Miracle-dz/UniFAR-FTUs huggingface dataset:
Beta Band Transcranial Alternating (tACS) and Direct Current Stimulation (tDCS) Applied After Initial Learning Facilitate Retrieval of a Motor Sequence

The primary motor cortex (M1) contributes to the acquisition and early consolidation of a motor sequence. Although the relevance of M1 excitability for motor learning has been supported, the significance of M1 oscillations remains an open issue. This study aims at investigating to what extent retrieval of a newly learned motor sequence can be differentially affected by motor-cortical transcranial alternating (tACS) and direct current stimulation (tDCS). Alpha (10 Hz), beta (20 Hz) or sham tACS was applied in 36 right-handers. Anodal or cathodal tDCS was applied in 30 right-handers. Participants learned an eight-digit serial reaction time task (SRTT; sequential vs. random) with the right hand. Stimulation was applied to the left M1 after SRTT acquisition at rest for 10 min. Reaction times were analyzed at baseline, end of acquisition, retrieval immediately after stimulation and reacquisition after eight further sequence repetitions. Reaction times during retrieval were significantly faster following 20 Hz tACS as compared to 10 Hz and sham tACS indicating a facilitation of early consolidation. tDCS yielded faster reaction times, too, independent of polarity. No significant differences between 20 Hz tACS and tDCS effects on retrieval were found suggesting that 20 Hz effects might be associated with altered motor-cortical excitability. Based on the behavioral modulation yielded by tACS and tDCS one might speculate that altered motor-cortical beta oscillations support early motor consolidation possibly associated with neuroplastic reorganization.
Chapter 2 Personality and the Internet

The Internet is a modern communication technology which is able to create a comprehensive environment. Here, surfers may carry out a variety of social activities, for example, joining a support group or interacting with friends or with people chosen at random. Although millions of people around the world communicate with one another every day, surfing is an individual experience and, therefore, any attempt to understand behavior on the net must involve an examination of the personality of the surfer. Our personality is the source of our emotions, cognition, and behavior. Its structure has been compared to that of an enormous iceberg, of which only the tip is visible above the water level and the rest of its huge mass is hidden beneath. In a similar way, we know and understand only a small part of our personality; most of it remains unrevealed. Interaction over the Internet, with its anonymity, high degree of control, and the ability to find similar others, creates a unique protective environment that encourages people to express themselves more freely than they would in a regular interaction. This may be especially relevant to people who are socially inhibited and so find difficulty in expressing themselves. They may feel that it is only via the Internet that they can communicate effectively. In some cases, this may lead them to give their life in cyberspace precedence over their real life. Thus, the secure Internet environment may have a direct impact on their personality, and it is on this that our chapter will focus. This chapter is divided into four main parts. The first focuses on the issue of interaction between personality and Internet use. The second will discuss the expression of identity over the Internet. In the third part, we move on to discuss two negative social phenomena that are associated with Internet use— loneliness and addiction; and the closing section focuses on new directions in which the Internet can be utilized as a tool for the improvement of human well-being.
Unraveling the Slut Narrative: Gender Constraints on Adolescent Girls' Sexual Decision-Making

ABSTRACT Limited research exists on the slut labeling process, a key means of enforcing rules around appropriate female sexuality. This study explores that process through qualitative interviews with 44 adolescent girls in Travis County, Texas. Labeling girls as sluts or hos was pervasive and was based on a number of factors beyond sexual behavior, including dress, friendships with boys, or jealousy from other girls. Responses depicted a narrow space in which girls functioned, bound at one end by limited agency and at the other by pervasive vulnerability. There was consensus about the negative consequences of being labeled. Most girls held mixed opinions about the slut labeling process: this may reflect their attempts to push back against a system that entraps them. These findings suggest that prevention and sex education programs must explicitly address slut labeling as well as other gender narratives that impede healthy sexual development.
Requirements for a MDE System to Support Collaborative In-Car Communication Diagnostics

Modern automobiles come with a high degree of electronics and an enormous amount of in-car communication activities. This leads to an increasingly complex data volume which challenges automotive engineers in detecting and analyzing erroneous communication processes. In this paper, we present results of our studies on current working behaviour and environments of analysis and diagnosis experts in the automotive industry. While we found a sufficient hardware and software support in single user environments, co-located collaborative environments lack specific software to support collaboration. In particular, we observed a need for support of multiple devices in collaborative multiple display environments (MDEs). After a detailed user analysis and evaluation we present system requirements for information analysis applications in MDEs as required for the analysis of in-car communication activities.
Feynman-Schwinger representation approach to nonperturbative physics

