Miracle-dz/UniFAR-FTUs huggingface dataset:
Game of Thrones: Fully Distributed Learning for Multiplayer Bandits

We consider an N-player multiarmed bandit game in which each player chooses one out of M arms for T turns. Each player has different expected rewards for the arms, and the instantaneous rewards are independent and identically distributed or Markovian. When two or more players choose the same arm, they all receive zero reward. Performance is measured using the expected sum of regrets compared with optimal assignment of arms to players that maximizes the sum of expected rewards. We assume that each player only knows that player’s own actions and the reward that player received each turn. Players cannot observe the actions of other players, and no communication between players is possible. We present a distributed algorithm and prove that it achieves an expected sum of regrets of near-[Formula: see text]. This is the first algorithm to achieve a near order optimal regret in this fully distributed scenario. All other works have assumed that either all players have the same vector of expected rewards or that communication between players is possible.
Theoretical analysis on Noise2Noise using Stein's Unbiased Risk Estimator for Gaussian denoising: Towards unsupervised training with clipped noisy images

Recently, Noise2Noise has been proposed for unsupervised training of deep neural networks in image restoration problems including denoising Gaussian noise. However, it does not work well for truncated noise with non-zero mean. Here, we perform theoretical analysis on Noise2Noise for the limited case of Gaussian noise removal using Stein's Unbiased Risk Estimator (SURE). We extend SURE to deal with a pair of noise realizations to directly compare with Noise2Noise. Then, we show that Noise2Noise with Gaussian noise is a special case of our newly extended SURE with a pair of uncorrelated noise realizations. Lastly, we propose a compensation method for clipped Gaussian noise to approximately follow Normal distribution and show how this compensation method can be used for SURE based unsupervised denoiser training. We also show that our theoretical analysis provides insights on how to use Noise2Noise for clipped Gaussian noise.
Chemical Proteomics-Guided Identification of a Novel Biological Target of the Bioactive Neolignan Magnolol

Understanding the recognition process between bioactive natural products and their specific cellular receptors is of key importance in the drug discovery process. In this outline, some potential targets of Magnolol, a natural bioactive compound, have been identified by proteomic approaches. Among them, Importin-β1 has been considered as the most relevant one. A direct binding between Magnolol and this nuclear chaperone has been confirmed by DARTS and molecular docking, while its influence on Importin-β1 translocation has been evaluated by in vitro assays.
MEG/EEG Group Analysis With Brainstorm

Brainstorm is a free, open-source Matlab and Java application for multimodal electrophysiology data analytics and source imaging [primarily MEG, EEG and depth recordings, and integration with MRI and functional near infrared spectroscopy (fNIRS)]. We also provide a free, platform-independent executable version to users without a commercial Matlab license. Brainstorm has a rich and intuitive graphical user interface, which facilitates learning and augments productivity for a wider range of neuroscience users with little or no knowledge of scientific coding and scripting. Yet, it can also be used as a powerful scripting tool for reproducible and shareable batch processing of (large) data volumes. This article describes these Brainstorm interactive and scripted features via illustration through the complete analysis of group data from 16 participants in a MEG vision study.
REAP: Runtime Energy-Accuracy Optimization for Energy Harvesting IoT Devices

The use of wearable and mobile devices for health and activity monitoring is growing rapidly. These devices need to maximize their accuracy and active time under a tight energy budget imposed by battery and form-factor constraints. This paper considers energy harvesting devices that run on a limited energy budget to recognize user activities over a given period. We propose a technique to co-optimize the accuracy and active time by utilizing multiple design points with different energy-accuracy trade-offs. The proposed technique switches between these design points at runtime to maximize a generalized objective function under tight harvested energy budget constraints. We evaluate our approach experimentally using a custom hardware prototype and 14 user studies. It achieves 46% higher expected accuracy and 66% longer active time compared to the highest performance design point.
Verifying and Monitoring IoTs Network Behavior Using MUD Profiles

IoT devices are increasingly being implicated in cyber-attacks, raising community concern about the risks they pose to critical infrastructure, corporations, and citizens. In order to reduce this risk, the IETF is pushing IoT vendors to develop formal specifications of the intended purpose of their IoT devices, in the form of a Manufacturer Usage Description (MUD), so that their network behavior in any operating environment can be locked down and verified rigorously. This article aims to assist IoT manufacturers in developing and verifying MUD profiles, while also helping adopters of these devices to ensure they are compatible with their organizational policies and track device network behavior using their MUD profile. Our first contribution is to develop a tool that takes the traffic trace of an arbitrary IoT device as input and automatically generates the MUD profile for it. We contribute our tool as open source, apply it to 28 consumer IoT devices, and highlight insights and challenges encountered in the process. Our second contribution is to apply a formal semantic framework that not only validates a given MUD profile for consistency, but also checks its compatibility with a given organizational policy. We apply our framework to representative organizations and selected devices, to demonstrate how MUD can reduce the effort needed for IoT acceptance testing. Finally, we show how operators can dynamically identify IoT devices using known MUD profiles and monitor their behavioral changes in their network.
Bounds for the VC Dimension of 1NN Prototype Sets

