Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Large-Scale Integrated Neurocognitive Architecture Part 1: Conceptual Framework

The idea of creating a general purpose machine intelligence that captures many of the features of human cognition goes back at least to the earliest days of artificial intelligence and neural computation. In spite of more than a half-century of research on this issue, there is currently no existing approach to machine intelligence that comes close to providing a powerful, general-purpose human-level intelligence. However, substantial progress made during recent years in neural computation, high performance computing, neuroscience and cognitive science suggests that a renewed effort to produce a general purpose and adaptive machine intelligence is timely, likely to yield qualitatively more powerful approaches to machine intelligence than those currently existing, and certain to lead to substantial progress in cognitive science, AI and neural computation. In this report, we outline a conceptual framework for the long-term development of a large-scale machine intelligence that is based on the modular organization, dynamics and plasticity of the human brain. Some basic design principles are presented along with a review of some of the relevant existing knowledge about the neurobiological basis of cognition. Three intermediate-scale prototypes for parts of a larger system are successfully implemented, providing support for the effectiveness of several of the principles in our framework. We conclude that a human-competitive neuromorphic system for machine intelligence is a viable long-term goal, but that for the short term, substantial integration with more standard symbolic methods as well as substantial research will be needed to make this goal achievable.
R&D, Firm Fundamentals, and Diminishing Returns for the Stock Market

There has been considerable controversy whether the FASB2 requirement of immediate R&D expensing is more appropriate than the capitalization of R&D. The uncertainty of future payoffs is a key issue. This study's empirical evidence indicates that stock market investors base their assessment of the value of R&D money within the context of firm fundamental variables in predictable ways. This analysis shows a positive association of the stock market response to R&D with firm fundamental characteristics such as Tobin's Q, Return on Equity, and Return on Assets. Size and Earnings History also have a more complex but interesting association with the R&D response which can be explained with prior literature. Findings indicate that market response to R&D is subject to diminishing returns. The results imply that investors are able to assess R&D uncertainty.
The Intermediate-Band Dropout Method: A New Method to Search for High-Redshift Galaxies

We propose a new method to search for high-redshift galaxies that is based on an intermediate-band dropout technique rather than the usual broad-band dropout one. In this method, we use an intermediate-band filter whose central wavelength is longer than 7000 \AA. This new method makes it possible to distinguish both very late-type stars such as L and T dwarfs and dusty galaxies at intermediate redshift from real high-$z$ Lyman break galaxies. The reason for this is that such interlopers do not show strong intermediate-band depression although they have very red broad-band colors that are indicative of Lyman break galaxies. Applying our new method to imaging data sets obtained with the Suprime-Cam on the Subaru Telescope, we find a new sample of Lyman break galaxies at $z \simeq 5$.
A non-parametric and scale-independent method for cluster analysis

The detection and analysis of structure and substructure in systems of galaxies is a well-known problem. Several methods of analysis exist with different ranges of applicability and giving different results. The aim of the present paper is to describe a general procedure of wide applicability that is based on a minimum number of general assumptions and gives an objective, testable, scale-independent and non-parametric estimate of the clustering pattern of a sample of observational data. The method follows the idea that the presence of a cluster in a data sample is indicated by a peak in the probability density underlying the data. There are two steps: the first is estimation of the probability density and the second is identification of the clusters
Illicit drug use and labour force participation: a simultaneous equations approach

Empirical research on the relationship between illicit drug use and labour market success has been found to have mixed results in the literature. Relevant sources of variability are the methods used to account for the potential endogeneity of drug use. Using clinical data of drug users, this paper utilizes a recursive simultaneous-equations approach as an alternative for estimating the effect of consumption on labour participation and control the endogeneity problem. Our results confirm that drug use is endogenously determined, and provides evidence to support that frequent use of dependency drugs greatly decreases the likelihood of be in work. The results derive here show the potentially labour market-related costs of drug use and abuse, mainly in terms of production loss.
Characterizing and Modeling Ag ents in Digital Games

A promising approach in digital games is the possibility of customizing the game according to different demands. Artificial Intelligence algorithms play an important role in this direction, allowing the implementation of different behaviors for game agents. To accomplish this, it is necessary to model these agents in such way their behavior can be easily tunned to address different game features. In this paper, we discuss a generic representation to model virtual agents in digital games. Agents are modeled using a linear combination of different variables, which are used to represent specific game features. We perform experiments with FPS and Strategy games (COUNTER STRIKE and CIVILIZATION IV, respectively) and results show the effectiveness of this approach in characterizing and modeling agents. We were able to infer agents models by observing matches and also to generate different behaviors varying agent’s models.
CoFFEE: An Expandable and Rich Platform for Computer- Mediated, Face-to-Face Argumentation in Classroom

