Miracle-dz/UniFAR-FTUs huggingface dataset:
The neural underpinnings of an optimal exploitation of social information under uncertainty

Social information influences decision-making through an integration of information derived from individual experience with that derived from observing the actions of others. This raises the question as to which extent one should utilize social information. One strategy is to make use of uncertainty estimates, leading to a copy-when-uncertain strategy that weights information from individual and social sources based on their respective reliabilities. Here, we investigate this integration process by extending models of Bayes optimal integration of sensory information to a social decision context. We then use a key parameter of our behavioral model in conjunction with functional magnetic resonance imaging to identify the neural substrate that is specifically linked to the fidelity of this integration process. We show that individuals behave near Bayes optimal when integrating two distinct sources of social information but systematically deviate from Bayes optimal choice when integrating individual with social information. This systematic behavioral deviation from optimality is linked to activity of left inferior frontal gyrus. Thus, an ability to optimally exploit social information depends on processes that overcome an egocentric bias, and this regulatory role involves the left inferior prefrontal cortex. The findings provide a mechanistic explanation for observations wherein individuals neglect the benefits from exploiting social information.
Hardware implementation of a soft cancellation decoder for polar codes

Polar Codes can provably achieve the capacity of discrete memoryless channels. In order to make practical, it is necessary to propose efficient hardware decoder architectures. In this paper, the first hardware decoder architecture implementing the Soft-output CANcellation (SCAN) decoding algorithm, is presented. This decoder was implemented on Field Programmable Gate Array (FPGA) devices. The proposed architecture is parametrizable for any number of iterations without adding hardware complexity. The SCAN decoder architecture is compared to another soft-output decoder that implements a Belief Propagation (BP) algorithm. The SCAN decoder can reach a higher throughput than a BP decoder, with a lower memory footprint. Moreover, only one iteration with the SCAN algorithm leads to better decoding performance than 50 iterations of the BP algorithm.
A Parallel Snapshot Retrieval Technique for MG

Retrieving historical information in a short time is a crucial objective in dynamic graphs. Some of the existing dynamic graph models use log-files to store the graph evolution history. These log-files store the history as a set of events sorted by their occurrence time. For answering a historical query on these models, the snapshot at the given historic time instant must be constructed first. Since the log-file events are sorted by time, the construction of a historical snapshot mandates that all events must be parsed and applied till the target time. Thus, the snapshot construction consumes a lot of time. However, these models provide accurate results. That is why, it is of a significant importance to provide accurate and fast results. In this paper, we present a log-file structure that is able to efficiently handle both accuracy and performance.
Score following: An artificially intelligent musical accompanist

Score Following is the process by which a musician can be tracked through their performance of a piece, for the purpose of accompanying the musician with the appropriate notes. This tracking is done by following the progress of the musician through the score (written music) of the piece, using observations of the notes they are playing. Artificially intelligent musical accompaniment is where a human musician is accompanied by a computer musician. The computer musician is able to produce musical accompaniment that relates musically to the human performance. Hidden Markov Models (HMMs) are a stochastic modelling tool that can be used to represent real-world systems in a variety of domains. This project discusses how HMMs can be used in the domain of Score Following and describes the construction and evaluation of a score following system that uses HMMs to implement score following. It explores the hypothesis that using an HMM to represent a musical score is an efficient and practical way to implement score following, and that in particular this method is suitable for providing real-time accompaniment to a human performer. The score followers developed during this project are tested and compared against other score following systems and against human musicians. The resulting performances support the project hypothesis to a large extent.
Incremental 3D Line Segments Extraction from Semi-dense SLAM

Despite much interest in Simultaneous Localization and Mapping (SLAM), there is a lack of efficient methods for representing and processing their large scale point clouds. In this paper, we propose to simplify the point clouds generated by the semi-dense SLAM using three-dimensional (3D) line segments. Specifically, we present a novel incremental approach for 3D line segments extraction. This approach reduces a 3D line segment fitting problem into two two-dimensional (2D) line segment fitting problems, which take advantage of both image edge segments and depth maps. We first detect edge segments, which are one-pixel-width pixel chains from keyframes. We then search 3D line segments of each keyframe along their detected edge pixel chains by minimizing the fitting error on both image plane and depth plane. By incrementally clustering the detected line segments, we show that the resulting 3D representation for the scene achieves a good balance between compactness and completeness. Our experimental results show that the 3D line segments generated by our method are highly accurate in terms of the location of their end points. Additionally, we also demonstrate that these line segments greatly improve the quality of 3D surface reconstruction compared to a feature point based baseline.
Action Recognition in Video Using Sparse Coding and Relative Features