The Feynman-Schwinger representation provides a convenient framework for the calculation of nonperturbative propagators. In this paper we first investigate an analytically solvable case, namely the scalar QED in 0+1 dimension. With this toy model we illustrate how the formalism works. The analytic result for the self-energy is compared with the perturbative result. Next, using a {chi}{sup 2}{phi} interaction, we discuss the regularization of various divergences encountered in this formalism. The ultraviolet divergence, which is common in standard perturbative field theory applications, is removed by using a Pauli-Villars regularization. We show that the divergence associated with large values of Feynman-Schwinger parameter {ital s} is spurious and it can be avoided by using an imaginary Feynman parameter is. {copyright} {ital 1999} {ital The American Physical Society}
A W-Band 4-GHz Bandwidth Phase-Modulated Pulse Compression Radar Transmitter in 65-nm CMOS

This paper presents a fully integrated W-band 4-GHz bandwidth (BW) pseudo-noise (PN)-coded pulse compression radar transmitter (TX) in a CMOS technology. The PN-coded pulse compression scheme is adopted to obtain high spectral density and to lower the TX leakage using a 63-bit PN code generator based on linear feedback shift registers. We propose a sub-harmonic pumped pulse former and a pulsed power amplifier for high TX efficiency with the suppression of local oscillator (LO)/2LO leakage. A frequency synthesizer including a frequency divider chain generates a sub-harmonic LO signal, as well as a 5-GHz digital clock. Digital blocks with the PN-code generator are synchronized with the clock signal, which makes all pulses start with the same phase. The proposed TX achieves 14.5-dBm maximum output power with the tuning range of 75-81.5 GHz, and the phase noise is -95.2 dBc/Hz at a 1-MHz offset in the range of LO frequencies. In pulse mode, it generates a 4-GHz BW RF pulse signal, which corresponds to a range resolution of 7.5 cm, and the average dc power dissipation is 160 mW.
Automatic scenario detection for improved WCET estimation

Modern embedded applications usually have real-time constraints and they are implemented using heterogeneous multiprocessor systems-on-chip. Dimensioning a system requires accurate estimations of the worst-case execution time (WCET). Overestimation leads to over-dimensioning. This paper introduces a method for automatic discovery of scenarios that incorporate correlations between different parts of applications. It is based on the application parameters with a large impact on the execution time. The authors showed on a benchmark that, using scenarios, the estimated WCET might be reduced with 16%.
Storyboarding Form-B ased Interfaces

A bstr act: T his paper identifies two-staged interaction as the abstract concept behind form-based interfaces. From this we derive form storyboarding, a method for eliciting, specifying and communicating functional requirements for applications with form-based interfaces. T he method encompasses a visual language for the documents which have to be created, and a set of proposals for activities. T he visual language is based on bipartite state transition diagrams.
Relic density of dark matter in the next-to-minimal supersymmetric standard model

We present a code for computing the relic density of dark matter in the next-to-minimal supersymmetric standard model (NMSSM). Dominant corrections to the Higgs masses are calculated with NMHDECAY as well as theoretical and collider constraints. All neutralino annihilation and coannihilation processes are then computed with an extended version of micrOMEGAs, taking into account higher order corrections to Higgs vertices. We explore the parameter space of the NMSSM and consider in particular the case of a bino LSP, of a mixed bino–higgsino LSP and of a singlino LSP. As compared to the case for the MSSM, neutralino annihilation is often more efficient, as it can take place via (additional) Higgs resonances as well as annihilation into light Higgs states. Models with a large singlino component can be compatible with WMAP constraints.
Modeling and detection techniques for Counter-Terror Social Network Analysis and Intent Recognition

In this paper, we describe our approach and initial results on modeling, detection, and tracking of terrorist groups and their intents based on multimedia data. While research on automated information extraction from multimedia data has yielded significant progress in areas such as the extraction of entities, links, and events, less progress has been made in the development of automated tools for analyzing the results of information extraction to “connect the dots. ” Hence, our Counter-Terror Social Network Analysis and Intent Recognition (CT-SNAIR) work focuses on development of automated techniques and tools for detection and tracking of dynamically-changing terrorist networks as well as recognition of capability and potential intent. In addition to obtaining and working with real data for algorithm development and test, we have a major focus on modeling and simulation of terrorist attacks based on real information about past attacks. We describe the development and application of a new Terror Attack Description Language (TADL), which is used as a basis for modeling and simulation of terrorist attacks. Examples are shown which illustrate the use of TADL and a companion simulator based on a Hidden Markov Model (HMM) structure to generate transactions for attack scenarios drawn from real events. We also describe our techniques for generating realistic background clutter traffic to enable experiments to estimate performance in the presence of a mix of data. An important part of our effort is to produce scenarios and corpora for use in our own research, which can be shared with a community of researchers in this area. We describe our scenario and corpus development, including specific examples from the September 2004 bombing of the Australian embassy in Jakarta and a fictitious scenario which was developed in a prior project for research in social network analysis. The scenarios can be created by subject matter experts using a graphical editing tool. Given a set of time ordered transactions between actors, we employ social network analysis (SNA) algorithms as a filtering step to divide the actors into distinct communities before determining intent. This helps reduce clutter and enhances the ability to determine activities within a specific group. For modeling and simulation purposes, we generate random networks with structures and properties similar to real-world social networks. Modeling of background traffic is an important step in generating classifiers that can separate harmless activities from suspicious activity. An algorithm for recognition of simulated potential attack scenarios in clutter based on Support Vector Machine (SVM) techniques is presented. We show performance examples, including probability of detection versus probability of false alarm tradeoffs, for a range of system parameters.
How Changes in Oil Prices Affect the Macroeconomy