CoFFEE (Collaborative Face to Face Educational Environment) is a suite of applications that has been designed to let teachers and students cooperate during classroom discussions. Teachers can author and design CoFFEE sessions, where each step includes instances of different cooperative tools chosen in a significantly large set (more than 10 tools are currently available). The session is, then, executed in classroom/lab where the collaborative discussion takes place. All CoFFEE tools are highly configurable and, thanks to the concept of session, the functionalities can be adapted for schools of different grade and even for university courses. From a technological point of view CoFFEE is an expandable framework: new collaborative functionalities can be added as autonomous components; each component can be developed independently from each other, sharing a common user interface. This result has been achieved by building CoFFEE as a rich framework since it offers a consistent set of basic services that can be used as building blocks for totally new collaborative components.
Radiometric compensation for a low-cost immersive projection system

Catopsys is a low-cost projection system aiming at making mixed reality (virtual, augmented or diminished reality) affordable. It combines a videoprojector, a camera and a convex mirror and works in a non-specific room. This system displays an immersive environment by projecting an image onto the different parts of the room. However, the presence of an uncalibrated projector, heterogeneous materials and light inter-reflections influence the colors of the environment displayed in the room. Radiometric compensation of the projection process enables the system to reduce this problem. In this paper, we present our low-cost immersive projection system and propose a radiometric model and a compensation method which handle the projector response, surface materials and inter-reflections between surfaces. Our method works in two stages. First, the radiometric response of the projection process is evaluated. Then, this radiometric response is used to compensate the projection process in the desired environments.
Extraction of the lines of curvature from raw point cloud

A new method for direct extraction of the lines of curvature from raw point clouds is presented in this paper. We first extract smooth and continue direction fields by applying surface fitting algorithm. And then a priority queue is established according to the curvatures of each point for extracting lines of curvature in a feature-preserving manner. The approach is performed without computing an explicit parameterization of the surface, and is thus applicable to manifolds of any genus. Experiments on various 3D point set are implemented to verify its feasibility and performance.
Automatic testing of sequential and concurrent substitutability

Languages with inheritance and polymorphism assume that a subclass instance can substitute a superclass instance without causing behavioral differences for clients of the superclass. However, programmers may accidentally create subclasses that are semantically incompatible with their superclasses. Such subclasses lead to bugs, because a programmer may assign a subclass instance to a superclass reference. This paper presents an automatic testing technique to reveal subclasses that cannot safely substitute their superclasses. The key idea is to generate generic tests that analyze the behavior of both the subclass and its superclass. If using the subclass leads to behavior that cannot occur with the superclass, the analysis reports a warning. We find a high percentage of widely used Java classes, including classes from JBoss, Eclipse, and Apache Commons Collections, to be unsafe substitutes for their superclasses: 30% of these classes lead to crashes, and even more have other behavioral differences.
Evaluating motion: spatial user behaviour in virtual environments

User-behaviour analysis has only recently been adapted to the context of the virtual world domain and remains limited in its application. Behaviour analysis is based on instrumentation data, automated, detailed, quantitative information about user behaviour within the virtual environment (VE) of digital games. A key advantage of the method in comparison with existing user-research methods, such as usability- and playability-testing is that it permits very large sample sizes. Furthermore, games are in the vast majority of cases based on spatial, VEs within which the players operate and through which they experience the games. Therefore, spatial behaviour analyses are useful to game research and design. In this paper, spatial analysis methods are introduced and arguments posed for their use in user-behaviour analysis. Case studies involving data from thousands of players are used to exemplify the application of instrumentation data to the analysis of spatial patterns of user behaviour.
Advection-dominated Accretion: Self-Similarity and Bipolar Outflows

We consider axisymmetric viscous accretion flows where a fraction f of the viscously dissipated energy is advected with the accreting gas as stored entropy and a fraction 1-f is radiated. When f is small (i.e. very little advection), our solutions resemble standard thin disks in many respects except that they have a hot tenuous corona above. In the opposite {\it advection-dominated} limit ($f\rightarrow1$), the solutions approach nearly spherical accretion. The gas is almost at virial temperature, rotates at much below the Keplerian rate, and the flow is much more akin to Bondi accretion than to disk accretion. We compare our exact self-similar solutions with approximate solutions previously obtained using a height-integrated system of equations. We conclude that the height- integration approximation is excellent for a wide range of conditions. We find that the Bernoulli parameter is positive in all our solutions, especially close to the rotation axis. This effect is produced by viscous transport of energy from small to large radii and from the equator to the poles. In addition, all the solutions are convectively unstable and the convection is especially important near the rotation axis. For both reasons we suggest that a bipolar outflow will develop along the axis of the flows, fed by material from the the surface layers of the equatorial inflow.
Lysine 63-Polyubiquitination Guards against Translesion Synthesis–Induced Mutations

