Miracle-dz/UniFAR-FTUs huggingface dataset:
IntentionGAN : Multi-Modal Imitation Learning from Unstructured Demonstrations

Traditionally, imitation learning has focused on using isolated demonstrations of a particular skill [3]. The demonstration is usually provided in the form of kinesthetic teaching, which requires the user to spend sufficient time to provide the right training data. This constrained setup for imitation learning is difficult to scale to real world scenarios, where robots have to be able to execute a combination of different skills. To learn these skills, the robots would require a large number of robot-tailored demonstrations, since at least one isolated demonstration has to be provided for every individual skill. In order to improve the scalability of imitation learning, we propose a framework that can learn to imitate skills from a set of unstructured and unlabeled demonstrations of various tasks. As a motivating example, consider a highly unstructured data source, e.g. a video of a person cooking a meal. A complex activity, such as cooking, involves a set of simpler skills such as grasping, reaching, cutting, pouring, etc. In order to learn from such data, three components are required: i) the ability to map the image stream to state-action pairs that can be executed by a robot, ii) the ability to segment the data into simple skills, and iii) the ability to imitate each of the segmented skills. In this work, we tackle the latter two components, leaving the first one for future work. In this paper, we present a novel imitation learning method that learns a multi-modal stochastic policy, which is able to imitate a number of automatically segmented tasks using a set of unstructured and unlabeled demonstrations. Our results indicate that the presented technique can separate the demonstrations into sensible individual skills and imitate these skills using a learned multi-modal policy.
The Forgotten Medium: The Impact of Reading Violent Literature on Aggressive Thoughts

The Forgotten Medium: The Impact of Reading Violent Literature on Aggressive Thoughts McKay Stevens Department of Psychology, BYU Master of Science Media violence in television, film, video games, and music has been linked to increased aggression. Only in recent years have researchers begun to investigate the impact that reading violent literature can have on individuals. The present study exposed individuals to either a violent or nonviolent story and measured aggressive cognitions. No significant effect was found for story type on aggressive cognitions but a gender effect for aggressive word completions was found. Discussion centers on possible differences between media types as well as future suggestions for investigations into violent literature.
An efficient probing mechanism for next generation mobile broadband systems

Opportunistic scheduling exploits multiuser diversity for improving the performance of wireless systems. However, it requires instantaneous channel state information (CSI) to be available at the transmitter side. Since acquiring CSI is resource consuming, it always comes at a cost. We consider the problem of efficient channel state estimation in the context of 4G mobile broadband systems. Our proposed mechanism selects the subcarriers to be probed per user, by estimating the anticipated tentative rate with and without probing. For the latter an informed guess of the channel state is performed. The distinguishing characteristic of this work is that the channel state evolution is assumed Markovian, thus capturing the impact of mobility, contrary to the much simpler i.i. d. case hitherto considered. Based on this probing mechanism we derive a subcarrier allocation algorithm that aims at maximizing the sum rate of the system. Our simulations indicate that the proposed algorithm leads to significant performance benefits.
Plasmid-mediated ROB-1 beta-lactamase in Pasteurella multocida from a human specimen

A Pasteurella multocida human isolate was resistant to beta-lactams because of production of ROB-1 beta-lactamase. The beta-lactamase was encoded by a 4.3-kb plasmid closely related to that of a Pasteurella bovine strain, as shown by Sau3A restriction profile and hybridization with a plasmid probe containing the blaROB-1 gene.
Discovery of In-Band Streaming Services in Peer-to-Peer Overlays

Peer-to-peer overlays can be used for service discovery over a global network fabric. We describe and evaluate a new service indexing mechanism for in-band streaming services such as application relays, mixers, and media transcoders. For this type of service, the location of the service in the network and service admission status are key attributes. We describe and analyze a service indexing mechanism which uses network position-based advertisement. We show that this mechanism gives good service selection, provides a close to uniform advertisement distribution in the overlay, reduces message overhead, and exhibits acceptable stability and setup delay.
Reciprocal Negative Regulation between EGFR and DEPTOR Plays an Important Role in the Progression of Lung Adenocarcinoma