This work presents an approach to category-based action recognition in video using sparse coding techniques. The proposed approach includes two main contributions: i) A new method to handle intra-class variations by decomposing each video into a reduced set of representative atomic action acts or key-sequences, and ii) A new video descriptor, ITRA: Inter-Temporal Relational Act Descriptor, that exploits the power of comparative reasoning to capture relative similarity relations among key-sequences. In terms of the method to obtain key-sequences, we introduce a loss function that, for each video, leads to the identification of a sparse set of representative key-frames capturing both, relevant particularities arising in the input video, as well as relevant generalities arising in the complete class collection. In terms of the method to obtain the ITRA descriptor, we introduce a novel scheme to quantify relative intra and inter-class similarities among local temporal patterns arising in the videos. The resulting ITRA descriptor demonstrates to be highly effective to discriminate among action categories. As a result, the proposed approach reaches remarkable action recognition performance on several popular benchmark datasets, outperforming alternative state-of the-art techniques by a large margin.
A New Approach to Contextual Suggestions Based on Word2Vec

Abstract : We report our participation in the contextual suggestion track of TREC 2014 for which we submitted two runs using a novel approach to complete the competition. The goal of the track is to generate suggestions that users might fond of given the history of users' preference where he or she used to live in when they travel to a new city. We tested our new approach in the dataset of ClueWeb12-CatB which has been pre-indexed by Luence. Our system represents all attractions and user contexts in the continuous vector space learnt by neural network language models, and then we learn the user-dependent pro le model to predict the user's ratings for the attraction's websites using Softmax. Finally, we rank all the venues by using the generated model according the users' personal preference.
Quantitative Proteomics and Dynamic Imaging of the Nucleolus Reveal Distinct Responses to UV and Ionizing Radiation*

The nucleolus is a nuclear organelle that coordinates rRNA transcription and ribosome subunit biogenesis. Recent proteomic analyses have shown that the nucleolus contains proteins involved in cell cycle control, DNA processing and DNA damage response and repair, in addition to the many proteins connected with ribosome subunit production. Here we study the dynamics of nucleolar protein responses in cells exposed to stress and DNA damage caused by ionizing and ultraviolet (UV) radiation in diploid human fibroblasts. We show using a combination of imaging and quantitative proteomics methods that nucleolar substructure and the nucleolar proteome undergo selective reorganization in response to UV damage. The proteomic responses to UV include alterations of functional protein complexes such as the SSU processome and exosome, and paraspeckle proteins, involving both decreases and increases in steady state protein ratios, respectively. Several nonhomologous end-joining proteins (NHEJ), such as Ku70/80, display similar fast responses to UV. In contrast, nucleolar proteomic responses to IR are both temporally and spatially distinct from those caused by UV, and more limited in terms of magnitude. With the exception of the NHEJ and paraspeckle proteins, where IR induces rapid and transient changes within 15 min of the damage, IR does not alter the ratios of most other functional nucleolar protein complexes. The rapid transient decrease of NHEJ proteins in the nucleolus indicates that it may reflect a response to DNA damage. Our results underline that the nucleolus is a specific stress response organelle that responds to different damage and stress agents in a unique, damage-specific manner.
Improved multi-frame super resolution via multiframe blind deblurring using the alternating direction method of multipliers

Multi-frame super resolution (MFSR) aims to estimate a high resolution image from a set of low resolution images. Such task is ill-posed and typically computationally costly. We propose a new MFSR forward model, and reformulate the MFSR problem into a problem of multi-frame blind deblurring (MFDB) which is easier to be addressed than the former. We further efficiently solve the MFBD problem via attacking the resulting sub-problems of alternating minimization using the alternating direction method of multipliers (ADMM). Experiments on synthetic and real images show the effectiveness of the proposed method in terms of speed and image quality.
Segmenting of object in postprocessing stage in underwater video sequences under dynamic condition

Information assimilation and dissemination is the major task and challenge in the new era of technology. There are many means of information storing. Underwater Videos and images reveal much information when they are analyzed. The proposed method uses Mixture of Gaussian as a basic model to segment the moving object under dynamic condition. The proposed method remove the motion of the underwater algae or plants which exists as background by checking the status of each foreground pixels in each frame and decides whether to be present as output or not in the post processing stage. Finally the output is compared with a validated ground truth.
Effective latent space graph-based re-ranking model with global consistency

