Miracle-dz/UniFAR-FTUs huggingface dataset:
A Software Engineering Approach to Scientific Data Management

1. Introduction 1.1 A changing science An ecologist stands under an enormous hemlock tree deep in the New England forest. She marks down the number of a tag on the tree on a sheet of paper, measures its girth, and notes its health and what plants are growing around it. After examining several dozen more trees, she will put her notes in a filing cabinet with similar ones from past years, and eventually the ecologist or one of her students will analyze the collected data and publish their conclusions. A few yards from the hemlock stands an 80-foot tall scaffolding structure, steadied by guy wires. The sensors this eddy-flux tower holds above the forest canopy take measurements of environmental variables such as atmospheric turbulence, carbon dioxide flux, photosynthetically active radiation, and wind speed every few milliseconds, year-round, then wirelessly send them to a database accessible by anyone with an Internet connection. Over the next few years, teams of researchers from several universities across the country will download the data, integrate it with measurements from stations located in other ecosystems, perform analyses, and potentially publish many papers on different aspects of the collected data. The ecologist represents a traditional kind of science that, while essential, is rapidly being eclipsed by the advancement of technology. New data collection capabilities, such as sensor networks, combined with powerful computational techniques make it possible for new, large-scale questions to be investigated in disciplines such as particle physics, systems biology, climate change, and ecosystem ecology. At the same time, the scale of the datasets involved creates equally large data management challenges. Many of these issues rest simply on the fact that it can be difficult to keep track of what has been done with any subset or to the whole of any given scientific dataset. A single dataset used in the new information-intensive sciences is no longer a physical folder of plot inventory sheets or even digital pages in a spreadsheet, but often is a complex synthetic product of several initial sets. Data frequently undergoes quality control and other preliminary computational steps before analysis is even begun. A lack of knowledge about these procedures can further complicate data management and analysis in a variety of ways. Metadata about the origins and subsequent transformations of data is called provenance, and its production and use is an important challenge facing today's scientific community. The term "provenance" itself is …
An Iterative Soft Bit Error Rate Estimation of Any Digital Communication Systems Using a Nonparametric Probability Density Function

In general, performance of communication system receivers cannot be calculated analytically. The bit error rate (BER) is thus computed using the Monte Carlo (MC) simulation (Bit Error Counting). It is shown that if we wish to have reliable results with good precision, the total number of transmitted data must be conversely proportional to the product of the true BER by the relative error of estimate. Consequently, for small BERs, simulation results take excessively long computing time depending on the complexity of the receiver. In this paper, we suggest a new means of estimating the BER. This method is based on an estimation, in an iterative and nonparametric way, of the probability density function (pdf) of the soft decision of the received bit. We will show that the hard decision is not needed to compute the BER and the total number of transmitted data needed is very small compared to the classical MC simulation. Consequently, computing time is reduced drastically. Some theoretical results are also given to prove the convergence of this new method in the sense of mean square error (MSE) criterion. Simulation results of the suggested BER are given using a simple synchronous CDMA system.
Iron lines from transient and persisting hot spots on AGN accretion disks

Aims. We model the X-ray reprocessing from a strong co-rotating flare above an accretion disk in active galactic nuclei. By performing detailed radiative transfer computations we explore the horizontal structure and evolution of the underlying hot spot. The main goal is to study how the resulting spectral features manifest themselves in short exposure time spectra. Methods. We analyze both the vertical and the horizontal spot structure and its dynamical reprocessed spectrum. To obtain the spectral evolution seen by a distant observer, we apply a general relativity ray-tracing technique. We concentrate on the energy band around the iron K-line, where the relativistic effects are most pronounced. Persistent flares lasting for a significant fraction of the orbital time scale and short, transient flares are considered. Results. In our time-resolved analysis, the spectra recorded by a distant observer depend on the position of the flare/spot with respect to the central black hole. If the flare duration significantly exceeds the light travel time across the spot, then the spot horizontal stratification is unimportant. On the other hand, if the flare duration is comparable to the light travel time across the spot radius, the lightcurves exhibit a typical asymmetry in their time profiles. The sequence of dynamical spectra proceeds from more strongly to less strongly ionized re-emission. At all locations within the spot the spectral intensity increases towards edge-on emission angles, revealing the limb brightening effect. Conclusions. Future X-ray observatories with significantly larger effective collecting areas will enable to spectroscopically map out the azimuthal irradiation structure of the accretion disk and to localize persistent flares. If the hot spot is not located too close to the marginally stable orbit of the black hole, it will be possible to probe the reflecting medium via the sub-structure of the iron K-line. Indications for transient flares will only be obtained from analyzing the observed lightcurves on the gravitational time scale of the accreting supermassive black hole.
Comparative investigation of antioxidant activity of human serum blood by amperometric, voltammetric and chemiluminescent methods

