Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Stochastic Online Learning Policies for Opportunistic Spectrum Access

The fundamental problem of multiple secondary users contending for opportunistic spectrum access over multiple channels in cognitive radio networks has been formulated recently as a decentralized multi-armed bandit (D-MAB) problem. In a D-MAB problem there are M users and N arms (channels) that each offer i.i. d. stochastic rewards with unknown means so long as they are accessed without collision. The goal is to design distributed online learning policies that incur minimal regret. We consider two related problem formulations in this paper. First, we consider the setting where the users have a prioritized ranking, such that it is desired for the K-th-ranked user to learn to access the arm offering the K-th highest mean reward. For this problem, we present DLP, the first distributed policy that yields regret that is uniformly logarithmic over time without requiring any prior assumption about the mean rewards. Second, we consider the case when a fair access policy is required, i.e., it is desired for all users to experience the same mean reward. For this problem, we present DLF, a distributed policy that yields order-optimal regret scaling with respect to the number of users and arms, better than previously proposed policies in the literature. Both of our distributed policies make use of an innovative modification of the well-known UCB1 policy for the classic multi-armed bandit problem that allows a single user to learn how to play the arm that yields the K K-th largest mean reward.
Comparative Analysis of 3D Bladder Tumor Spheroids Obtained by Forced Floating and Hanging Drop Methods for Drug Screening

Introduction: Cell-based assays using three-dimensional (3D) cell cultures may reflect the antitumor activity of compounds more accurately, since these models reproduce the tumor microenvironment better. Methods: Here, we report a comparative analysis of cell behavior in the two most widely employed methods for 3D spheroid culture, forced floating (Ultra-low Attachment, ULA, plates), and hanging drop (HD) methods, using the RT4 human bladder cancer cell line as a model. The morphology parameters and growth/metabolism of the spheroids generated were first characterized, using four different cell-seeding concentrations (0.5, 1.25, 2.5, and 3.75 × 104 cells/mL), and then, subjected to drug resistance evaluation. Results: Both methods generated spheroids with a smooth surface and round shape in a spheroidization time of about 48 h, regardless of the cell-seeding concentration used. Reduced cell growth and metabolism was observed in 3D cultures compared to two-dimensional (2D) cultures. The optimal range of spheroid diameter (300–500 μm) was obtained using cultures initiated with 0.5 and 1.25 × 104 cells/mL for the ULA method and 2.5 and 3.75 × 104 cells/mL for the HD method. RT4 cells cultured under 3D conditions also exhibited a higher resistance to doxorubicin (IC50 of 1.00 and 0.83 μg/mL for the ULA and HD methods, respectively) compared to 2D cultures (IC50 ranging from 0.39 to 0.43). Conclusions: Comparing the results, we concluded that the forced floating method using ULA plates was considered more suitable and straightforward to generate RT4 spheroids for drug screening/cytotoxicity assays. The results presented here also contribute to the improvement in the standardization of the 3D cultures required for widespread application.
BANDWIDTH ALLOCATION IN WIRELESS MESH NETWORKS

Admission management and channel allocation for end-to-end flows in multi-radio multi-channel wireless mesh networks is provided by interference aware channel allocation approach (IACA). In existing solutions the consequence of inter-flow and intra-flow interference has been neglected, so inaccurate information measure estimation and reservation might occur. The inter-flow interference from existing flows and also the expected intra-flow interference of the incoming flow are taken into account for correct estimation of channel allocation and to perform admission management. Moreover, this approach exploits wireless link diversity in multi-radio multi-channel mesh networks between neighboring nodes within the network thus on improve flow admission quantitative relation and to make sure of load-balancing. Index Terms—Multi-radio multi-channel wireless mesh networks, QoS guarantees, Interference, Load-balancing, Interference-Aware channel allocation.
A new propagation model for 2.4 GHz wireless LAN