Recently the re-ranking algorithms have been quite popular for web search and data mining. However, one of the issues is that those algorithms treat the content and link information individually. Inspired by graph-based machine learning algorithms, we propose a novel and general framework to model the re-ranking algorithm, by regularizing the smoothness of ranking scores over the graph, along with a regularizer on the initial ranking scores (which are obtained by the base ranker). The intuition behind the model is the global consistency over the graph: similar entities are likely to have the same ranking scores with respect to a query. Our approach simultaneously incorporates the content with other explicit or implicit link information in a latent space graph. Then an effective unified re-ranking algorithm is performed on the graph with respect to the query. To illustrate our methodology, we apply the framework to literature retrieval and expert finding applications on DBLP bibliography data. We compare the proposed method with the initial language model method and another PageRank-style re-ranking method. Also, we evaluate the proposed method with varying graphs and settings. Experimental results show that the improvement in our proposed method is consistent and promising.
Efficiently Using Second Order Information in Large l1 Regularization Problems

We propose a novel general algorithm LHAC that efficiently uses second-order information to train a class of large-scale l1-regularized problems. Our method executes cheap iterations while achieving fast local convergence rate by exploiting the special structure of a low-rank matrix, constructed via quasi-Newton approximation of the Hessian of the smooth loss function. A greedy active-set strategy, based on the largest violations in the dual constraints, is employed to maintain a working set that iteratively estimates the complement of the optimal active set. This allows for smaller size of subproblems and eventually identifies the optimal active set. Empirical comparisons confirm that LHAC is highly competitive with several recently proposed state-of-the-art specialized solvers for sparse logistic regression and sparse inverse covariance matrix selection.
th International Conference on Information Fusion ( FUSION ) Improved Fast Covariance Intersection for Distributed Data Fusion

Among the possible approaches to tackling the problem ofdata incest in distributed datafusion networks, covariance intersection is a candidate that yields consistent estimates independent of network structure and any possible cross-correlation between local estimates. The corresponding weighting coefficients are usually chosen with the aim for a minimum trace or determinant of the resulting error variance matrix. A fast non-iterative algorithm exists that, to a certain extent, approximately solves this nonlinear optimization problem with extremely reduced numerical implementation effort. Yet, the obtained weighting coefficients do not depend on the relative orientation ofthe estimation error variance matrices which may lead to a degraded performance in certain applications. Hence, an improved fast covariance intersection algorithm is developed that comes with a slightly increased implementation effort while yielding significantly better estimation results in some cases and comparable results in all other ones. Simulation results confirm the postulated performance improvement.
Gephyrin Regulates the Cell Surface Dynamics of Synaptic GABAA Receptors

The efficacy of fast synaptic inhibition is critically dependent on the accumulation of GABAA receptors at inhibitory synapses, a process that remains poorly understood. Here, we examined the dynamics of cell surface GABAA receptors using receptor subunits modified with N-terminal extracellular ecliptic pHluorin reporters. In hippocampal neurons, GABAA receptors incorporating pHluorin-tagged subunits were found to be clustered at synaptic sites and also expressed as diffuse extrasynaptic staining. By combining FRAP (fluorescence recovery after photobleaching) measurements with live imaging of FM4-64-labeled active presynaptic terminals, it was evident that clustered synaptic receptors exhibit significantly lower rates of mobility at the cell surface compared with their extrasynaptic counterparts. To examine the basis of this confinement, we used RNAi to inhibit the expression of gephyrin, a protein shown to regulate the accumulation of GABAA receptors at synaptic sites. However, whether gephyrin acts to control the actual formation of receptor clusters, their stability, or is simply a global regulator of receptor cell surface number remains unknown. Inhibiting gephyrin expression did not modify the total number of GABAA receptors expressed on the neuronal cell surface but significantly decreased the number of receptor clusters. Live imaging revealed that clusters that formed in the absence of gephyrin were significantly more mobile compared with those in control neurons. Together, our results demonstrate that synaptic GABAA receptors have lower levels of lateral mobility compared with their extrasynaptic counterparts, and suggest a specific role for gephyrin in reducing the diffusion of GABAA receptors, facilitating their accumulation at inhibitory synapses.
Performance evaluation of profiler mechanisms for the Internet Assured Service

As Internet is rapidly growing and receiving traffic from multimedia applications that are sensitive to available bandwidth and delay experienced in the network, there is a strong need for quality of service (QoS) support. The Integrated and Differentiated Service models are two approaches for adding QoS to Internet. The Assured Service is an end-to-end service based on the Differentiated Service architecture. In this paper, we study and compare the performance of three profiler mechanisms to provide the guaranties of an Internet Assured Service. Two of them, TSW and Leaky Bucket are the most commonly used, and the third is a new Counter Based profiler, which is proposed in this paper. The study is done by simulation employing TCP RENO sources.
The longitudinal relationship between drug use and risky sexual behaviors among colombian adolescents.