Introduction A blood test can provide important information about the functional state of the antioxidant system. Malfunction of this system increases the concentration of free radicals and can cause oxidative stress. A difficulty in assessing oxidative stress is the lack of a universal method for determining the antioxidant activity (AOA) of blood components, because of their different nature. Material and methods The objects of investigation were sera of 30 male patients with a diagnosis of alcohol dependence syndrome and healthy donors. Comparative investigation of total antioxidant activity (TAA) of human serum blood was carried out by voltammetric (VA), amperometric (AM) and chemiluminescent (HL) methods. Results All applied methods revealed that serum TAA of the patients with alcoholism is lower than TAA of healthy donors (control group); according to amperometric method the average value of serum TAA was 850 ±210 nA × s, and 660 ±150 nA × s for healthy donors and alcoholics respectively (p < 0.05). Similar trend was revealed by chemiluminescence and voltammetry methods. The results confirm that thiol compounds make a significant contribution to the antioxidant activity of serum. The average thiol concentrations were 0.94 ±0.34 mmol/l and 1.21 ±0.36 mmol/l (p < 0.05) for alcoholics and healthy donors respectively. Decreasing thiol concentration in blood of alcoholics leads to depletion of antioxidant systems of blood. However, the differences between the results of AM, VA and HL methods were significant, because they reflected different aspects of antioxidant activity. Conclusions For objective assessment of antioxidant activity of biological objects, we suggest using methods based on different model systems.
HyperCubeMap: Optimal social network ad allocation using hyperbolic embedding

Advertising activity on SNS has grown rapidly and is now a billion dollar business. In the SNS advertising model, the SNS serves as the advertising agent, and takes the advantage of network diffusion to attract advertisers and charges for the cascading impressions. The optimal ad allocation task is to choose the ad allocation plan that maximizes the revenue. Each user has different diffusion ability, limited daily impressions and the advertisers have various bidding prices and budget concerns. A feasible plan that obeys the constraints is difficult to find. The solution of this problem lies in the space of No|Ads|×|User|, which makes direct optimization unattractive. In this paper, we study SNS advertising business models, formulate the SNS ad allocation problem and show their connections with hyperbolic embedding. We develop a new embedding algorithm HyperCubeMap that allows for dimension reduction. Our proposed method reduces the dimensionality of the original problem significantly, runs two to four orders of magnitude faster, and reaches 95% of the optimum.
Discovering Personal Paths from Sparse GPS Traces

Personal paths capture “personal meaningful places” [13, 14] in temporal sequence. Knowledge of a user’s paths enables novel and useful features for location-aware applications, e.g., traffic condition updates for commuting routes, carpool partner finding with similar commuting routes and schedules. Prior work has explored algorithms to discover “significant locations” [1,2] and “transportation routes” [6, 9] from GPS data, however, we know of no algorithms specifically designed for sparse GPS traces, which represent typical location datasets collected from GPS enabled mobile devices. In this paper, we report two spatio-temporal clustering algorithms, TDJ and R-TDJ, for discovering personal paths. Specifically, the algorithms are designed to address the noisy and sparse nature of GPS data. Our experiment results show that both TDJ and R-TDJ discovered meaningful spatio-temporal clusters to form personal paths. R-TDJ demonstrates better performance on sparse GPS data.
Benchmark Concentration: Capitalization Weights versus Equal Weights in the FTSE 100 Index

Identifying a suitable benchmark is essential when testing asset pricing models, measuring the performance of active investors, or providing market proxy portfolios for passive investors. Concern that increased domination of capitalization weighted stock indices by a few large firms will lead to inefficient portfolio diversification is leading some investors and researchers to argue that index providers should adjust their weighting methods to limit concentration. This study tests and rejects the hypothesis that concentration arising as a result of capitalization weights in the FTSE 100 Index increases risk, either during normal market conditions or during negative tail events in the return distribution. On the contrary, during the left tail of the return distribution, the equally weighted portfolio of FTSE 100 Index constituents exhibits higher risk and lower returns than the capitalization weighted FTSE 100 Index portfolio, a finding consistent with variations of the CAPM that allow for time varying risk premia.
Relationship satisfaction and the subjective distance of past relational events