This paper presents a detailed study of large-scale path loss on wireless networks operating in accordance with the standard IEEE 802.11g at 2.4 GHz in indoor and outdoor environments. A new propagation model is proposed. A comparative analysis of the existing and proposed models and the effects of the temperature and relative humidity on signal attenuation is made.
Quercetin Directly Interacts with Vitamin D Receptor (VDR): Structural Implication of VDR Activation by Quercetin

The vitamin D receptor (VDR) is a member of the nuclear receptor (NR) superfamily. The VDR binds to active vitamin D3 metabolites, which stimulates downstream transduction signaling involved in various physiological activities such as calcium homeostasis, bone mineralization, and cell differentiation. Quercetin is a widely distributed flavonoid in nature that is known to enhance transactivation of VDR target genes. However, the detailed molecular mechanism underlying VDR activation by quercetin is not well understood. We first demonstrated the interaction between quercetin and the VDR at the molecular level by using fluorescence quenching and saturation transfer difference (STD) NMR experiments. The dissociation constant (Kd) of quercetin and the VDR was 21.15 ± 4.31 μM, and the mapping of quercetin subsites for VDR binding was performed using STD-NMR. The binding mode of quercetin was investigated by a docking study combined with molecular dynamics (MD) simulation. Quercetin might serve as a scaffold for the development of VDR modulators with selective biological activities.
Dendritic Molecules: Concepts, Syntheses, Perspectives

Masses, Sizes, and Shapes of Macromolecules from Multifunctional Monomers From Theory to Practice: Historical Perspectives Nomenclature Synthetic Methodologies: Divergent Procedures Synthetic Methodologies: Convergent Procedures Synthetic Methodologies: One-step (Hyperbranched) Procedures Chiral Dendritic Macromolecules Dendrimers Containing Metal Sites Dendritic Networks Utilitarian Aspects Appendices Index.
Efficient implementation of multiplexer and priority multiplexer using 1S1R ReRAM crossbar arrays

Memristive devices have been shown to have low leakage power, non-volatile storage capability and high storage density. In addition, by using stateful logic approaches, hybrid CMOS nano-crossbar arrays offer functionalities such as arithmetic operations, which make them ideal target for in-memory computing. Multiplexers are useful circuits that are used in a wide variety of applications such as encoding-decoding, signal routing, data communications and data bus control. In this paper, we report the first study on implementation of multiplexers using 1S1R crossbar arrays. An efficient mapping of the multiplexers is presented, with logarithmic delay, in terms of number of control signals — n. Physics-based circuit simulations are performed to validate our approach.
An Error-Components Framework for Joint Choice Models of Activity Timing and Duration

This paper develops a model of activity and trip scheduling that combines three elements that have previously been investigated in isolation: the duration of activities, the time-of-day preference for activity participation and the effect of schedule delays on the valuation of activities. The model is in essence a GEV discrete choice model, describing individuals’ choice between alternative activity patterns. The utility function is formulated in a flexible way, applying a bell-shaped component to represent time-of-day preferences for activities. The model was tested using a 2001 data set from the Netherlands. The estimation results suggest that time-of-day preferences and schedule delays associated with the work activity are the most important factors influencing the scheduling of the work tour. Error components included in the model suggest that there is considerable unobserved heterogeneity with respect to mode preferences and schedule delay.
Chinese Word Segmentation and Named Entity Recognition by Character Tagging

This paper describes our word segmentation system and named entity recognition (NER) system for participating in the third SIGHAN Bakeoff. Both of them are based on character tagging, but use different tag sets and different features. Evaluation results show that our word segmentation system achieved 93.3% and 94.7% F-score in UPUC and MSRA open tests, and our NER system got 70.84% and 81.32% F-score in LDC and MSRA open tests.
A hybrid relational approach for word sense disambiguation in machine translation