We estimate a New Keynesian general-equilibrium open economy model to examine how changes in oil prices affect the macroeconomy. Our model allows oil price changes to be transmitted through temporary demand and supply channels (affecting the output gap), as well as through persistent supply side effects (affecting trend growth). We estimate this model for Canada, the United Kingdom, and the United States over the period 1971-2008, and find that it matches the data very well in terms of first and second moments. We conclude that (i) energy prices affect the economy primarily through the supply side, whereas we do not find substantial demand-side effects; (ii) higher oil prices have temporary negative effects on both the output gap and on trend growth, which translates into a permanent reduction in the level of potential and actual output. Also, results for the United States indicate that oil supply shocks have more persistent negative effects on trend growth than oil demand shocks. These effects are statistically significant; however, our simulations also indicate that the effects are economically small.
Stream-based Active Learning for efficient and adaptive classification of 3D objects

We present a new Active Learning approach for classifying objects from streams of 3D point cloud data. The major problems here are the non-uniform occurrence of class instances and the unbalanced numbers of samples per class. We show that standard online learning methods based on decision trees perform comparably bad for such data streams, which are however particularly relevant for mobile robots that need to learn semantics persistently. To address this, we use Mondrian forests (MF), a recent online learning algorithm that is independent on the data order. We present an extension of that algorithm and show that MF are less overconfident than standard Random Forests. In experiments on the KITTI benchmark, we show that this leads to a substantially improved classification performance for data streams, rendering our approach very attractive for lifelong robot learning applications.
The Bystander Response in C3H 10T½ Cells: The Influence of Cell-to-Cell Contact

Abstract Mitchell, S. A. , Randers-Pehrson, G. , Brenner, D. J. and Hall, E. J. The Bystander Response in C3H 10T½ Cells: The Influence of Cell-to-Cell Contact. Radiat. Res. 161, 397–401 (2004). Although radiation-induced heritable damage in mammalian cells was thought to result from the direct interaction of radiation with DNA, it is now accepted that biological effects may occur in cells that were not themselves traversed by ionizing radiation but are close to those that were. However, little is known about the mechanism underlying such a bystander effect, although cell-to-cell communication is thought to be of importance. Previous work using the Columbia microbeam demonstrated a significant bystander effect for clonogenic survival and oncogenic transformation in C3H 10T½ cells. The present study was undertaken to assess the importance of the degree of cell-to-cell contact at the time of irradiation on the magnitude of this bystander effect by varying the cell density. When 10% of cells were exposed to a range of 2–12 α particles, a significantly greater number of cells (P < 0.0001) were inactivated when cells were irradiated at high density (>90% in contact with neighbors) than at low density (<10% in contact). In addition, the oncogenic transformation frequency was significantly higher in high-density cultures (P < 0.0004). These results suggest that when a cell is hit by radiation, the transmission of the bystander signal through cell-to-cell contact is an important mediator of the effect, implicating the involvement of intracellular communication through gap junctions.
Visualization of Item Features, Customer Preference and Associated Uncertainty using Fuzzy Sets

Some of the requirements during preferences discovery or preference modeling through machine learning and data mining are: (i) understanding of features of items, e.g. genres content of a movie; (ii) understanding of patterns in customer feedback on items to explore and identify customer preferences; (iii) understanding of discovered patterns in customer preference on features of items, e.g. preference to genres of movies; and (iv) understanding of similarity among customers' preference, e.g. to form similar cluster of customers in their genre preference. An attempt is made to satisfy these requirements using fuzzy set driven information visualization technique; and movie as the item and genre as the feature are used for illustration. Visualization of features of items, patterns of customer previous feedback to these items, and the relationship between the feedback and item features along with associated measure of uncertainty are presented. The uncertainty is non-stochastic type that is induced from subjectivity, vagueness and imprecision in item features and user preference; and it is modeled using fuzzy set. The visualization of the discovered preference along various demographic features of the users is also presented. This in turn can help forming clusters of users with similar preference to various kinds of items.
Unsupervised Name Disambiguation via Social Network Similarity