The present research examines how the subjective time of relational memories is linked to present relationship satisfaction. We tested the hypothesis that satisfied (but not dissatisfied) partners would keep happy relational events subjectively close in time and relegate transgressions to the subjectively distant past (regardless of when those events actually occurred). We found support for our predictions in the context of romantic relationships (Study 1) and with any type of close other (e.g., friends, family members; Study 2). To better understand the implications of the subjective distancing pattern among highly satisfied versus dissatisfied partners, we examined the role of perceptions of event importance. We found that highly satisfied partners’ adaptive pattern of distancing mediates their tendency to ascribe continued importance to past relationship glories, while dismissing earlier relational disappointments as unimportant (Study 2). We then examined the causal impact of subjective time on importance and on subsequent relationship satisfaction by manipulating both event valence and perceptions of subjective distance (Study 3). People were more satisfied when happy relational events felt close and unhappy ones felt distant. This work sheds light on a reciprocal process whereby highly satisfied partners navigate the temporal landscape of their relational histories by retaining and valuing happy memories and by discarding the relevance of painful ones, which then maintains or boosts subsequent relationship satisfaction.
The Intensified Blurring of Boundaries Between Work and Private Life through IT Consumerisation

Consumer technologies have intensified the blurring between work and private spaces. For instance, employees increasingly use privately owned devices for work or company provided devices for private tasks. By means of a multiple case study in four organisations, we investigate the current use of consumer IT in organisations in relation to the increased blurring between work and private life. With regard to boundary theory, we are able to identify six technology-related aspects that describe the intensifying role of IT consumerisation in terms of blurring boundaries. Whether people strive for work-life integration or segmentation, they often experience conflicts in realising their individual preference. However, it seems that IT consumerisation further moved the standard for work-life blurring towards integration rather than segmentation. The number of identified conflicts suggests that there is still a lot of potential for software vendors and user companies to make the management of work and private life spaces more feasible. More specifically, there is a need for solutions that better target individual preferences towards work-life blurring. Our study sheds further light on both increasing chances and challenges that consumer technology puts on employees and organisations.
A Product Partition Model With Regression on Covariates

We propose a probability model for random partitions in the presence of covariates. In other words, we develop a model-based clustering algorithm that exploits available covariates. The motivating application is predicting time to progression for patients in a breast cancer trial. We proceed by reporting a weighted average of the responses of clusters of earlier patients. The weights should be determined by the similarity of the new patient’s covariate with the covariates of patients in each cluster. We achieve the desired inference by defining a random partition model that includes a regression on covariates. Patients with similar covariates are a priori more likely to be clustered together. Posterior predictive inference in this model formalizes the desired prediction. We build on product partition models (PPM). We define an extension of the PPM to include a regression on covariates by including in the cohesion function a new factor that increases the probability of experimental units with similar covariates to be included in the same cluster. We discuss implementations suitable for any combination of continuous, categorical, count, and ordinal covariates. An implementation of the proposed model as R-package is available for download.
Dimensions of Anthropomorphism : From Humanness to Humanlikeness

In HRI anthropomorphism has been considered to be a unidimensional construct. However, social psychological studies of the potentially reverse process to anthropomorphisation - known as dehumanization - indicate that there are two distinct senses of humanness with different consequences for people who are dehumanized by deprivation of some of the aspects of these dimensions. These attributes are crucial for perception of others as humans. Therefore, we hypothesized that the same attributes could be used to anthropomorphize a robot in HRI and only a two-dimensional measures would be suitable to distinguish between different forms of making a robot more humanlike. In a study where participants played a quiz based on the TV show “Jeopardy! ” we manipulated a NAO robot’s intelligence and emotionality. The results suggest that only emotionality, not intelligence, makes robots be perceived as more humanlike. Furthermore, we found some evidence that anthropomorphism is a multidimensional phenomenon.Categories and Subject DescriptorsI.2. 9 [Computing Methodologies]: ARTIFICIAL INTELLIGENCE—Robotics
Two stage spectrum sensing for Cognitive Radio using CMME

Spectrum sensing is fundamental functionality in Cognitive Radio (CR) to identify spectral white spaces for opportunistic communication. Around last decade thorough study of spectrum sensing suggests that traditional energy detection (ED) performance is better and fast at high SNR and worst in low SNR, whereas cyclostationary and Eigenvalue based methods perform better at low SNR, but implementation complexity is high. As suggested in IEEE 802.22 standard for CR, we propose in this paper, novel two stage spectrum sensing based on energy detection as coarse sensing first stage and combination of maximum-minimum eigen value based detection technique (CMME) as fine sensing second stage. The dual stage threshold parameter is designed to maximize the probability of detection for given constraints on the probability of false alarm. Comparative analysis of single stage ED, Combination of maximum-minimum eigen value based detection (CMME), Cyclostationary detection technique (CSD), Combination of Cyclostationary detection and ED detection and Combination of CMME and ED detection is performed using probability detection (Pd) versus SNR curve.
Redundant via insertion in directed self-assembly lithography

