Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Exposure in Online Social Networks

In online social networks (OSNs), the privacy of users is impacted by exposure of information about those users to other users of the system. Various factors, including design and user behavior, may affect the degree to which information about users is exposed. We propose the notion of knowledge exposure that measures the probability that information about users will be seen by others. We argue that such a measure can give OSN users and designers insight about how privacy is affected based on system design and user behavior. We present exposure as a promising notion that can complement privacy control efforts in an OSN rather than replacing existing measures such as access control. We provide a formal model of exposure in an OSN, and demonstrate through experiments how it can be calculated for various information items.
Diagnosis of Autism Spectrum Disorders Using Multi-Level High-Order Functional Networks Derived From Resting-State Functional MRI

Functional brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used for Autism Spectrum Disorder (ASD) diagnosis. Typically, these networks are constructed by calculating functional connectivity (FC) between any pair of brain regions of interest (ROIs), i.e., using Pearson's correlation between rs-fMRI time series. However, this can only be called as a low-order representation of the functional interaction, because the relationship is investigated just between two ROIs. Brain disorders might not only affect low-order FC, but also high-order FC, i.e., the higher-level relationship among multiple brain regions, which might be more crucial for diagnosis. To comprehensively characterize such relationship for better diagnosis of ASD, we propose a multi-level, high-order FC network representation that can nicely capture complex interactions among brain regions. Then, we design a feature selection method to identify those discriminative multi-level, high-order FC features for ASD diagnosis. Finally, we design an ensemble classifier with multiple linear SVMs, each trained on a specific level of FC networks, for boosting the final classification accuracy. Experimental results show that the integration of both low-order and first-level high-order FC networks achieves the best ASD diagnostic accuracy (81%). We further investigated those selected discriminative low-order and high-order FC features and found that the high-order FC features can provide complementary information to the low-order FC features in the ASD diagnosis.
Spatial-Pooling Memristor Crossbar Converting Sensory Information to Sparse Distributed Representation of Cortical Neurons

The spatial pooler in brain's neocortex models how the cortical neurons learn the feedforward connections and forms the cortical representation from various sensory information. To realize the spatial pooler's operation in hardware, we propose a new spatial-pooling memristor crossbar that converts the inputs from sensory organ into the sparse distributed representation (SDR) of cortical neurons, in this paper. The spatial pooling is composed of overlapping, inhibition, and learning steps. The proposed memristor crossbar can perform exactly the same functions of overlapping, inhibition, and learning with the spatial-pooling software algorithm. By converting the sensory information to SDR using the spatial-pooling memristor crossbar, we can have the following advantages for the cortical information processing: first, preserving the semantic similarity through the spatial pooling; second, changing synaptic weights continuously; third, keeping the sparsity of SDR as low as 2% like brain's neocortex; and fourth, improving the noise robustness. In this paper, these four properties of the spatial pooling in brain's neocortex have been simulated and verified in the memristor crossbar circuit. For verifying the spatial-pooling operation, the memristor crossbar has been tested for MNIST image recognition in this paper. The recognition rate is as high as 95% for the memristor crossbar with 4096 columns.
Deep Representation Learning for Domain Adaptation of Semantic Image Segmentation

Deep Convolutional Neural Networks have pushed the state-of-the art for semantic segmentation provided that a large amount of images together with pixel-wise annotations is available. Data collection is expensive and a solution to alleviate it is to use transfer learning. This reduces the amount of annotated data required for the network training but it does not get rid of this heavy processing step. We propose a method of transfer learning without annotations on the target task for datasets with redundant content and distinct pixel distributions. Our method takes advantage of the approximate content alignment of the images between two datasets when the approximation error prevents the reuse of annotation from one dataset to another. Given the annotations for only one dataset, we train a first network in a supervised manner. This network autonomously learns to generate deep data representations relevant to the semantic segmentation. Then the images in the new dataset, we train a new network to generate a deep data representation that matches the one from the first network on the previous dataset. The training consists in a regression between feature maps and does not require any annotations on the new dataset. We show that this method reaches performances similar to a classic transfer learning on the PASCAL VOC dataset with synthetic transformations.
Synergistic CPU-GPU Frequency Capping for Energy-Efficient Mobile Games