The epidermal growth factor receptor (EGFR) activates downstream mTOR phosphorylation to promote the progression of many different tumor types, thus making it a prime therapeutic target. However, the role of DEP domain-containing mTOR-interacting protein (DEPTOR), a natural mTOR inhibitor, remains unclear in this process. Here, it is reported that EGFR expression is significantly increased in tumors of lung adenocarcinoma patients and is negatively correlated with the expression of DEPTOR. Activation of EGFR signaling, by EGF, in A549 lung adenocarcinoma cells (overexpressing EGFR) significantly enhanced the function of the mTOR autoamplification loop, consisting of S6K, mTOR, CK1α, and βTrCP1, which resulted in downregulation of DEPTOR expression. Gefitinib, a specific EGFR inhibitor, stimulated DEPTOR accumulation by downregulating the function of the mTOR autoamplification loop. Furthermore, a series of assays conducted in DEPTOR knockout or ectopic expression in A549 cells confirmed that DEPTOR inhibited proliferation, migration, and invasion as well as the in vivo tumor growth of lung adenocarcinoma. Importantly, tumor progression mediated by EGFR ectopic expression was diminished by transfection with DEPTOR. This study uncovers the important inhibitory role of DEPTOR in lung adenocarcinoma progression and reveals a novel mechanism that EGFR downregulates DEPTOR expression to facilitate tumor growth. Implications: DEPTOR acts as a tumor suppressor by limiting EGFR-driven lung adenocarcinoma progression. Mol Cancer Res; 14(5); 448–57. ©2016 AACR.
A Cloud-based Interactive Data Infrastructure for Sensor Networks

Small specialized sensor devices capable of both reporting on environmental factors and interacting with the environment are becoming increasingly ubiquitous, reliable and inexpensive. This transformation has enabled domain sciences to create "instruments at large" – dynamic and often self-organizing groups of sensors whose outputs are capable of being aggregated and correlated to support experiments organized around specific questions. This calls for an infrastructure that can support remote administration of sensors, relies on protocols that can withstand unreliable communications, and extend storage capability that can scale to support many data producing sensors, many different data types, and many end user requests. In this work we present protocols and a cloud-based data store called "WaggleDB" that address the above challenges. The system efficiently aggregates and stores data from sensor networks and enables users to query the data sets. The "WaggleDB" data store incorporates a scalable multi-tier architecture with individually scalable layers toward overcoming the challenges.
Modeling of tsunami waves and atmospheric swirling flows with graphics processing unit (GPU) and radial basis functions (RBF)

The faster growth curves in the speed of graphics processing units (GPUs) relative to CPUs have spawned a new area of development in computational technology. There is much potential in utilizing GPUs for solving evolutionary partial differential equations and producing the attendant visualization. We are concerned with modeling tsunami waves, where computational time is of extreme essence in broadcasting warnings. We employed an NVIDIA board on a MacPro to test the efficacy of the GPU on the set of shallow‐water equations, and compared the relative speeds between CPU and GPU for two types of spatial discretization based on second‐order finite differences and radial basis functions (RBFs). We found that the GPU produced a speedup by a factor of 8 in favor of the finite difference method and a factor of 7 for the RBF scheme. We also studied the atmospheric dynamics problem of swirling flows over a spherical surface and found a speedup of 5.3 by the GPU. The time steps employed for the RBF method are larger than those used in finite differences, because of the fewer number of nodal points needed by RBF. Thus, RBF acting in concert with GPU would hold great promise for tsunami modeling because of the spectacular reduction in the computational time. Copyright © 2009 John Wiley & Sons, Ltd.
Observability of path loss parameters in WLAN-based Simultaneous Localization and Mapping

Indoor positioning by means of received signal strengths has been gathering much interest since the massive presence of wireless local area networks (WLANs) in buildings. Theoretical approaches rely on the perfect knowledge of the APs' positions and propagation conditions; since this is unrealistic in real world, we estimate such knowledge as well as the building map from data by applying Simultaneous Localization and Mapping (SLAM). In this paper we address the joint estimation of the path loss parameters, namely the transmitted power and the path loss exponent, this latter being usually approximated in the literature by the free space value. We provide examples that show the relevance of estimating both parameters and analyze observability issues from the point of view of estimation theory. The integration of the parameter estimation in a WLAN based SLAM algorithm - WiSLAM - has been carried out and the results are discussed.
Optimized Compressed Sensing for Curvelet-based Seismic Data Reconstruction

Compressed sensing (CS) or compressive sampling provides a new sampling theory to reduce data acquisition, which says that compressible signals can be exactly reconstructed from highly incomplete sets of measurements. Very recently, the CS has been applied for seismic exploration and started to compact the traditional data acquisition. In this paper, we present an optimized sampling strategy for the CS data acquisition, which leads to better performance by the curvelet sparsity-promoting inversion in comparison with random sampling and jittered sampling scheme. One of motivation is to reduce the mutual coherence between measurement sampling schemes and curvelet sparse transform in the CS framework. The basic idea of our optimization is to directly redistribute the energy in frequency domain making original spectrum easily discriminated from the random noise induced by random undersampling, while offering control on the maximum gap size. Numerical experiments on synthetic and real seismic data show good performances of the proposed optimized CS for seismic data reconstruction.
Dynamic searching particle filtering scheme for indoor localization in wireless sensor network