We propose a novel approach for word sense disambiguation which makes use of corpus-based evidence combined with background knowledge. Using an inductive logic programming technique, it generates expressive models which exploit several knowledge sources and also the relations between them. The approach is evaluated in two tasks: identification of the correct translation for verbs in English-Portuguese and disambiguation of verbs from the Senseval-3 competition. The accuracy obtained in the multilingual task outperforms the alternative learning techniques investigated. The models also yielded significant improvement to the translation quality when integrated into a machine translation system. In the monolingual task, the approach performs as well as the state-of-the- art systems for Senseval verbs.
A Comprehensive Evaluation of Software Rejuvenation Policies for Transaction Systems With Markovian Arrivals

Software rejuvenation is one of the proactive fault management techniques to prevent system performance degradation, which may lead to the system failure caused by software aging. In the design of software rejuvenation, it is important to determine the optimal timing of triggering the rejuvenation in terms of the system overhead. In this paper, we consider six software rejuvenation policies, which are categorized into time-based and workload-based policies, under the environment where the arrival stream of system follows a Markovian arrival process (MAP). After building the stochastic models with respective rejuvenation policies, we formulate the loss probability of transaction and the upper bound of mean response time as the system performance indices. In the numerical illustrations, we exhibit a comprehensive study to compare six software rejuvenation policies numerically and show that the proposed rejuvenation policies called wait-time policies are superior to the others under the MAP arrival stream.
Phosphorylation of IRF-3 on Ser 339 Generates a Hyperactive Form of IRF-3 through Regulation of Dimerization and CBP Association

ABSTRACT The IκB kinase-related kinases, TBK1 and IKKi, were recently shown to be responsible for the C-terminal phosphorylation of IRF-3. However, the identity of the phosphoacceptor site(s) targeted by these two kinases remains unclear. Using a biological assay based on the IRF-3-mediated production of antiviral cytokines, we demonstrate here that all Ser/Thr clusters of IRF-3 are required for its optimal transactivation capacity. In vitro kinase assays using full-length His-IRF-3 as a substrate combined with mass spectrometry analysis revealed that serine 402 and serine 396 are directly targeted by TBK1. Analysis of Ser/Thr-to-Ala mutants revealed that the S396A mutation, located in cluster II, abolished IRF-3 homodimerization, CBP association, and nuclear accumulation. However, production of antiviral cytokines was still present in IRF-3 S396A-expressing cells. Interestingly, mutation of serine 339, which is involved in IRF-3 stability, also abrogated CBP association and dimerization without affecting gene transactivation as long as serine 396 remained available for phosphorylation. Complementation of IRF-3-knockout mouse embryonic fibroblasts also revealed a compensatory mechanism of serine 339 and serine 396 in the ability of IRF-3 to induce expression of the interferon-stimulated genes ISG56 and ISG54. These data lead us to reconsider the current model of IRF-3 activation. We propose that conventional biochemical assays used to measure IRF-3 activation are not sensitive enough to detect the small fraction of IRF-3 needed to elicit a biological response. Importantly, our study establishes a molecular link between the role of serine 339 in IRF-3 homodimerization, CBP association, and its destabilization.
Evaluating the Impact of Dry January 2016

The North West Coast (NWC) Academic Health Science Network (AHSN) has identified harms caused by alcohol as a specific area of work in the region. In particular, the AHSN is keen to support innovative initiatives where they can have a direct effect on the reduction of alcohol-related attendances and admissions in Emergency Departments (EDs) and Urgent Care Centres (UCCs). The Public Health Institute at Liverpool John Moores University was invited to conduct an evaluation to help gauge the success of the campaign using mixed methodologies. The evaluation was composed of: a comparison and analysis of Dry January participation data (shared by HITCH on behalf of Alcohol Concern); Trauma and Injury Intelligence Group (TIIG) data and North West Ambulance Service (NWAS) data; and, semi-structured interviews with key stakeholders within companies who participated in the Dry January campaign.
There's No Place Like Home: Evaluating Family Medicine Residents' Training in Home Care

