Miracle-dz/UniFAR-FTUs huggingface dataset:
A Privacy Preserving Clustering Approach Based on Principle Component Analysis

-The significant advances in data collection and data storage technologies, large databases are maintained by the organizations. Private and sensitive data available in these databases need to be preserved. Privacy preserving data mining is one of the popular research areas which allow sharing of privacy-sensitive data for analysis purpose. In this paper, a hybrid method is proposed for privacy preserved clustering in centralized database environment. In this hybrid method, Principle Component Analysis (PCA) and glide transformation are used as a combination to obtain the distorted dataset. Different people have different privacy requirements. The proposed hybrid method considering the different people privacy requirements and distort the samples in the database at different degrees. The experimental results demonstrate that the proposed hybrid method efficiently protects the private data of individuals and retains the important information for clustering analysis.
PlanIt: A crowdsourcing approach for learning to plan paths from large scale preference feedback

We consider the problem of learning user preferences over robot trajectories for environments rich in objects and humans. This is challenging because the criterion defining a good trajectory varies with users, tasks and interactions in the environment. We represent trajectory preferences using a cost function that the robot learns and uses it to generate good trajectories in new environments. We design a crowdsourcing system - PlanIt, where non-expert users label segments of the robot's trajectory. PlanIt allows us to collect a large amount of user feedback, and using the weak and noisy labels from PlanIt we learn the parameters of our model. We test our approach on 122 different environments for robotic navigation and manipulation tasks. Our extensive experiments show that the learned cost function generates preferred trajectories in human environments. Our crowdsourcing system is publicly available for the visualization of the learned costs and for providing preference feedback: http://planit.cs. cornell.edu.
IT, Proactive Environmental Strategy and Firm Performance: A Resource-based Analysis

The study of the relationships between information technology (IT), environmental organizational issues and performance is a cutting-edge research topic for information systems (IS) research. However, at present we know very little about these relationships. Drawing on the perspective of IT-enabled capabilities and the literature on organizations and the natural environment, our study introduces the construct capability of proactive corporate environmental strategy (ES) to the IS community. Through a resource-based analysis, we propose that IT capability may enable the implementation of a proactive ES and that this strategy could play a significant role in determining the business value of IT. Using structural equations modelling (SEM) with data collected from 63 firms, we find that IT capability is an enabler of proactive ES and that this strategy plays a significant role in mediating the effects of IT capability on firm performance.
Effect of electron—electron scattering on the electrical and thermal conductivity of metals

The effect of interelectronic scattering processes on the electrical and thermal conductivity of metals is considered for two simple cases: one unfilled parabolic band and two unfilled parabolic bands corresponding to one s-band and one inverted d-band. The first case corresponds to an alkali metal and the second to a transition metal. Electronhole scattering is also considered and is found to have effects of the same magnitude as electron-electron scattering on the thermal conductivity of the metals. (C.E.S.)
Nonlinear zero dynamics analysis and control of a low power gas turbine. (Research report SCL-003/2005.)

Background. Gas turbines are highly nonlinear systems, where the widely applied linear controllers based on locally linearized models are of limited use. Method of approach. A simple nonlinear dynamic model is developed that is in a quasi-polynomial form. Nonlinear stability and zero dynamics analysis is applied to find a controller structure for input-output linearization. Results. The estimated stability neighborhood of the system wit h turbine inlet pressure held constant is 30-35% of the operating domain of the turbine. The servo contr oller for the rotational speed is asyptotically stable and robust against disturbances in the operating domain. Conclusions. The proposed servo controller has better qualitative and quanti tative properties than a servo controller based on a locally linearized model.
Effect of Openings on Elephant’s Foot Buckling of Large Oil Tanks