OBJECTIVE To identify the longitudinal relationships between drug use and risky sexual behaviors and early pregnancy in Colombian adolescents. DESIGN Confidential survey of adolescents, consisting of structured individual interviews, at 2 time points, 2 years apart. A standard self-report questionnaire was adapted to ensure linguistic and cultural relevance. SETTING Community samples representing differing levels of risky sexual behavior and drug use. Cohorts were drawn from higher- and lower-risk geographic areas and from various self-reported ethnic groups. PARTICIPANTS Adolescents (N = 2226) randomly selected from 3 major Colombian cities: Bogotá, Medellín, and Barranquilla. MAIN OUTCOME MEASURES Data were collected concerning adolescent drug use, sexual behaviors, and a history of pregnancy. The youths' drug use included measures of all illegal drugs. RESULTS By using regression analyses (controlling for demographic variables) a reciprocal longitudinal relationship between risky sexual behaviors and drug use was identified. Those adolescents who reported higher levels of drug use at time 1 also had more sexual partners, had higher frequencies of unprotected sex, and were more likely to have experienced early pregnancy at time 2. The reverse relationship was true as well. The level of violence experienced by the adolescent emerged as a moderator of some of these relationships. CONCLUSIONS Reducing adolescent drug use may also reduce levels of risky sexual behavior and early pregnancy and vice versa. Furthermore, the importance of addressing violence as a risk factor for both problem behaviors is emphasized.
Development of a 2-Plex Luminex-Based Competitive Immunoassay to Quantify Neutralizing Antibodies Induced by Virus-Like Particles for Human Papillomavirus 16 and 18

Human papillomavirus (HPV) L1 virus-like particles (VLPs) were proven an effective vaccine candidate to prevent against HPV-16 and -18 infections. In order to evaluate the potency of our produced HPV-16 and -18 L1 VLPs-based vaccine candidates, also to quantify neutralizing antibodies induced by them, a 2-plex Luminex-based competitive immunoassay was developed. Unlike the published paper, the no-biotin conjugated neutralizing mAbs spiked normal human serum (NHS) was used for standard curve preparation, while phycoerythrin (PE) was not labeled directly to neutralizing mAbs for signaling. After the coupling optimization of VLPs to microspheres and the neutralizing mAbs biotinylation, the 2-plex standard curve was prepared with good fit and high dynamic range. In addition, no cross-reactivity was also confirmed. The 2-plex Luminex-based immunoassay represents good potential not only for vaccine candidate's evaluation but also for its further clinical use.
Uncontrolled hypertension due to volume overload contributes to higher left ventricular mass index in CAPD patients.

BACKGROUND Hypertension (HT) is common in patients on continuous ambulatory peritoneal dialysis (CAPD) and is responsible for increased cardiovascular morbidity and mortality. In this study, we aimed to determine the prevalence of 'uncontrolled HT' during background therapy in CAPD patients by using office measurements and ambulatory blood pressure monitoring (ABPM). We further determined whether intravascular volume status, assessed by inferior vena cava diameter (IVCD) index, contributes to higher blood pressure (BP) and increased left ventricular mass index (LVMI). METHODS Seventy-four CAPD patients were included in the final analysis. All patients underwent echocardiographic examination and received ABPM. Patients undergoing CAPD were categorized into two groups: 'uncontrolled HT' (Group A) and 'normotensive and controlled HT' (Group B). Intravascular volume status was determined using the IVCD index and collapsibility index (CI) on the same day as ABPM. RESULTS The prevalence of HT was 84% when using office measurements and 82% when using daytime ABPM. Daytime BP was 147/92 mm Hg by office measurements and 145/91 mm Hg by ABPM (P>0.05). The prevalence of 'uncontrolled HT' measured by ABPM was 73% (n=54). Patients with uncontrolled HT (Group A) were taking more antihypertensive medications than patients with 'normotension and controlled HT' (Group B, n=20; 1.0+/-0.8 vs 0.5+/-0.7, P=0.008). The IVCD index was higher in Group A than in Group B (9.2+/-2.1 vs 7.7+/-1.9 mm/m(2), P=0.007). There was no correlation between IVCD index and office BP, ABPM measurements or LVMI. The LVMI was also higher in Group A than in Group B (145+/-39 vs 118+/-34 g/m(2), P<0.01). Stepwise multiple regression analysis revealed that 24 h diastolic BP and haemoglobin were independent determinants of LVMI. CONCLUSION Uncontrolled HT on background therapy is highly prevalent among volume overloaded CAPD patients. Further long-term prospective studies examining effects of salt restriction and ultrafiltration on BP control and left ventricle wall thickness are warranted.