Miracle-dz/UniFAR-FTUs huggingface dataset:
A recommendation algorithm using positive and negative latent models

This paper proposes an algorithm for recommender systems that uses both positive and negative latent user models. In recommending items to a user, recommender systems usually exploit item content information as well as the preferences of similar users. Various types of content information can be attached to items and these are useful for judging user preferences. For example, in movie recommendations, a movie record may include the director, the actors, and reviews. These types of information help systems calculate sophisticated user preferences. We first propose a probabilistic model that maps multi-attributed records into a low-dimensional feature space. The proposed model extends latent Dirichlet allocation to the handling of multi-attributed data. We derive an algorithm for estimating the model's parameters using the Gibbs sampling technique. Next, we propose a probabilistic model to calculate user preferences for items in the feature space. Finally, we develop a recommendation algorithm based on the probabilistic model that works efficiently for large quantities of items and user ratings. We use a publicly available movie corpus to evaluate the proposed algorithm empirically, in terms of both its recommendation accuracy and its processing efficiency.
A CONSTRUCTIVE APPROACH TO SPARSE LINEAR REGRESSION IN HIGH-DIMENSIONS

We propose a constructive approach to estimating sparse, highdimensional linear regression models. The approach is a computational algorithm motivated from the KKT conditions for the `0penalized least squares solutions. It generates a sequence of solutions iteratively, based on support detection using primal and dual information and root finding. We refer to the algorithm as SDAR for brevity. Under a sparse Riesz condition on the design matrix and certain other conditions, we show that with high probability, the `2 estimation error of the solution sequence decays exponentially to the minimax error bound in O( √ J log(R)) iterations; and under a mutual coherence condition and certain other conditions, the `∞ estimation error decays to the optimal error bound in O(log(R)) iterations, where J is the number of important predictors and R is the relative magnitude of the nonzero target coefficients. Moreover the SDAR solution recovers the oracle least squares estimator within a finite number of iterations with high probability if the sparsity level is known. Computational complexity analysis shows that the cost of SDAR is O(np) per iteration. We also consider an adaptive version of SDAR for use in practical applications where the true sparsity level is unknown. Simulation studies demonstrate that SDAR is competitive with or outperforms Lasso, MCP and two greedy methods in accuracy and efficiency.
A Silicon-on-Sapphire Low-Voltage Temperature Sensor for Energy Scavengers

The paper report on the design and test of a low-voltage temperature sensor designed for MEMS power-harvesting systems. The core of the sensor is a bandgap voltage reference circuit operating with a supply voltage in the range of 1-1.5V. The prototype was fabricated on a conventional 0.5mum silicon-on-sapphire (SOS) process. The sensor design consumes 15muA of current at 1V. The internal reference voltage is 550mV. The temperature sensor has a digital square wave output whose frequency is proportional to temperature. A linear model of the dependency of output frequency with temperature has a conversion factor of 1.6kHz/degC. The output is also independent of supply voltage in the range of 1-1.5V. Measured results and targeted applications for the proposed circuit were reported.
Effect of Multipath Laser Shock Processing on Microhardness, Surface Roughness, and Wear Resistance of 2024-T3 Al Alloy

Laser shock processing (LSP) is an innovative surface treatment technique with high peak power, short pulse, and cold hardening for strengthening metal materials. LSP is based on the application of a high intensity pulsed laser beam (I > 1 GW/cm2; t < 50 ns) at the interface between the metallic target and the surrounding medium (a transparent confining material, normally water) forcing a sudden vaporization of the metallic surface into a high temperature and density plasma that immediately develops inducing a shock wave propagating into the material. The shock wave induces plastic deformation and a residual stress distribution in the target material. In this paper we study the increase of microhardness and surface roughness with the increase of laser pulse energy in 2024-T3 Al alloy. The influence of the thickness of the confining layer (water) on microhardness and surface roughness is also studied. In addition, the effect of LSP treatment with best conditions on wear behaviors of the alloy was investigated.
Refining Stylized Facts from Factor Models of Inflation

Factor models of disaggregate inflation indices suggest that sectoral shocks generate the bulk of sectoral inflation variance, but no persistence. Aggregate shocks, by contrast, are the root of sectoral inflation persistence, but have negligible relative variance. We show that simple factor models do not cope well with essential features of price data. In particular, sectoral inflation series are subject to features such as measurement error, sales and item substitutions. In factor models, these blow up the variance of sector-specific shocks, while reducing their persistence. We control for such effects by estimating a refined factor model and find that inflation variance is driven by both aggregate and sectoral shocks. Sectoral shocks, too, generate substantial inflation persistence. Both findings contrast with earlier evidence from factor models, but align well with recent micro evidence. Our results have implications for the foundations of price stickiness, and provide quantitative inputs for calibrating models with sectoral heterogeneity.
Political Regime, Private Investment, and Foreign Direct Investment in Developing Countries