Mobile platforms are increasingly using Heterogeneous Multiprocessor Systems-on-Chip (HMPSoCs) with differentiated processing cores and GPUs to achieve high performance for graphics-intensive applications such as mobile games. Traditionally, separate CPU and GPU governors are deployed in order to achieve energy efficiency through Dynamic Voltage Frequency Scaling (DVFS) but miss opportunities for further energy savings through coordinated system-level application of DVFS. We present a cooperative CPU-GPU DVFS strategy (called Co-Cap) that orchestrates energy-efficient CPU and GPU DVFS through synergistic CPU and GPU frequency capping to avoid frequency overprovisioning while maintaining desired performance. Unlike traditional approaches that target a narrow set of mobile games, our Co-Cap approach is applicable across a wide range of microbenchmarks and mobile games. Our methodology employs a systematic training phase using fine-grained refinement steps with evaluations of frequency capping tables followed by a deployment phase, allowing deployment across a wide range of microbenchmarks and mobile games with varying graphics workloads. Our experimental results across multiple sets of over 200 microbenchmarks and 40 mobile games show that Co-Cap improves energy per frame by on average 8.9% (up to 18.3%) and 7.8% (up to 27.6%) (16.6% and 15.7% in CPU-dominant applications) and achieves minimal frames-per-second (FPS) loss by 0.9% and 0.85% (1.3% and 1.5% in CPU-dominant applications) on average in training and deployment sets, respectively, compared to the default CPU and GPU governors, with negligible overhead in execution time and power consumption on the ODROID-XU3 platform.
Left-Lateralized Contributions of Saccades to Cortical Activity During a One-Back Word Recognition Task

Saccadic eye movements are an inherent component of natural reading, yet their contribution to information processing at subsequent fixation remains elusive. Here we use anatomically-constrained magnetoencephalography (MEG) to examine cortical activity following saccades as healthy human subjects engaged in a one-back word recognition task. This activity was compared with activity following external visual stimulation that mimicked saccades. A combination of procedures were employed to eliminate saccadic ocular artifacts from the MEG signal. Both saccades and saccade-like external visual stimulation produced early-latency responses beginning ~70 ms after onset in occipital cortex and spreading through the ventral and dorsal visual streams to temporal, parietal and frontal cortices. Robust differential activity following the onset of saccades versus similar external visual stimulation emerged during 150-350 ms in a left-lateralized cortical network. This network included (i) left lateral occipitotemporal and nearby inferotemporal cortex, (ii) left posterior Sylvian fissure and nearby multimodal cortex, and (iii) medial parietooccipital, posterior cingulate and retrosplenial cortices. Moreover, this left-lateralized network colocalized with word repetition priming effects. Together, results suggest that central saccadic mechanisms influence a left-lateralized language network in occipitotemporal and temporal cortex above and beyond saccadic influences at preceding stages of information processing during visual word recognition.
Sentiment analysis of Indian currency demonetization

Data has been playing a very important role in our day-to-day life, from every passing event, whether it is a small event or a huge event, data is always generated. Recently Demonetization took place in our country, and there had been creation of huge amounts of data. This huge amount data could be seen on social media sites like twitter, Facebook etc. This data is valuable in many senses and can be analyzed to get some deep insights about how people felt about this change in our country. This data is not only valuable for making analysis but is also valuable for predicting people's support towards present government. Furthermore, main objective will be to present different trends which are positively or negatively linked to demonetization which occurred on 8th November 2016 and whether the people of our country are taking demonetization as a positive step or not.
Intraspinal microstimulation produces over-ground walking in anesthetized cats