Though names reference actual entities it is nontrivial to resolve which entity a particular name observation represents. Even when names are devoid of typographical error, the resolution process is confounded by both ambiguity, where the same name correctly references multiple entities, and by variation, when an entity is correctly referenced by multiple names. Thus, before link analysis for surveillance or intelligence-gathering purposes can proceed, it is necessary to ensure vertices and edges of the network are correct. In this paper, we concentrate on ambiguity and investigate unsupervised methods which simultaneously learn 1) the number of entities represented by a particular name and 2) which observations correspond to the same entity. The disambiguation methods leverage the fact that an entity’s name can be listed in multiple sources, each with a number of related entity’s names, which permits the construction of name-based relational networks. The methods studied in this paper differ based on the type of network similarity exploited for disambiguation. The first method relies upon exact name similarity and employs hierarchical clustering of sources, where each source is considered a local network. In contrast, the second method employs a less strict similarity requirement by using random walks between ambiguous observations on a global social network constructed from all sources, or a community similarity. While both methods provide better than simple baseline results on a subset of the Internet Movie Database, findings suggest methods which measure similarity based on community, rather than exact, similarity provide more robust disambiguation capability.
Vox Populi: Collecting High-Quality Labels from a Crowd

With the emergence of search engines and crowdsourcing websites, machine learning practitioners are faced with datasets that are labeled by a large heterogeneous set of teachers. These datasets test the limits of our existing learning theory, which largely assumes that data is sampled i.i. d. from a fixed distribution. In many cases, the number of teachers actually scales with the number of examples, with each teacher providing just a handful of labels, precluding any statistically reliable assessment of an individual teacher’s quality. In this paper, we study the problem of pruning low-quality teachers in a crowd, in order to improve the label quality of our training set. Despite the hurdles mentioned above, we show that this is in fact achievable with a simple and efficient algorithm, which does not require that each example be repeatedly labeled by multiple teachers. We provide a theoretical analysis of our algorithm and back our findings with empirical evidence.
Predictions for the Very Early Afterglow and the Optical Flash

According to the internal-external shocks model for gamma-ray bursts (GRBs), the GRB is produced by internal shocks within a relativistic flow while the afterglow is produced by external shocks with the interstellar medium. We explore the early afterglow emission. For short GRBs the peak of the afterglow will be delayed, typically by few dozens of seconds after the burst. For long GRBs the early afterglow emission will overlap the GRB signal. We calculate the expected spectrum and the light curves of the early afterglow in the optical, X-ray, and gamma-ray bands. These characteristics provide a way to discriminate between late internal shocks emission (part of the GRB) and the early afterglow signal. If such a delayed emission, with the characteristics of the early afterglow, is detected, it can be used to prove the internal shock scenario as producing the GRB, as well as to measure the initial Lorentz factor of the relativistic flow. The reverse shock, at its peak, contains energy which is comparable to that of the GRB itself but has a much lower temperature than that of the forward shock so it radiates at considerably lower frequencies. The reverse shock dominates the early optical emission, and an optical flash brighter than 15th magnitude is expected together with the forward shock peak at X-rays or gamma-rays. If this optical flash is not observed, strong limitations can be put on the baryonic contents of the relativistic shell deriving the GRBs, leading to a magnetically dominated energy density.
Estimated Rates of Retinal Ganglion Cell Loss in Glaucomatous Eyes with and without Optic Disc Hemorrhages

Purpose To evaluate whether optic disc hemorrhages are associated with faster rates of estimated retinal ganglion cell (RGC) loss in glaucoma. Methods A longitudinal observational cohort study of 222 eyes of 122 patients with glaucoma recruited from the Diagnostic Innovations Glaucoma Study (DIGS) followed for an average of 3.74±0.85 years. All subjects had optical coherence tomography and standard automated perimetry during follow up. Optic disc hemorrhages were detected by masked evaluation of stereophotographs. Rates of change in estimated numbers of RGCs were determined using a previously described method. A random coefficients model was used to investigate the relationship between disc hemorrhages and rates of change in estimated RGC counts over time. Results 19 eyes of 18 subjects had at least one disc hemorrhage during follow up. At baseline, average estimated RGC counts in eyes with and without disc hemorrhages were 677,994 cells and 682,021 cells, respectively (P = 0.929). Eyes with optic disc hemorrhages during follow-up had significantly faster rates of estimated RGC loss than eyes without disc hemorrhages (22,233 cells/year versus 10,704 cells/year, P = 0.020). The effect of disc hemorrhages on the rates of estimated RGC loss remained significant after adjusting for confounding variables. Conclusion Eyes with disc hemorrhages showed faster rates of RGC loss compared to eyes without disc hemorrhages. These results provide further evidence that disc hemorrhages should be considered as an indicator of increased risk for faster neural loss in glaucoma.