This paper uses annual aggregate data for 36 low or middle income countries covering the period 1995-2001 to investigate the effect of FDI on private investment. It also explores if the relationship between FDI and private investment is influenced by the nature of the political regime, using four governance measures (voice and accountability, regulatory quality, political stability, and control of corruption) to distinguish between ? market-friendly? (high or good governance values) and ? market-unfriendly? (low governance) regimes. The results, which hold for all of the governance measures, show that private investment is more important than FDI in terms of the contribution to total investment, and that FDI inflows and private investment are higher in countries with good governance. Interestingly, the findings demonstrate that FDI tends to displace domestic private investment, and this ? crowding out? effect is greater in countries with good governance.
From Brute Facts to Institutional Facts in Multiagent-Systems

Some multi-agent models point to the use of a institutional dimension to ensure a suitable behavior of the agents and to aid the agents in their activities in the system. In some scenarios, the interaction between agents and institution may be mediated by the environment. This approach can be related to the theory of Construction of Social Reality, proposed by John Searle. In this paper we analyse some multi-agent models that propose the creation of institutional facts as result of facts ocurring in the environment. As result, we point some relevant points of the models.
Multiple Pathways of Recombination Induced by Double-Strand Breaks in Saccharomyces cerevisiae

SUMMARY The budding yeast Saccharomyces cerevisiae has been the principal organism used in experiments to examine genetic recombination in eukaryotes. Studies over the past decade have shown that meiotic recombination and probably most mitotic recombination arise from the repair of double-strand breaks (DSBs). There are multiple pathways by which such DSBs can be repaired, including several homologous recombination pathways and still other nonhomologous mechanisms. Our understanding has also been greatly enriched by the characterization of many proteins involved in recombination and by insights that link aspects of DNA repair to chromosome replication. New molecular models of DSB-induced gene conversion are presented. This review encompasses these different aspects of DSB-induced recombination in Saccharomyces and attempts to relate genetic, molecular biological, and biochemical studies of the processes of DNA repair and recombination.
Brain-Computer Interfaces for stroke rehabilitation: Evaluation of feedback and classification strategies in healthy users

A Brain-Computer Interface (BCI) is a tool for reading and interpreting signals that are derived from the user's brain, for example using Electroencephalography (EEG) to record signals from the user's scalp. Based on these signals, applications and external devices can be controlled. In the last decades a variety of different BCIs for communication and control applications were developed. A quite new and promising idea is to utilize BCIs as a tool for stroke rehabilitation. The BCI detects the intention to move and provides online feedback to the user, who is therefore able to train the correct motor control of the affected parts of the body. The aim of this publication is to optimize current BCI-strategies for stroke rehabilitation. Therefore a new method of providing immersive feedback via a 3-D virtual reality environment is evaluated. The second crucial aspect is to gain higher classification accuracy of the BCI. In the past years, in terms of signal processing huge improvements have already been done. Besides this, the latest development in EEG-hardware allows using a higher number of EEG-electrodes compared to former years. Hence, we also tested the influence of an increased number of EEG-electrodes.
A Semantically Oriented Readability Checker for German

One major reason that readability checkers are still far away from judging the understandability of texts consists in the fact that no semantic information is used. Syntactic, lexical, or morphological information can only give limited access for estimating the cognitive difficulties for a human being to comprehend a text. In this paper however, we present a readability checker which uses semantic information in addition. This information is represented as semantic networks and is derived by a deep syntactico-semantic analysis. We investigate in which situations a semantic readability indicator can lead to superior results in comparison with ordinary surface indicators like sentence length. Finally, we compute the correlations and absolute errors for our semantic indicators related to user ratings collected in an online evaluation.
QoS alternate path for multicast

Current algorithms adopted by most multicast routing protocols only consider one or two QoS metrics, without regard to the capabilities of the path nor its current load. With the changes of the network topology and resources available, the receivers have no alternative if the default multicast tree can not meet application requirements. It is so important to reroute a constraint multicast tree from receivers in advance. CAPM algorithm that aims to resolve this problem can find feasible alternate path according to the receiver's needs, its validity and performance are illustrated in large and heavy load network.
Exploring back-of-device interaction