Objective. Spinal cord injury causes a drastic loss of motor, sensory and autonomic function. The goal of this project was to investigate the use of intraspinal microstimulation (ISMS) for producing long distances of walking over ground. ISMS is an electrical stimulation method developed for restoring motor function by activating spinal networks below the level of an injury. It produces movements of the legs by stimulating the ventral horn of the lumbar enlargement using fine penetrating electrodes (≤50 μm diameter). Approach. In each of five adult cats (4.2–5.5 kg), ISMS was applied through 16 electrodes implanted with tips targeting lamina IX in the ventral horn bilaterally. A desktop system implemented a physiologically-based control strategy that delivered different stimulation patterns through groups of electrodes to evoke walking movements with appropriate limb kinematics and forces corresponding to swing and stance. Each cat walked over an instrumented 2.9 m walkway and limb kinematics and forces were recorded. Main results. Both propulsive and supportive forces were required for over-ground walking. Cumulative walking distances ranging from 609 to 835 m (longest tested) were achieved in three animals. In these three cats, the mean peak supportive force was 3.5 ± 0.6 N corresponding to full-weight-support of the hind legs, while the angular range of the hip, knee, and ankle joints were 23.1 ± 2.0°, 29.1 ± 0.2°, and 60.3 ± 5.2°, respectively. To further demonstrate the viability of ISMS for future clinical use, a prototype implantable module was successfully implemented in a subset of trials and produced comparable walking performance. Significance. By activating inherent locomotor networks within the lumbosacral spinal cord, ISMS was capable of producing bilaterally coordinated and functional over-ground walking with current amplitudes <100 μA. These exciting results suggest that ISMS may be an effective intervention for restoring functional walking after spinal cord injury.
Echocardiography in intensive care

Ultrasound waves are formed when a voltage is applied across a substance with piezo-electric properties. Echocardiography transducers are commonly made from lead-zirconate-titanate-5 (PZT-5). The electric impulses are delivered to the transducer in various patterns to create pulses of ultrasound. Some of this acoustic energy is reflected back to the transducer, which is received by the PZT-5 crystal and is converted back into electrical energy. This signal is then processed and displayed on a monitor. The frequency used by the transducer affects both the resolution of the image obtained and the penetration of the ultrasound beam into the body. Transthoracic echocardiography (TTE) employs low-frequency transducers (2–4 MHz), which allows better penetration of the acoustic energy through the chest wall, but at the cost of reduced longitudinal resolution. Transoesophageal echocardiography (TOE) does not require such penetration and so uses higher frequency transducers (3.5– 7 MHz) to produce better resolution imaging.
Predicting immune reconstitution syndrome.

a Score was 0 if patient had 1 opportunistic infection; 1 if patient had 11 opportunistic infection, a baseline CD8 T cell count 1468.5 cells/mL, and a baseline hemoglobin level 110.2 g/dL; 2 if a patient had 11 opportunistic infection, a baseline CD8 T cell count 1468.5 cells/mL, and a baseline hemoglobin level 10.2 g/dL; 3 if patient had 11 opportunistic infection, a baseline CD8 T cell count of 468.5 cells/mL, and a baseline hemoglobin level 113.6 g/dL; and 4 if a patient had 11 opportunistic infection, a baseline CD8 T cell count of 1468.5 cells/mL, and a baseline hemoglobin level 13.6 g/dL. b Evidence of IRS is not available, because patient case note was not available or was incomplete. Predicting Immune Reconstitution Syndrome
Automatic choroid cells segmentation and counting in fluorescence microscopic image

In this paper, we proposed a method to automatically segment and count the rhesus choroid-retinal vascular endothelial cells (RF/6A) in fluorescence microscopic images which is based on shape classification, bottleneck detection and accelerated Dijkstra algorithm. The proposed method includes four main steps. First, a thresholding filter and morphological operations are applied to reduce the noise. Second, a shape classifier is used to decide whether a connected component is needed to be segmented. In this step, the AdaBoost classifier is applied with a set of shape features. Third, the bottleneck positions are found based on the contours of the connected components. Finally, the cells segmentation and counting are completed based on the accelerated Dijkstra algorithm with the gradient information between the bottleneck positions. The results show the feasibility and efficiency of the proposed method.
Competencies of leadership professionals: a national study of premier leadership degree programs

Competencies of Leadership Professionals: A National Study of Premier Leadership Degree Programs. (December 2005) Anthony Clyde Andenoro B.A., University of Toledo; M.S., Texas A&M University Chair of Advisory Committee: Dr. Kim E. Dooley This study examined formal leadership degree programs and their ability to prepare professionals for leadership positions. This study provided data outlining the necessary competencies for leadership positions and examined the current activities and formal curriculum included in three premier formal leadership degree programs. This study also provided recommendations for the enhancement of current curriculum and methods in formal leadership degree programs to better facilitate the learning and empowerment of student populations interested in pursuing leadership positions. This study utilized qualitative research in order to study the interrelationships holistically. The data collected in this study provided insight into what leadership competencies were valued and taught by highly ranked diverse undergraduate and graduate academic programs. These programs were selected based on their reputation of excellence, faculty, and overall program experience. The gatekeepers and graduates of these highly motivated academic programs identified the necessary competencies for success after graduation in the leadership work force. This examination allowed for holistic impressions to be made about curriculum revision and enhancement that added to the foundation and future of leadership education.
Plankton Assemblage Estimated with BGC‐Argo Floats in the Southern Ocean: Implications for Seasonal Successions and Particle Export