In this paper, we propose a robust and efficient particle filtering framework for indoor localization in wireless sensor network (WSN) which searches effective anchors and constructs particle filter dynamically. Within this framework, three algorithms are integrated into the dynamic particle filter: anchor selection algorithm, location constraint resampling and SIR particle filter. The proposed scheme searches the maximum number of anchors with line of sight (LOS) to the target to guarantee the effective measurement. Then, we construct a dynamic particle filter with the chosen anchors and develop a novel resampling scheme which generates the particles within the indoor location constraints. The proposed scheme is proved to be robust and computational efficient. Simulation results show that our scheme is accurate with low computation cost, which is promising for real-time implementation.
Signaling methods for multimedia steganography

Conventional communication methods employ a wide variety of signaling techniques that essentially map a bit sequence to a real-valued sequence (which is a representation of a point in the signal constellation). The real-valued sequence is in turn transmitted over a communications channel. However, communication techniques for the purpose of multimedia steganography or data hiding have to transmit the real-valued sequence corresponding to a point in the signal constellation superimposed on the original content (without affecting the fidelity of the original content noticeably). In this paper, we explore practical solutions for signaling methods for multimedia steganography. Data hiding is seen as a sophisticated signaling technique using a periodic signal constellation. We propose such a signaling method and present both theoretical and simulated evaluations of its performance in an additive noise scenario. The problem of optimal choice of the parameters for the proposed technique is also explored, and solutions are presented.
Do SADQ and AUDIT identify independent impacts of alcohol abuse - clinical and biochemical markers respectively?

Background and Objectives: We have analyzed extant data to see if Alcohol Use Disorder Identification Test (AUDIT) and Severity of Alcohol Dependence Questionnaire (SADQ) assess overlapping aspects of alcoholism, and how they relate to lab measures of alcoholism. Materials and Methods: Consecutive male patients between 20 and 50 years were recruited from varied departments of a general hospital. AUDIT and SADQ, as well as liver function tests, were part of the data obtained. Results: Despite, a significant correlation between total scores of SADQ and AUDIT (ρ = 0.188, P < 0.021) and some of their sub-scores. SADQ scores alone were significantly correlating with clinical variables of alcoholism such as family history and age of onset; AUDIT did not. On the other hand, AUDIT total scores correlated with total and conjugated bilirubin, while SADQ did not. Interpretation and Conclusion: Our data suggests that the two scales, AUDIT and SADQ may be tapping into two different outcomes of increased alcohol use namely clinical and biochemical markers, respectively. SADQ could be useful in studies looking at withdrawal related severity and clinical aspects of alcoholism; while AUDIT could be more suitable for studies looking at alcoholism-related medical morbidity. This needs to be confirmed in larger unselected samples from different community and clinical settings.
LeMo: A learning analytics application focussing on user path analysis and interactive visualization

LeMo is the prototype of an application for learning analytics, which collects data about learners' activities from different learning platforms. The article describes design principles of LeMo and their implications for efficient learning analytics. Focus is on the LeMo system architecture, user path analysis by algorithms of sequential pattern mining, and visualization of learners' activities.
The Association between Magnesium Intake and Fasting Insulin Concentration in Healthy Middle-Aged Women

Objective: We assessed the association between magnesium intake and fasting insulin levels in a large cohort of women. Methods: Female nurses free of diabetes, cardiovascular diseases and cancer from the Nurses Health Study provided blood samples between 1989–1990. We selected a sub-sample of 219 women for this analysis. Magnesium intake was assessed by a food frequency questionnaire in 1990 and categorized into quartiles. Cross-sectional geometric means of fasting insulin concentrations by quartiles of magnesium intake were obtained with Generalized Linear Model and adjusted for several risk factors and lifestyle characteristics. Results: After adjustment for age, body mass index (BMI), total energy, physical activity, hours per week spent sitting outside work, alcohol intake, smoking, and family history of diabetes, magnesium intake was inversely associated with fasting insulin concentration. The multivariate adjusted geometric mean for women in the lowest quartile of magnesium intake was 11.0 μU/mL and 9.3 μU/mL among those in the highest quartile of magnesium intake (p for trend = 0.04). The inverse association remained when we considered magnesium from only food sources. Conclusion: Higher magnesium intake is associated with lower fasting insulin concentrations among women without diabetes. Because lower fasting insulin concentrations generally reflect greater insulin sensitivity, these findings provide a mechanism through which higher dietary magnesium intake may reduce the risk of developing type 2 diabetes mellitus.
A Convolution Kernel for Sentiment Analysis using Word-Embeddings

Accurate analysis of a sentence’s sentiment requires understanding of how words interact to convey emotion. Current works use the sentence’s parse tree and recursively compute sentiment of the constituent phrases. This approach is expensive and requires a human to annotate all subtrees of a sentence. We examine how using a lexical similarity kernel can leverage word-embeddings, generated in an unsupervised manner, and capture the interactions between all word units. In our evaluation, we find that this type of kernel performs with comparable accuracy to the state-of-the-art for polar sentiment analysis. We examine weighting features by node-depth in the sentence’s parse tree; however, this yielded unsatisfactory results. The sentence’s parse tree must be leveraged to attain better sentiment analysis and we are confident that this kernel provides a framework that can be extended in future to better make use of this information.
State Reconstruction in Spatially Distributed BioProcess Systems using Reduced Order Models: Application to the Gluconic Acid Production.