Large non-anchored welded steel oil tanks are important equipments that are renowned for their extremely large structures, they are vulnerable to elephant’s foot buckling failure which can cause serious damages in seismic. Generally, locations of tank openings are at the first layer of shell course which are just close to elephant’s foot buckling position of oil tanks. While learning the effect of openings on elephant’s foot buckling of oil tanks, the welding residual deformation was assumed to be initial geometric imperfections. Numerical simulation method was adopted to analyze the effect of three factors (oil tanks with or without openings, opening sizes and opening positions) on elephant’s foot buckling critical load of oil tanks. Results show that the openings and reinforcement plates are beneficial to the increase of buckling critical load; the size and location of the openings have little effect on the buckling critical load.
nTD: Noise-Profile Adaptive Tensor Decomposition

Tensor decomposition is used for many web and user data analysis operations from clustering, trend detection, anomaly detection, to correlation analysis. However, many of the tensor decomposition schemes are sensitive to noisy data, an inevitable problem in the real world that can lead to false conclusions. The problem is compounded by over-fitting when the user data is sparse. Recent research has shown that it is possible to avoid over-fitting by relying on probabilistic techniques. However, these have two major deficiencies: (a) firstly, they assume that all the data and intermediary results can fit in the main memory, and (b) they treat the entire tensor uniformly, ignoring potential non-uniformities in the noise distribution. In this paper, we propose a Noise-Profile Adaptive Tensor Decomposition (nTD) method, which aims to tackle both of these challenges. In particular, nTD leverages a grid-based two-phase decomposition strategy for two complementary purposes: firstly, the grid partitioning helps ensure that the memory footprint of the decomposition is kept low; secondly (and perhaps more importantly) any a priori knowledge about the noise profiles of the grid partitions enable us to develop a sample assignment strategy (or s-strategy) that best suits the noise distribution of the given tensor. Experiments show that nTD's performance is significantly better than conventional CP decomposition techniques on noisy user data tensors.
Lessons for participatory designers of social media: long-term user involvement strategies in industry

Social media changes the conditions for user participation in service development. Active user communities, fast paced iterative development, considerable development after market launch, developer access to users' digital trails, peer production, and low cost feature distribution are well known facets that bring substantial changes. In this paper we distil lessons for participatory designers from an in-depth case study of an over decade-long service development in industry, Habbo Hotel by Sulake Corporation. We argue that the range of core issues that shape user participation in social media can be captured by three interrelated issues: 1) shifts in developer--user social distance, 2) cumulated user knowledge beyond one project, and 3) user-generated content and user-owned services. We then consider what insight these provide for a design initiative we are involved in: the Finnish national public service broadcasting company's teacher resource.
Th17 cells in natural SIV hosts

Purpose of reviewTo summarize our current understanding of the regulation of Th17 cells in pathogenic and nonpathogenic lentiviral infections. Recent findingsIt has been shown that Th17 cells, a recently identified T helper-cell subset deemed critical for antimicrobial mucosal immunity, are preferentially depleted in the gastrointestinal tracts of human immunodeficiency virus (HIV)-infected humans and simian immunodeficiency virus (SIV)-infected rhesus and pigtailed (PTMs) macaques. In contrast, Th17 cells are preserved at healthy levels in monkey species that are natural hosts for SIV, such as sooty mangabeys or African green monkeys (AGMs), which maintain mucosal immunity and remain AIDS free. These findings suggest that preservation of Th17 cells (or lack thereof) may be central in determining the pathogenic or nonpathogenic outcome of HIV/SIV infection. SummaryA preferential depletion of mucosal Th17 cells is a feature that distinguishes pathogenic HIV infection of humans from nonprogressive SIV infection of sooty mangabeys and AGMs. The exact mechanism accounting for this different phenotype is still unclear. To understand how natural hosts for SIV preserve Th17 cells and mucosal immunity might be central to the development of therapeutic interventions aimed at improving mucosal immunity in HIV-infected individuals.
Diagnostic Performance of an Anti‐Actin Autoantibody Binding Enzyme Immunodot Blot in Autoimmune Hepatitis Type 1

