannot take full advantage of the constant information ux generated by these programs. They work best as part of a full-screen display, they do not react well to changes in information, or they cannot attract the attention of users when new information arrives. This paper explores these problems and describes a number of interactors that may help alleviate them.
Hypericins as Potential Leads for New Therapeutics

70 years have passed since the first isolation of the naphthodianthrones hypericin and pseudohypericin from Hypericum perforatum L. Today, they continue to be one of the most promising group of polyphenols, as they fascinate with their physical, chemical and important biological properties which derive from their unique chemical structure. Hypericins and their derivatives have been extensively studied mainly for their antitumor, antiviral and antidepressant properties. Notably, hypericin is one of the most potent naturally occurring photodynamic agents. It is able to generate the superoxide anion and a high quantum yield of singlet oxygen that are considered to be primarily responsible for its biological effects. The prooxidant photodynamic properties of hypericin have been exploited for the photodynamic therapy of cancer (PDT), as hypericin, in combination with light, very effectively induces apoptosis and/or necrosis of cancer cells. The mechanism by which these activities are expressed continues to be a main topic of discussion, but according to scientific data, different modes of action (generation of ROS & singlet oxygen species, antiangiogenesis, immune responces) and multiple molecular pathways (intrinsic/extrinsic apoptotic pathway, ERK inhibition) possibly interrelating are implicated. The aim of this review is to analyse the most recent advances (from 2005 and thereof) in the chemistry and biological activities (in vitro and in vivo) of the pure naphthodianthrones, hypericin and pseudohypericin from H. perforatum. Extracts from H. perforatum were not considered, nor pharmakokinetic or clinical data. Computerised literature searches were performed using the Medline (PubMed), ChemSciFinder and Scirus Library databases. No language restrictions were imposed.
Ocular Tonometry and Sporadic Creutzfeldt - Jakob Disease (sCJD): A Confirmatory Case-Control Study

Aims To evaluate the hypothesis that sporadic Creutzfeldt-Jakob disease (sCJD) may be transmitted through ocular tonometry. Background The infectious agent of sCJD may be present in the cornea prior to clinical symptoms. Cornea infectiousness has been documented by cornea transplants in guinea pigs and humans. sCJD is resistant to complete inactivity by conventional sterilization techniques. Thus contact tonometry equipment is not disinfected sufficiently to kill sCJD. We previously hypothesized that contact tonometry is a sCJD risk factor. Study Design Population-based case-control study. Place and Duration of Study Department of Neurology, School of Medicine, Loma Linda University, Loma Linda, CA, USA; 4 years. Methodology An 11-state case-control study of pathologically confirmed definite sCJD cases, individually matched controls, and a sample of control surrogates was conducted. Ocular tonometry histories were obtained from case-surrogates, controls, and a sample of control-surrogates. Results The odds ratio (OR) for ever vs never having had an ocular tonometry test was statistically significant for matched and unmatched analyses for 15 through 3 years prior to disease onset, using both control self-responses and control surrogates: ORs were ∞ and 19.4 with 1-sided P-values <0.0001 and 0.003 and ORs=∞ and 11.1 with 1-sided P-values <0.003 and 0.02, respectively. ORs increased as the number of tonometry tests increased during this age period: trend test, 2-sided P-value < 0.0001. For ≥5 vs <5 tonometry tests, the OR was 5.8 (unmatched) and 3.7 (matched), 2-sided P-value<0.00005. Respondents generally could not specify the type of tonometry. There was no indication of increased tonometry testing among cases within 2 years of disease onset. Conclusions The a priori hypothesis was supported. Contact tonometry, preferred by ophthalmologists, may be capable of transmitting sCJD. Consideration should be given to using disposable instrument covers after each use. The use the disposable covers or non-contact tonometry is preferable in the absence of effective disinfectant processes at this time.
Complete genome and gene expression analyses of Asaia bogorensis reveal unique responses to culture with mammalian cells as a potential opportunistic human pathogen

Asaia bogorensis, a member of acetic acid bacteria (AAB), is an aerobic bacterium isolated from flowers and fruits, as well as an opportunistic pathogen that causes human peritonitis and bacteraemia. Here, we determined the complete genomic sequence of the As. bogorensis type strain NBRC 16594, and conducted comparative analyses of gene expression under different conditions of co-culture with mammalian cells and standard AAB culture. The genome of As. bogorensis contained 2,758 protein-coding genes within a circular chromosome of 3,198,265 bp. There were two complete operons encoding cytochrome bo3-type ubiquinol terminal oxidases: cyoABCD-1 and cyoABCD-2. The cyoABCD-1 operon was phylogenetically common to AAB genomes, whereas the cyoABCD-2 operon belonged to a lineage distinctive from the cyoABCD-1 operon. Interestingly, cyoABCD-1 was less expressed under co-culture conditions than under the AAB culture conditions, whereas the converse was true for cyoABCD-2. Asaia bogorensis shared pathogenesis-related genes with another pathogenic AAB, Granulibacter bethesdensis, including a gene coding pathogen-specific large bacterial adhesin and additional genes for the inhibition of oxidation and antibiotic resistance. Expression alteration of the respiratory chain and unique hypothetical genes may be key traits that enable the bacterium to survive under the co-culture conditions.
Analytical Approach for Segmentation of Mobile Customers using Data Mining Technique

In today’s world data mining have increasingly beco me very interesting and popular in terms of all app lication. Telecommunication companies generate a tremendous a mount of data. These data include call detail data, which describes the calls that traverse the telecommunication netwo rks. Data is a very important ingredient and extrac ting knowledge from the data serves great purposes to the users . This w ork helps in identifying the activities Segmentatio n of mobile customers of different groups will be done based on some rules. The basic principles of data mining is to analyze t h data from different angle, categorize it and finally to summarize it. T he need for data mining is that we have too much da ta, too much technology but don’t have useful information. Data mining soft ware allows user to analyze data. This paper introd uces the key principle of data pre-processing, Segmentation, classificatio n, clustering and introduction of WEKA tool. Weka i s a data mining tool. It provides the facility to classify the data throu gh various algorithms. Data mining is the process of analyzing data from different viewpoints and summarizing it into useful information. Data mining is one of the top researc h areas in recent days. WEKA is a data mining tool, it provides the facilit y to classify and cluster the data through machine learning algorithm.
DMP Report : Summer 2006 Parametric models for similarity-based classification

Keywords: parametric, classifier, similarity-based. A parametric classifier builds class models and classifies test points as being from classes that they are most likely to belong to, given the class models. Over the summer, I worked with Professor to develop a parametric classifier that builds models based on only the similarity between samples. The parametric clas-sifier was compared to other traditional classifiers, including near-neighbor methods, centroid-based classi-fiers, and support vector machines.
Credit Rationing, Output Gap, and Business Cycles

The cost-of-financing channel version of the financial accelerator proposed by Bernanke & Gertler [1989] is prominent in the literature. Yet, this particular channel has not been validated by empirical work. This paper presents an alternative version of the accelerator. This new accelerator, based on quantity credit rationing, is shown to be more powerful than the traditional accelerator. By causing factor under-utilization credit rationing generates an output gap persistent and sensitive to technology shocks. This accelerator is not a substitute to the traditional mechanism though, but rather a complement. My model helps improve the understanding of financial transmission mechanisms. It considers several types of collaterals. Financial frictions generate persistence when collaterals take the form of tangible assets. They generate amplification when collaterals take the form of cash flows or when asset prices are variable. JEL Classification: E32, E44