Eukaryotic cells possess several mechanisms to protect the integrity of their DNA against damage. These include cell-cycle checkpoints, DNA-repair pathways, and also a distinct DNA damage–tolerance system that allows recovery of replication forks blocked at sites of DNA damage. In both humans and yeast, lesion bypass and restart of DNA synthesis can occur through an error-prone pathway activated following mono-ubiquitination of proliferating cell nuclear antigen (PCNA), a protein found at sites of replication, and recruitment of specialized translesion synthesis polymerases. In yeast, there is evidence for a second, error-free, pathway that requires modification of PCNA with non-proteolytic lysine 63-linked polyubiquitin (K63-polyUb) chains. Here we demonstrate that formation of K63-polyUb chains protects human cells against translesion synthesis–induced mutations by promoting recovery of blocked replication forks through an alternative error-free mechanism. Furthermore, we show that polyubiquitination of PCNA occurs in UV-irradiated human cells. Our findings indicate that K63-polyubiquitination guards against environmental carcinogenesis and contributes to genomic stability.
Likelihood Prediction for Generalized Linear Mixed Models under Covariate Uncertainty

This article presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction are explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. This article outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson errors-in-variable generalized linear model, it has been shown in certain cases that LP produces better results than already known methods.
Low-mass star formation in R Coronae Australis: observations of organic molecules with the APEX telescope

This paper presents new APEX submillimetre molecular line observations of three low-mass protostars, IRS7A, IRS7B, and IRAS32, in the R Coronae Australis molecular cloud complex. The molecular excitation analysis is performed using a statistical equilibrium radiative transfer code. The derived beam averaged fractional abundances vary by less than a factor of two among the three sources, except those of H 2 CO and CH 3 OH, which show differences of about an order of magnitude. The molecular abundances are similar to those typically found in other star-forming regions in the Galaxy, such as the ρ Oph and Perseus molecular clouds. There is a marked difference in the kinetic temperatures derived for the protobinary source IRS7 from H 2 CO (≈40-60 K) and CH 3 OH (≈20 K), possibly indicating a difference in origin of the emission from these two molecules.
Effects of Cultivar and Process Variables on Dynamic-Mechanical and Sensorial Behavior of Value-Added Grape-Based Smoothies

The effects of either cooking temperature (45, 80, and 100 °C) or inclusion of seed particles on the dynamic-mechanical and sensorial properties of value-added Crimson seedless, Black Pearl, or Baresana grape-based smoothies were studied. The inclusion of seed particles resulted in significant increases of the phenolic content, both in Black Pearl and Baresana, but it did not affect in a negative way the sensorial characteristics of smoothies whereas it caused an increase of the viscoelastic behavior of Black Pearl and a slight decrease in Baresana grape-based smoothies. In particular, the investigated rheological parameters were the loss and storage modulus. Moreover, the loss tangent value (the ratio between loss and storage modulus) remained unchanged, indicating a pseudoplastic behavior of all samples, independent on the process conditions. The smoothies produced from Crimson grapes at the intermediate temperature (80 °C) showed sensorial and rheological characteristics similar to those manufactured at 45 °C and better than those manufactured at 100 °C.
Simply use the force Implementation of RF-based gesture interaction on an android phone

Various approaches exist to detect gestures and movements via smartphones. The abscence of reliable ad-hoc on-line gesture detection from environmental sources inspired this project for on-line hand gesture detection on a smartphone using only WiFi RSSI. We highlight our line of work and explain problems at hand to provide information for possible future work. We will furthermore introduce pcan showcase, a smartphone application, that is able to detect movement in front of the smartphone by reading the WiFi RSSI and use this information to control a Slideshow.
2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops a Modular Frameworl< for Ambient Health Monitoring