Back of device interaction is gaining popularity as an alternative input modality in mobile devices, however it is still unclear how the back of device is related to other interactions. My research explores the relationship between hand grip from the back of the device and other interactions. In order to investigate this relationship, I will use touch target application to study hand grip patterns, then analyse the correlation that exists between touch target and hand grip. Finally I will explore the possibilities offered when the relationship between the touch target and hand grip is established in a quantifiable way.
3D face recognition from complement component range face images

Face and facial attributes represent meaningful definition about a variety of information to discriminate an individual from others and for developing a computational model for automatic face recognition purpose. However, in this work, selection of relevant features from newly created face space is the pivotal contribution of the authors. Here, authors have demonstrated a new face space `Complement Component' that have been used to extract the four basic components along X, and Y axes in four directions. Later, authors have experimented the discriminative attributes from these face spaces for recognition purpose. Here, comparison of the proposed method has been reported by examining its success on two well accepted 3D face databases, namely: Frav3D and Texas3D. In case of 2D face images, it does not contain depth like information i.e. Z-values in X-Y plane through intensity values. Therefore, it has not been undertaken during this investigation.
A 1mW Multi-channel 12-bit SAR A/D Converter

Design and realization of an 8-channel 12bit SAR A/D converter (ADC) is described in this paper. Based on the successive approximation register (SAR) architecture, this A/D Converter has a maximum sample rate of 140KSPS and the entire ADC consumes only 1mW from a 1.8V supply and the shutdown current is only 1uA. The low-power ADC was fabricated by a 0.18 μm 2P4M CMOS process. The layout area of the ADC is 0.3 mm × 0.35 mm. At the high performance point (12-bit, 140KSPS), the INL and DNL of the ADC are 0.7LSB and 0.66LSB, respectively.
Image segmentation based on two-dimensional histogram and the Geese particle swarm optimization algorithm

Image segmentation is a key part in image processing fields. The image segmentation method based on maximum entropy thresholding and two-dimensional histogram has many advantages, but it requires a large amount of computing time. To solve this problem, the Geese-LDW-PSO algorithm was introduced in this paper. Here, the Geese-LDW-PSO which was inspired by the wild geese group was the particle swarm optimization attached with linear descend inertia weight. First, the Geese-LDW-PSO was used to seek the optimal threshold value of a picture adaptively in the two-dimensional gray space. Then, the picture was segmented with the optimal threshold value which had been gotten. The simulation results showed that the Geese-LDW-PSO algorithm performed better in the segmentation of a vehicle brand image.
Title: Watermaze Learning Enhances Excitability of CA1 Pyramidal Neurons

The dorsal hippocampus is crucial for learning the hidden-platform location in the hippocampus-dependent, spatial watermaze task. We have previously demonstrated that the postburst afterhyperpolarization (AHP) of hippocampal pyramidal neurons is reduced after acquisition of the hippocampus-dependent, temporal trace eyeblink conditioning task. We report here that the AHP and one or more of its associated currents (IAHP and/or sIAHP) are reduced in dorsal hippocampal CA1 pyramidal neurons from rats that learned the watermaze task as compared to neurons from control rats. This reduction was a learning induced phenomenon as the AHP of CA1 neurons from rats that failed to learn the hidden-platform location was similar to that of neurons from control rats. We propose that reduction of the AHP in pyramidal neurons in regions crucial for learning is a cellular mechanism of learning that is conserved across species and tasks.
The Influence of Age on pH Regulation in Hippocampal Slices Before, During, and after Anoxia

Changes in intracellular and extracellular pH may influence the vulnerability of brain tissue to anoxic or ischemic damage. In the present study, we investigated whether the increased vulnerability of aged brain tissue to anoxic damage is associated with age-related alterations in pH regulation. We obtained evidence for altered pH regulation by measuring concurrent changes in intracellular and extracellular pH before, during, and after anoxia in hippocampal slices from young adult (6–8 months old) and aged (24–27 months old) rats. We found indications of impaired pH regulation in aged hippocampal slices (a) before anoxia, as seen in a lower resting intracellular pH, (b) during anoxia, as seen in a slower decline in extracellular pH, and (c) during recovery after anoxia, as seen in a slower rate of recovery of intracellular pH. Age-related changes in pH regulation may contribute to the faster onset of anoxic depolarization in aged brain tissue during anoxia.