A serologic hallmark of autoimmune hepatitis (AIH) type 1 are anti‐smooth muscle autoantibodies (ASMA) with specificity for filamentous actin (F‐actin; AAA (anti‐actin antibodies)), traditionally detected by indirect immunofluorescence (IFT) using rat liver, kidney, and stomach tissue sections as substrates. However, IFT is a subjective method requiring an experienced investigator. Therefore, a more objective technique for the detection of AAA may be a helpful diagnostic tool.
Influence of Opinion Dynamics on the Evolution of Games

Under certain circumstances such as lack of information or bounded rationality, human players can take decisions on which strategy to choose in a game on the basis of simple opinions. These opinions can be modified after each round by observing own or others payoff results but can be also modified after interchanging impressions with other players. In this way, the update of the strategies can become a question that goes beyond simple evolutionary rules based on fitness and become a social issue. In this work, we explore this scenario by coupling a game with an opinion dynamics model. The opinion is represented by a continuous variable that corresponds to the certainty of the agents respect to which strategy is best. The opinions transform into actions by making the selection of an strategy a stochastic event with a probability regulated by the opinion. A certain regard for the previous round payoff is included but the main update rules of the opinion are given by a model inspired in social interchanges. We find that the fixed points of the dynamics of the coupled model are different from those of the evolutionary game or the opinion models alone. Furthermore, new features emerge such as the independence of the fraction of cooperators with respect to the topology of the social interaction network or the presence of a small fraction of extremist players.
Predicting helix–helix interactions from residue contacts in membrane proteins

Motivation: Helix–helix interactions play a critical role in the structure assembly, stability and function of membrane proteins. On the molecular level, the interactions are mediated by one or more residue contacts. Although previous studies focused on helix-packing patterns and sequence motifs, few of them developed methods specifically for contact prediction. Results: We present a new hierarchical framework for contact prediction, with an application in membrane proteins. The hierarchical scheme consists of two levels: in the first level, contact residues are predicted from the sequence and their pairing relationships are further predicted in the second level. Statistical analyses on contact propensities are combined with other sequence and structural information for training the support vector machine classifiers. Evaluated on 52 protein chains using leave-one-out cross validation (LOOCV) and an independent test set of 14 protein chains, the two-level approach consistently improves the conventional direct approach in prediction accuracy, with 80% reduction of input for prediction. Furthermore, the predicted contacts are then used to infer interactions between pairs of helices. When at least three predicted contacts are required for an inferred interaction, the accuracy, sensitivity and specificity are 56%, 40% and 89%, respectively. Our results demonstrate that a hierarchical framework can be applied to eliminate false positives (FP) while reducing computational complexity in predicting contacts. Together with the estimated contact propensities, this method can be used to gain insights into helix-packing in membrane proteins. Availability: http://bio-cluster.iis.sinica.edu.tw/TMhit/ Contact: tsung@iis.sinica.edu.tw Supplementary information:Supplementary data are available at Bioinformatics online.
Andretic miniature brain : setting arousal thresholds in a Drosophila Dopamine in

References ml#ref-list-1 http://rspb.royalsocietypublishing. org/content/early/2011/01/04/rspb.2010. 2564.full. ht This article cites 57 articles, 13 of which can be accessed free P<P Published online 5 January 2011 in advance of the print journal. Subject collections (414 articles) cognition Articles on similar topics can be found in the following collections Email alerting service here right-hand corner of the article or click Receive free email alerts when new articles cite this article sign up in the box at the top
The Patient Experience of Depression and Remission: Focus Group Results

Few depression measures were developed with the patient perspective. To obtain patient views on depression and early symptom resolution, 4 focus groups (N = 31) were conducted. Patients also completed the Quick Inventory of Depressive Symptomology (Self-Report), SF-36, and a rating of the bothersomeness and significance of 12 symptoms of depression. Transcripts were reviewed for major themes. Irritability was a key symptom and it remitted earlier than others. Important to participants were low mood, low energy, lack of motivation, lack of focus/concentration, feelings of guilt, self-critical thoughts, feeling overwhelmed, lack of enjoyment, hypersomnia, restlessness, anger, and irritability. Gender differences emerged; most men reported irritability as one of the first symptoms to remit; for women, motivation level and energy commonly remitted first. Results suggest that new measures of treatment outcome should encompass irritability, anger, and ability to cope with life stressors.
User-oriented key management scheme for content protection in OPMD environment