In Statistical Learning, the Vapnik-Chervonenkis (VC) dimension is an important combinatorial property of classifiers. To our knowledge, no theoretical results yet exist for the VC dimension of edited nearest-neighbour (1NN) classifiers with reference set of fixed size. Related theoretical results are scattered in the literature and their implications have not been made explicit. We collect some relevant results and use them to provide explicit lower and upper bounds for the VC dimension of 1NN classifiers with a prototype set of fixed size. We discuss the implications of these bounds for the size of training set needed to learn such a classifier to a given accuracy. Further, we provide a new lower bound for the two-dimensional case, based on a new geometrical argument.
THE "FOREVER YOUNG" CANON. FIGURATIONS OF THE ADVERTISING OF FEMININITY

In contemporary advertising discourse, the importance of branding prevails over the product. This is one of several consequences increasingly exploiting the applicant of the intangibles in detriment of those who took as the foundation of USP. To paraphrase Jean Marie Floch, the promotional sheets relates increasingly with oblique paradigms advertising and especially the mythical advertising, whose references as Ph Michel or mainly Leo Bernett Michel and Jacques Seguela were inevitable (Floch, 1990: 201-204; Camilo, 2010: 123-130). Precisely with reference to this discursive context, it is our purpose to show how the actor advertising is calculated, especially that starring role of "young" and "woman". This work aims to carry out a classification of regimes of the representation of advertising of the "young woman" and is derived from a semiotic analysis of nature texts. It is based on a corpus of 230 by means of advertising published in women's magazines published in Portugal in the period of May and July 2015. We intend with this qualitative analysis to draw a semiotic profile of "female actor” and see how far this is excessive determined by an attribute: the "youth".
Evaluating active learning methods for annotating semantic predications

Abstract Objectives This study evaluated and compared a variety of active learning strategies, including a novel strategy we proposed, as applied to the task of filtering incorrect semantic predications in SemMedDB. Materials and methods We evaluated 8 active learning strategies covering 3 types—uncertainty, representative, and combined—on 2 datasets of 6,000 total semantic predications from SemMedDB covering the domains of substance interactions and clinical medicine, respectively. We also designed a novel combined strategy called dynamic β that does not use hand-tuned hyperparameters. Each strategy was assessed by the Area under the Learning Curve (ALC) and the number of training examples required to achieve a target Area Under the ROC curve. We also visualized and compared the query patterns of the query strategies. Results All types of active learning (AL) methods beat the baseline on both datasets. Combined strategies outperformed all other methods in terms of ALC, outperforming the baseline by over 0.05 ALC for both datasets and reducing 58% annotation efforts in the best case. While representative strategies performed well, their performance was matched or outperformed by the combined methods. Our proposed AL method dynamic β shows promising ability to achieve near-optimal performance across 2 datasets. Discussion Our visual analysis of query patterns indicates that strategies which efficiently obtain a representative subsample perform better on this task. Conclusion Active learning is shown to be effective at reducing annotation costs for filtering incorrect semantic predications from SemMedDB. Our proposed AL method demonstrated promising performance.
Heterogeneity Matters: Predicting Self-Esteem in Online Interventions Based on Ecological Momentary Assessment Data

Self-esteem is a crucial factor for an individual's well-being and mental health. Low self-esteem is associated with depression and anxiety. Data about self-esteem is oftentimes collected in Internet-based interventions through Ecological Momentary Assessments and is usually provided on an ordinal scale. We applied models for ordinal outcomes in order to predict the self-esteem of 130 patients based on diary data of an online depression treatment and thereby illustrated a path of how to analyze EMA data in Internet-based interventions. Specifically, we analyzed the relationship between mood, worries, sleep, enjoyed activities, social contact, and the self-esteem of patients. We explored several ordinal models with varying degrees of heterogeneity and estimated them using Bayesian statistics. Thereby, we demonstrated how accounting for patient-heterogeneity influences the prediction performance of self-esteem. Our results show that models that allow for more heterogeneity performed better regarding various performance measures. We also found that higher mood levels and enjoyed activities are associated with higher self-esteem. Sleep, social contact, and worries were significant predictors for only some individuals. Patient-individual parameters enable us to better understand the relationships between the variables on a patient-individual level. The analysis of relationships between self-esteem and other psychological factors on an individual level can therefore lead to valuable information for therapists and practitioners.
Application of Aluminum Oxide Nanoparticles in Aspergillus terreus Cultivations: Evaluating the Effects on Lovastatin Production and Fungal Morphology