Ambient Health Monitoring is becoming increasingly important for supporting proactive self-monitoring as part of a healthy lifestyle and as an enabler of appropriate healthcare services in Ambient Assisted Living (AAL). However, the heterogeneity of available context sources and AAL infrastructure components hinders the design of holistic systems. In this paper, we describe a comprehensive modular system approach for integrating heterogeneous context sources, including: stationary sensor networks in AAL infrastructure; wireless medical device sensors; embedded mobile device sensors; as well as virtual sensors. We outline the potential use cases of the system by presenting a prototype lifelogging application for Android, which dynamically integrates several sensor types into a personal health record, with a special focus on activity recognition. The application also demonstrates the usage of gamification methods as a persuasive means of enhancing the intrinsic motivation of users towards a personalized healthy lifestyle. 1. Introduction Worldwide, human life expectancy has been growing for many years. This fortunate development is made possible, in large part, by significant improvements in advanced health monitoring technologies and related treatments. Whereas in the past, assessment of vital parameters was restricted to a doctor's office or a clinical environment, recent advances in sensor technologies, mobile devices and wireless networks have enabled the proliferation of continuous Ambient Health Monitoring (ARM) at home and on the move. Longer lifetimes creates challenging demands for the organization of daily life of elderly people with degrading mental and physical abilities, and will impose significant financial and organizational burdens on the healthcare systems of societies confronted with this demographic change. Ambient Assisted Living (AAL) research approaches these challenges by providing technical healthcare solutions, which are often based on monitoring health-related data. ARM can be used proactively as a means of encouraging healthy life strategies in younger people (e.g., Quantified Self Movement [1]) and for fulfilling the increasing demands by the elderly population for support mechanisms that enable an autonomous, active and fulfilling lifestyle. AAL projects are often focused on developing high-tech infrastructures that replace missing or fading human abilities (e.g., eating) with (semi-) automated technical services (e.g., feeding robots). Moreover, many AAL projects focus on specific aspects of service provisioning and develop dedicated solutions for those. Examples include sophisticated solutions for dedicated sensing tasks, like in the DiaTrace project [2], which combines activity detection, meal photographs, blood sugar monitoring, etc. into a dedicated nutrition support system. Unfortunately, most of these approaches are not …
Introduced annual grass increases regional fire activity across the arid western USA (1980–2009)

Non‐native, invasive grasses have been linked to altered grass‐fire cycles worldwide. Although a few studies have quantified resulting changes in fire activity at local scales, and many have speculated about larger scales, regional alterations to fire regimes remain poorly documented. We assessed the influence of large‐scale Bromus tectorum (hereafter cheatgrass) invasion on fire size, duration, spread rate, and interannual variability in comparison to other prominent land cover classes across the Great Basin, USA. We compared regional land cover maps to burned area measured using the Moderate Resolution Imaging Spectroradiometer (MODIS) for 2000–2009 and to fire extents recorded by the USGS registry of fires from 1980 to 2009. Cheatgrass dominates at least 6% of the central Great Basin (650 000 km2). MODIS records show that 13% of these cheatgrass‐dominated lands burned, resulting in a fire return interval of 78 years for any given location within cheatgrass. This proportion was more than double the amount burned across all other vegetation types (range: 0.5–6% burned). During the 1990s, this difference was even more extreme, with cheatgrass burning nearly four times more frequently than any native vegetation type (16% of cheatgrass burned compared to 1–5% of native vegetation). Cheatgrass was also disproportionately represented in the largest fires, comprising 24% of the land area of the 50 largest fires recorded by MODIS during the 2000s. Furthermore, multi‐date fires that burned across multiple vegetation types were significantly more likely to have started in cheatgrass. Finally, cheatgrass fires showed a strong interannual response to wet years, a trend only weakly observed in native vegetation types. These results demonstrate that cheatgrass invasion has substantially altered the regional fire regime. Although this result has been suspected by managers for decades, this study is the first to document recent cheatgrass‐driven fire regimes at a regional scale.
Reliability-driven task mapping for lifetime extension of networks-on-chip based multiprocessor systems

Shrinking transistor geometries, aggressive voltage scaling and higher operating frequencies have negatively impacted the lifetime reliability of embedded multi-core systems. In this paper, a convex optimization-based task-mapping technique is proposed to extend the lifetime of a multiprocessor systems-on-chip (MPSoCs). The proposed technique generates mappings for every application enabled on the platform with variable number of cores. Based on these results, a novel 3D-optimization technique is developed to distribute the cores of an MPSoC among multiple applications enabled simultaneously. Additionally, reliability of the underlying network-on-chip links is also addressed by incorporating aging of links in the objective function. Our formulations are developed for directed acyclic graphs (DAGs) and synchronous dataflow graphs (SDFGs), making our approach applicable for streaming as well as non-streaming applications. Experiments conducted with synthetic and real-life application graphs demonstrate that the proposed approach extends the lifetime of an MPSoC by more than 30% when applications are enabled individually as well as in tandem.