ABSTRACT This study examined residents' experiences in a physician home visit program and their attitudes toward working with older patients in a home setting. In-depth personal interviews were conducted with all second and third year residents in a family medicine residency program (n = 17). Most residents expressed positive attitudes about caring for patients in their homes. Residents said home visits allowed them to provide better care, and enhanced their assessment skills and understanding of community services. We identified a lack of coordination and consistency in several areas, including home visit assessments and the role of physician preceptors. The need for formal training to identify and treat elder abuse was also identified. Participation in a home visit program appears to be an effective learning experience for residents, and helps residents to understand the needs of older people living in the community.
Frequently Asked Questions Retrieval for Croatian Based on Semantic Textual Similarity

Frequently asked questions (FAQ) are an efficient way of communicating domainspecific information to the users. Unlike general purpose retrieval engines, FAQ retrieval engines have to address the lexical gap between the query and the usually short answer. In this paper we describe the design and evaluation of a FAQ retrieval engine for Croatian. We frame the task as a binary classification problem, and train a model to classify each FAQ as either relevant or not relevant for a given query. We use a variety of semantic textual similarity features, including term overlap and vector space features. We train and evaluate on a FAQ test collection built specifically for this purpose. Our best-performing model reaches 0.47 of mean reciprocal rank, i.e., on average ranks the relevant answer among the top two returned answers.
Pediatric epilepsy: Clustering by functional connectivity using phase synchronization

This study proposes a nonlinear data-driven method to delineate Electroencephalogram (EEG) recordings as either coming from controls or patients with epilepsy. This method uses the probability of recurrence and the correlation between electrodes to extract the phase synchronization and the functional connectivity maps of the brain from interictal EEG data recordings. This newly proposed algorithm utilizes probabilistic clustering by extracting graph theoretical features from the calculated functional connectivity matrices. Results reveal that brain connectivity networks of epileptic and control populations show statistically significant differences (t (340) = -37.4771, p<;0.01) between them. Performance results show an accuracy of 92.8% with a sensitivity of 85.7% and a specificity of 100%, when tested on 14 subjects. These preliminary results confirm that this method can be used to enhance and validate diagnosis of epileptic patients from controls using non-invasive scalp EEG signals.
Dosimetry and image quality of four dental cone beam computed tomography scanners compared with multislice computed tomography scanners.

OBJECTIVES The aim of this study was to evaluate the radiation dose and image quality of four dental cone beam CT (CBCT) scanners, and to compare them with those of two multislice CT (MSCT) scanners. METHODS Tissue doses were measured using a tissue-equivalent anthropomorphic RANDO Head Phantom((R)) with thermoluminescence dosemeters (TLD). An RSVP Head Phantom(TM) with a specially designed cylindrical insert was used for comparison of image quality and absorbed dose. Image quality was evaluated in the form of contrast-to-noise ratio (CNR) and modulation transfer function (MTF). RESULTS Using standard imaging parameters, the effective doses varied between 14 microSv and 269 microSv (International Commission on Radiation Protection (ICRP) 1990) and 27 microSv and 674 microSv (ICRP 2008) with the CBCT scanners, and between 350 microSv and 742 microSv (ICRP 1990) and 685 microSv and 1410 microSv (ICRP 2008) with the MSCT scanners. The CNR of the CBCT and MSCT scanners were 8.2-18.8 and 13.6-20.7, respectively. Low-dose MSCT protocols provided CNRs comparable with those from CBCT scanners. The 10% MTF of the CBCT scanners varied between 0.1 mm(-1) and 0.8 mm(-1), and was 0.5 mm(-1) for all the MSCT protocols examined. CONCLUSIONS CBCT scanners provide adequate image quality for dentomaxillofacial examinations while delivering considerably smaller effective doses to the patient. Large variations in patient dose and image quality emphasize the importance of optimizing imaging parameters in both CBCT and MSCT examinations.
Implicit beliefs about ideal body image predict body image dissatisfaction