In this paper, we propose a key management scheme for content protection in the OPMD (One Person Multi Device) environment where one user owns multiple devices. IBTD (ID-Based Threshold Decryption) was applied for the proposed scheme, and this uses the user IDs and the device IDs as their public key. It is the key management scheme that can decrypt the encrypted content only when more than t secret sharing values are combined among the n secret sharing values generated by the contents server. Our scheme enables the users to use their own contents anytime and anywhere on any device that they own. For the contents server, it enables an effective key management which is user-oriented, not device-oriented.
Neisseria gonorrhoeae II. Colonial Variation and Pathogenicity During 35 Months In Vitro

During 35 months of selective in vitro cultivation, Neisseria gonorrhoeae cells retained their virulence for humans and were shown to be closely related to a particular colonial morphology. Saline-autoagglutinability was the only other characteristic distinguishing virulent from avirulent cells. Human responses to challenge with cells of the different colonial types were studied for their relationships to virulence or avirulence.
Big data @ facebook

The Facebook Data Infrastructure supports a wide range of applications, including both external facing products and services and internal applications. This paper focuses on the Data Warehousing and Analytics platform of Facebook that provides support for batch-oriented analytics applications. Facebook's data infrastructure is built largely on top of open-source technologies such as Apache Hadoop, HDFS, MapReduce and Hive, and provides a rich set of tools for different users to perform analytics queries on Facebook data. As the Facebook user base continues to grow, we continue to enhance our data platform in order to deal with the challenges of scaling with increasing amounts of data.
Differential Antitumor Activity of Aflibercept and Bevacizumab in Patient-Derived Xenograft Models of Colorectal Cancer

The recombinant fusion protein aflibercept (ziv-aflibercept in the United States) binds VEGF-A, VEGF-B, and placental growth factor (PlGF). The monoclonal antibody bevacizumab binds VEGF-A. Recent studies hypothesized that dual targeting of VEGF/PlGF is more beneficial than targeting either ligand. We compared activity of aflibercept versus bevacizumab in 48 patient-derived xenograft (PDX) colorectal cancer models. Nude mice engrafted subcutaneously with PDX colorectal cancer tumors received biweekly aflibercept, bevacizumab, or vehicle injections. Differential activity between aflibercept and bevacizumab, determined by mouse (m), human (h), VEGF-A, and PlGF levels in untreated tumors, was measured. Aflibercept induced complete tumor stasis in 31 of 48 models and bevacizumab in 2 of 48. Based on statistical analysis, aflibercept was more active than bevacizumab in 39 of 48 models; in 9 of 39 of these models, bevacizumab was considered inactive. In 9 of 48 remaining models, aflibercept and bevacizumab had similar activity. Tumor levels of hVEGF-A (range 776–56,039 pg/mg total protein) were ∼16- to 1,777-fold greater than mVEGF-A (range 8–159 pg/mg total protein). Tumor levels of mPlGF (range 104–1,837 pg/mg total protein) were higher than hPlGF (range 0–543 pg/mg total protein) in 47 of 48 models. Tumor cells were the major source of VEGF; PlGF was primarily produced by tumor stroma. Because tumor levels of hVEGF-A were far greater than mVEGF-A, bevacizumab's inability to bind mVEGF-A is unlikely to explain higher and more consistent aflibercept activity. Neutralizing PlGF and VEGFR-1 activation may be a factor and should be investigated in future studies. In these colorectal cancer PDX models, aflibercept demonstrated greater antitumor activity than bevacizumab. Mol Cancer Ther; 13(6); 1636–44. ©2014 AACR.
Vascular dementia: a radical redefinition.