In this work, the dissipative nature of spatially distributed bioprocess systems is exploited to develop efficient state observers based on a low dimensional dynamic representation of the original set of partial differential equations. The approach we suggest combines standard observer design techniques for bioreactors with efficient model reduction methodologies based on projection of the original concentration fields on low dimensional subspaces capturing the slow dynamics of the process. Aspects related with the location of sensors and their influence on the ability to reconstruct concentration fields will also be considered. Finally, the different aspects of the methodology, as well as the efficiency of the resulting observers will be illustrated on a case study of industrial interest, namely a tubular bioreactor producing gluconic acid by Aspergillus Niger.
Toward a Ubiquitous Mobile Access Model: A Roaming Agreement-Less Approach

The development in mobile devices and wireless technologies opens up unlimited choices of mobile services such as mobile commerce. These advances make access services available and convenient everywhere at any time. Since mobile users usually move, accessing services becomes unavailable especially in some locations that are not covered by their home networks. Therefore, it becomes necessary to roam into foreign networks in order to access such services. However, authenticating visiting users by a foreign network results in some security concerns. This challenge lies in the fact that a foreign network provider does not initially have the authentication credentials of the mobile users. The existing approaches are either roaming agreement-based in exchanging authentication information between the home network and a foreign network or vulnerable to some security attacks. This paper proposes a roaming agreement-less approach based on our ubiquitous mobile access model. This approach consists of two tokens: Passport (identification token) and Visa (authorisation token) to provide a flexible authentication method for foreign network to authenticate mobile users. The security analysis indicates that our proposal is more secure and suitable for ubiquitous mobile communications specially in roaming agreement-less enviroment.
Information needs and coping styles of primary family caregivers of women following breast cancer surgery.

PURPOSE/OBJECTIVES To determine the information needs and unmet needs of primary family caregivers of women with breast cancer, their informational coping styles, and the relationships among needs, coping styles, and caregiver and patient variables in the first three weeks after surgery. DESIGN Descriptive, correlational survey. SETTING Three surgical inpatient units at one private and two public hospitals in Perth, Western Australia. SAMPLE 141 primary family caregivers of women having surgery for breast cancer. METHODS Modified Family Inventory of Needs-Husbands and the Miller Behavioral Style Scale administered within one week after surgery and repeated one to two weeks later, after the postoperative visit with the surgeon. MAIN RESEARCH VARIABLES Information needs, unmet needs, informational coping styles. FINDINGS All 30 needs were rated as important by the majority of participants. A reduction in the median percentage of unmet needs occurred between time 1 (22%) and time 2 (10%) (p = 0.00004). Caregivers with children younger than 20 had a greater number of needs than the remaining sample (p = 0.001). Caregivers who received information from the breast nurse counselor and medical staff had the lowest percentage of unmet needs compared with those reporting any other source of information (p = 0.007). Caregivers of private patients had more unmet needs compared with public patients' caregivers (p = 0.035). Most caregivers displayed a high monitoring coping style, but further analysis of composite monitoring and blunting profiles revealed that 11%-16% were low monitors as well as low blunters and another 22%-26% displayed an apparently conflicting style of both high monitoring and high blunting. CONCLUSIONS Western Australian caregivers have a similar range and priority of needs as those previously reported internationally. Caregivers in the private system, where breast centers are not established, are at risk for not having their needs met. Further studies are needed to determine how informational coping styles may affect family caregivers' need for and response to education given by nurses. IMPLICATIONS FOR NURSING Breast nurse counselors and other nursing staff play an important role in caregiver support. Caregivers with young children need additional support. Caregivers' coping styles indicate the need for high levels of information, which, paradoxically, may lead to increased distress. Therefore, nurses should consider assessing caregivers' informational coping styles to balance the amount of information given with appropriate strategies for assisting caregivers to cope with stressful information.
Assessing cognitive changes in the elderly: Reliable Change Indices for the Mini‐Mental State Examination

Stein J, Luppa M, Maier W, Wagner M, Wolfsgruber S, Scherer M, Köhler M, Eisele M, Weyerer S, Werle J, Bickel H, Mösch E, Wiese B, Prokein J, Pentzek M, Fuchs A, Leicht H, König H‐H, Riedel‐Heller SG for the AgeCoDe Study Group. Assessing cognitive changes in the elderly: Reliable Change Indices for the Mini‐Mental State Examination.