We examined whether implicit measures of actual and ideal body image can be used to predict body dissatisfaction in young female adults. Participants completed two Implicit Relational Assessment Procedures (IRAPs) to examine their implicit beliefs concerning actual (e.g., I am thin) and desired ideal body image (e.g., I want to be thin). Body dissatisfaction was examined via self-report questionnaires and rating scales. As expected, differences in body dissatisfaction exerted a differential influence on the two IRAP scores. Specifically, the implicit belief that one is thin was lower in participants who exhibited a high degree of body dissatisfaction than in participants who exhibited a low degree of body dissatisfaction. In contrast, the implicit desire to be thin (i.e., thin ideal body image) was stronger in participants who exhibited a high level of body dissatisfaction than in participants who were less dissatisfied with their body. Adding further weight to the idea that both IRAP measures captured different underlying constructs, we also observed that they correlated differently with body mass index, explicit body dissatisfaction, and explicit measures of actual and ideal body image. More generally, these findings underscore the advantage of using implicit measures that incorporate relational information relative to implicit measures that allow for an assessment of associative relations only.
Discovering Relations to Augment a Web-scale Knowledge Base Constructed from the Web

We propose a method for automatically discovering new knowledge from a knowledge base consisting of entity-relation-entity triples, constructed automatically from the Web. This method infers new relations between entity pairs by exploiting the structure of massive graphs, which can be obtained by linking existing entity-relation-entity triples. After identifying an unlinked entity pair likely to have a strong association from the graphs and a connected component around it, the method learns a new relation name from a similar structure. More precisely, it retrieves other connected components containing a similar structure and finds a new relation between the entities. The process can be considered a simple form of analogical reasoning, followed by identifying an additional relation in the searched connected component in the knowledge base. We used two datasets to evaluate the performance of this method. The ReVerb dataset, a large-scale knowledge base constructed from an Open Information Extraction method, was used to demonstrate its scalability and accuracy in comparison with SHERLOCK and other baselines. Our evaluation with the YAGO dataset showed the proposed method is applicable to other types of knowledge bases that are not from the Open Information Extraction paradigm.
Monte Carlo Methods for Maximum Margin Supervised Topic Models

An effective strategy to exploit the supervising side information for discovering predictive topic representations is to impose discriminative constraints induced by such information on the posterior distributions under a topic model. This strategy has been adopted by a number of supervised topic models, such as MedLDA, which employs max-margin posterior constraints. However, unlike the likelihood-based supervised topic models, of which posterior inference can be carried out using the Bayes' rule, the max-margin posterior constraints have made Monte Carlo methods infeasible or at least not directly applicable, thereby limited the choice of inference algorithms to be based on variational approximation with strict mean field assumptions. In this paper, we develop two efficient Monte Carlo methods under much weaker assumptions for max-margin supervised topic models based on an importance sampler and a collapsed Gibbs sampler, respectively, in a convex dual formulation. We report thorough experimental results that compare our approach favorably against existing alternatives in both accuracy and efficiency.
Introducing notched flexible needles with increased deflection curvature in soft tissue

Robotics-assisted needle steering can enhance the accuracy of needle-based medical interventions, especially when the designated target locations are obscured by obstacles. However, the steering techniques using standard needles are not capable of achieving high curvatures and cannot follow tightly curved paths inside tissue. In this work, we introduce a new notched steerable needle with improved curvature. The notched needle is developed by carving a series of small notches on a regular needle shaft. The notches decrease the needle's flexural rigidity and increase the maximum achievable curvature. First, we develop a model of the notched needle deflection inside soft tissue using the finite element method (FEM). The model relates the needle radius of curvature to the number of notches and their locations on the needle shaft. Next, the capability of the notched steerable needle in achieving high curvature and the model's accuracy in predicting needle curvature are validated by performing several needle insertion experiments on a tissue phantom. The results demonstrate that our newly developed needles can achieve a minimum radius of curvature of 198 mm, which is 67% better than standard needles, enabling future research in needle steering in tight spaces.