'Vascular dementia' may be the leading cause of cognitive impairment in the world, yet there is little agreement as to what this concept encompasses or how it is defined. A critical review reaches the conclusion that the concept of 'vascular dementia' has become obsolete. 'Vascular' is too generic, and fails to identify specific etiologies which may be subject to current and future preventive measures. 'Dementia' identifies patients too late to do much about their problem. An alternate approach is suggested. Identify patients across the whole spectrum of vascular cognitive impairment, from high risk with no deficit ('brain-at-risk stage') to full-blown dementia. Describe the cognitive impairment in terms of standardized neuropsychological measures, and relate the dementia to the specific vascular cause, so that the appropriate preventive measures can be implemented.
Smart Artificial Firefly Colony Algorithm‐Based Support Vector Regression for Enhanced Forecasting in Civil Engineering

Advanced data mining techniques are potential tools for solving civil engineering (CE) problems. This study proposes a novel smart artificial firefly colony algorithm‐based support vector regression (SAFCA‐SVR) system that integrates firefly algorithm (FA), chaotic maps, adaptive inertia weight, Lévy flight, and least squares support vector regression (LS‐SVR). First, adaptive approach and randomization methods are incorporated in FA to construct a novel and highly effective metaheuristic algorithm for global optimization. The enhanced FA is then used to optimize parameters in LS‐SVR model. The proposed system is validated by comparing its performance with those of empirical methods and previous works via cross‐validation algorithm and hypothesis test through the real‐world engineering cases. Specifically, high‐performance concrete, resilient modulus of subgrade soils, and building cooling load are used as case studies. The SAFCA‐SVR achieved 8.8%–91.3% better error rates than those of previous works. Analytical results confirm that using the proposed hybrid system significantly improves the accuracy in solving CE problems.
Autonomous Machine Learning

Inspiration from human learning sets the focus on one essential but poorly studied characteristic of learning: Autonomy. One remarkable characteristic of human learning is that, whereas we are in general not excellent in one specific domain, we are quite good in most of them, and able to adapt when a new problem appears. We are versatile and adaptable, which are critical properties for autonomous learning: we can learn in a changing and uncertain world. With neither explicit labels, nor data preprocessing or segmentation, we are able to pay attention to important information and neglect noise. We define by ourselves our goals and the means to reach them, self-evaluate our performances and re-exploit previously learned knowledge and strategies in some different context. In contrast, recent advances in Machine Learning exhibit impressive results, with powerful algorithms surpassing human performances in some very specific domains of expertise, but these models still have very poor autonomy.
Impact of hospital-related factors on outcome after treatment of cerebral aneurysms.

BACKGROUND AND PURPOSE The goal of this study was to examine the impact of hospital characteristics on outcome after the treatment of ruptured and unruptured cerebral aneurysms. METHODS We identified all discharges in New York State from 1995 through 2000 with a principal diagnosis of subarachnoid hemorrhage (SAH) or unruptured cerebral aneurysm (UCA) in patients who were treated by aneurysm clipping, wrapping, or endovascular coiling. An adverse outcome was defined as in-hospital death or discharge to a rehabilitation hospital or long-term facility. We examined the effect of hospital factors, including the rate of endovascular therapy and overall procedural volume, on outcome, length of stay, and total charges. RESULTS There were 2200 (36.9%) and 3763 (63.1%) admissions for attempted treatment of UCA and SAH, respectively. The 10 highest-volume hospitals performed half of all the procedures. Overall, hospital volume was associated with fewer adverse outcomes and lower in-hospital mortality for both UCA (adverse outcome: odds ratio [OR], 0.89; P<0.0001; mortality: OR, 0.94; P=0.002 for each 10 additional procedures performed per year) and SAH (adverse outcome: OR, 0.94; P=0.03; mortality: OR, 0.95; P=0.005). Use of endovascular therapy (each additional 10% of cases performed endovascularly) was associated with fewer adverse outcomes after treatment of unruptured aneurysm (0.83, P=0.026). Hospital volume had more of an effect on outcome after aneurysm clipping than after endovascular therapy. CONCLUSIONS Hospital procedural volume and the propensity of a hospital to use endovascular therapy are both independently associated with better outcome. Improvement in outcome could be achieved by a program of regionalization and selective referral for the treatment of cerebral aneurysms.