In directed self-assembly lithography (DSAL), vias that are located close are clustered and patterned together. A large and complex cluster, however, is not allowed in this process due to its potential danger of pattern failure. We address redundant via insertion in DSAL. The goal is to insert maximum number of redundant vias while adjacent vias do not form a large and complex cluster. The problem is formulated as maximum independent set (MIS) of a conflict graph. Experiments demonstrate 13% more redundant vias inserted compared to simple-minded approach, basic insertion with no consideration of DSAL followed by removal of redundant vias in large and complex clusters. We also introduce DSA defect probability in order to quantitatively define which clusters should be allowed during insertion process.
The contrasting roles of N-CAM and N-cadherin as neurite outgrowth- promoting molecules

Summary The neural cell adhesion molecule (N-CAM) is a prominent member of the immunoglobulin gene superfamily of recognition molecules. It operates in a calcium-independent manner to promote cell–cell adhesion. Alternative splicing of a single gene generates more than twenty N-CAM isoforms and these can be further modified by the differential addition of complex N- and O-linked carbohydrates. In contrast, N-cadherin is a major calcium-dependent adhesion molecule in the brain; it is not a member of the immunoglobulin gene superfamily and, as far as we know, exists as a single gene product with no evidence of differential post-translational modification. Both molecules are believed to operate through a homophilic binding mechanism and both are expressed at key developmental times in a number of tissues including the brain. Antibody perturbation experiments suggest that both of the above cell adhesion molecules (CAMs) can support neurite outgrowth over complex cellular substrata such as astrocytes and Schwann cells. In the present review we discuss the use of a molecular genetic approach to study the neurite outgrowth-promoting activity of these molecules. Using this approach we have found that both CAMs are potent-inducers of neurite outgrowth from a variety of neurons. However, whereas a critical value of N-CAM expression is required for increased neurite outgrowth, with small increases above this value having substantial effects, N-cadherin promotes neurite outgrowth in a highly linear manner. In addition, whereas N-CAM promotes chick retinal ganglion cell (RGC) neurite outgrowth at E6 but not E11, N-cadherin does so throughout this developmental period. These studies show fundamental differences in neuronal responsiveness to CAMs, and suggest a more dynamic regulation for N-CAM-dependent neurite outgrowth than for N-cadherin-dependent neurite outgrowth.
Safety , Tolerability , and Mechanisms of Antiretroviral Activity of Pegylated Interferon Alfa-2 a in HIV-1 – Monoinfected Participants : A Phase II Clinical Trial

David M. Asmuth, Robert L. Murphy, Susan L. Rosenkranz, Juan J. L. Lertora, Shyam Kottilil, Yoninah Cramer, Ellen S. Chan, Robert T. Schooley, Charles R. Rinaldo, Nathan Thielman, Xiao-Dong Li, Sharon M. Wahl, Jessica Shore, Jennifer Janik, Richard A. Lempicki, Yaa Simpson, and Richard B. Pollard, for the AIDS Clinical Trials Group A5192 Team University of California–Davis Medical School, Sacramento, and University of California, San Diego; Northwestern University, Evanston, Illinois; Harvard School of Public Health, Boston, Massachusetts; National Institutes of Health Clinical Center, Bethesda, and SAIC-Frederick, Frederick, Maryland; University of Pittsburgh, Pittsburgh, Pennsylvania; Duke University, Durham, North Carolina; Frontier Science and Technology Research Foundation, Amherst, New York
Improving Prediction of MHC Class I Binding Peptides with Additional Binding Data

The ability to predict MHC-binding peptides is of great immunological importance in understanding immune response regulation and designing vaccine. Various computational peptide sequence based methods, such as HMM, ANN, CART and SVM, have been proposed to tackle this problem by learning a predictive model based on known binding data of the MHC molecule. We present a novel computational approach to predict binding affinities of peptides to a specific MHC molecule by learning a predictive model from the binding data of not only this molecule but also of other MHC molecules. This idea is examined in the HLA-A family with the binding nonamers of HLA-A1, A2, A3, A11, A24, A31, A33 and A68 from six publicly available databases of binding peptides. Experimental results show that the prediction accuracy of peptide binding to a specific HLA molecule is improved in most cases by incorporating the binding data of other HLA molecules that have similar peptide binding specificities. Specifically, with the binding data of HLA-A1, A3, A11, A31 and A33, which can be classified into the same supertype in terms of peptide-binding specificities with respect to existing literature, the prediction accuracy of each HLA molecule can be improved by incorporating the binding data of any other HLA molecule in this group. Our method also sheds light on the method of identifying relationships among different MHC molecules.