The Southern Ocean (SO) hosts plankton communities that impact the biogeochemical cycles of the global ocean. However, weather conditions in the SO restrict mainly in situ observations of plankton communities to spring and summer, preventing the description of biological successions at an annual scale. Here, we use shipboard observations collected in the Indian sector of the SO to develop a multivariate relationship between physical and bio-optical data, and, the composition and carbon content of the plankton community. Then we apply this multivariate relationship to five biogeochemical Argo (BGC-Argo) floats deployed within the same bio-geographical zone as the ship-board observations to describe spatial and seasonal changes in plankton assemblage. The floats reveal a high contribution of bacteria below the mixed layer, an overall low abundance of picoplankton and a seasonal succession from nano- to microplankton during the spring bloom. Both naturally iron-fertilized waters downstream of the Crozet and Kerguelen Plateaus show elevated phytoplankton biomass in spring and summer but they differ by a nano- or microplankton dominance at Crozet and Kerguelen, respectively. The estimated plankton group successions appear consistent with independent estimations of particle diameter based on the optical signals. Furthermore, the comparison of the plankton community composition in the surface layer with the presence of large mesopelagic particles diagnosed by spikes of optical signals provides insight into the nature and temporal changes of ecological vectors that drive particle export. This study emphasizes the power of BGC-Argo floats for investigating important biogeochemical processes at high temporal and spatial resolution.
Investing in Gulfport: Development of an Academic–Community Partnership to Address Health Disparities

Abstract:Background: Invest Health, a collaboration between the Robert Wood Johnson Foundation and The Reinvestment Fund, selected 50 midsized cities to participate in a health initiative that encourages cross-sector alliances to think creatively about mechanisms that address barriers to reducing health disparities among low-income populations. Gulfport, Mississippi, was 1 of 50 teams chosen to participate.Objective: To develop an academic–community partnership among the University of Southern Mississippi, Coastal Family Health Center (CFHC), Mercy Housing and Human Development (MHHD), the Mississippi State Department of Health Office of Health Disparity Elimination, and Gulfport residents to create the Healthy Gulfport Initiative, and, ultimately, the Gulf Coast Healthy Communities Collaborative (GCHCC). Methods: A Gulfport City team was developed per Invest Health guidelines and included five individuals who represented the public sector, community development, and an academic or health-related anchor institution in the community. Several data sources were used to develop city-wide priority health outcomes. A priority neighborhood experiencing health disparities related to the priority health outcomes was identified. A community-engaged needs assessment was conducted in the priority neighborhood. Residents were engaged in prioritizing the health, education, and activity needs of their community via a participatory nominal group process and survey data collection.Results: Residents in the priority neighborhood lack access to health care and healthy food options owing to transportation difficulties and proximity to resources.Conclusions: The GCHCC will be established to act as a "backbone organization," so that a common agenda can be created with an emphasis on potential for collective impact.
Sparse Regression by Projection and Sparse Discriminant Analysis

Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.
Deep Gaussian processes using expectation propagation and Monte Carlo methods

Deep Gaussian processes (DGPs) are the natural extension of Gaussian processes (GPs) to a multi-layer architecture. DGPs are powerful probabilistic models that have shown better results than standard GPs in terms of generalization performance and prediction uncertainty estimation. Nevertheless, exact inference in DGPs is intractable, making these models hard to train. For this task, current approaches in the literature use approximate inference techniques such as variational inference or approximate expectation propagation. In this work, we present a new method for inference in DGPs using an approximate inference technique based on Monte Carlo methods and the expectation propagation algorithm. Our experiments show that our method scales well to large datasets and that its performance is comparable or better than other state of the art methods. Furthermore, our training method leads to interesting properties in the predictive distribution of the DGP. More precisely, it is able to capture output noise that is dependent on the input and it can generate multimodal predictive distributions. These two properties, which are not shared by other state-of-the-art approximate inference methods for DGPs, are analyzed in detail in our experiments.