Aluminum oxide nanoparticles were supplemented to Aspergillus terreus ATCC 20542 precultures and the outcomes of the process were evaluated relative to the results of microparticle-enhanced and standard cultivations. The selected morphological parameters of fungal pellets (projected area, elongation, convexity, and shape factor) were monitored throughout the experiment, together with biomass, lactose, and lovastatin concentration. The qualitative and quantitative chemical analysis was performed with the use of liquid chromatography coupled with high resolution mass spectrometry. The results of the study indicated that the application of nanoparticles was indeed associated with morphological consequences, most notably the decreased pellet size. However, it turned out that the term “nanoparticle-enhanced cultivation” could not be used in the context of lovastatin production, as no marked increase of product titer was observed in nanoparticle-influenced variants relative to standard and microparticle-enhanced cultivation. In addition, the concentration of biomass in the nanoparticle-influenced runs was relatively low. Comparative analysis of total ion chromatograms revealed the presence of a molecule of unknown structure that could be detected solely in broths from standard and microparticle-containing cultures. This study represents the first evaluation of nanoparticles as the tools of morphological engineering aimed at enhanced lovastatin biosynthesis in A. terreus cultures.
Transcranial Direct Current Stimulation (tDCS) in Unilateral Cerebral Palsy: A Pilot Study of Motor Effect

Transcranial Direct Current Stimulation (tDCS) is an emerging tool to improve upper limb motor functions after stroke acquired in adulthood; however, there is a paucity of reports on its efficacy for upper limb motor rehabilitation in congenital or early-acquired stroke. In this pilot study we have explored, for the first time, the immediate effects, and their short-term persistence, of a single application of anodal tDCS on chronic upper limb motor disorders in children and young individuals with Unilateral Cerebral Palsy (UCP). To this aim, in a crossover sham-controlled study, eight subjects aged 10-28 years with UCP underwent two sessions of active and sham tDCS. Anodal tDCS (1.5 mA, 20 min) was delivered over the primary motor cortex (M1) of the ipsilesional hemisphere. Results showed, only following the active stimulation, an immediate improvement in unimanual gross motor dexterity of hemiplegic, but not of nonhemiplegic, hand in Box and Block test (BBT). Such improvement remained stable for at least 90 minutes. Performance of both hands in Hand Grip Strength test was not modified by anodal tDCS. Improvement in BBT was unrelated to participants' age or lesion size, as revealed by MRI data analysis. No serious adverse effects occurred after tDCS; some mild and transient side effects (e.g., headache, tingling, and itchiness) were reported in a limited number of cases. This study provides an innovative contribution to scientific literature on the efficacy and safety of anodal tDCS in UCP. This trial is registered with NCT03137940.
Clinical Predictive Factors for the Efficacy of Everolimus in Patients With Hormone Receptor-Positive, HER2-Negative Advanced Breast Cancer: A Multicenter Retrospective Cohort Study in Japan

Purpose: To investigate the clinical predictive factors for the efficacy of everolimus (EVE) for advanced/metastatic breast cancer (AMBC). Methods: Routine practice data of consecutive patients with AMBC who received EVE at 5 institutions in western Japan were retrospectively analyzed in this cohort study (study registration no.: UMIN 000032569). The correlation among time to treatment failure (TTF), overall survival (OS), and clinical background was investigated via univariate and multivariate analyses using Cox hazards model for the clinically important variables. Results: A total of 134 patients were included in the analysis. The median TTF and OS were 5.2 months (95% confidence interval [CI]: 4.1-6.4) and 27.3 months (95% CI: 23.7-30.9), respectively. Multivariate analysis showed that dose reduction of EVE from any initial dose was associated with a longer TTF (hazard ratio [HR]: 0.52; 95% CI: 0.32-0.84, P = .007). Meanwhile, very low hormone sensitivity (ie, relapse within the first 2 years during adjuvant endocrine therapy or progression within 3 months of endocrine therapy immediately before EVE) was associated with a shorter TTF (HR: 2.48; 95% CI: 1.49-4.10, P < .001). In the analysis of stratified treatment outcomes, TTF was longer in the group with <3 liver metastases and in groups other than the very low hormone sensitivity group, regardless of the treatment line of EVE. Conclusions: Low hormone sensitivity and ⩾3 liver metastases were important prognostic factors for the efficacy of EVE. EVE may be less effective in patients with AMBC with these factors, and as such, chemotherapy should be administered instead.
Cellular MRI Reveals Altered Brain Arrest of Genetically Engineered Metastatic Breast Cancer Cells

Purpose The combined use of anatomical magnetic resonance imaging (MRI), cellular MRI, and bioluminescence imaging (BLI) allows for sensitive and improved monitoring of brain metastasis in preclinical cancer models. By using these complementary technologies, we can acquire measurements of viable single cell arrest in the brain after systemic administration, the clearance and/or retention of these cells thereafter, the growth into overt tumours, and quantification of tumour volume and relative cancer cell viability over time. While BLI is very useful in measuring cell viability, some considerations have been reported using cells engineered with luciferase such as increased tumour volume variation, changes in pattern of metastatic disease, and inhibition of in vivo tumour growth. Procedures Here, we apply cellular and anatomical MRI to evaluate in vivo growth differences between iron oxide labeled naïve (4T1BR5) and luciferase-expressing (4T1BR5-FLuc-GFP) murine brain-seeking breast cancer cells. Balb/C mice received an intracardiac injection of 20,000 cells and were imaged with MRI on days 0 and 14. Mice that received 4T1BR5-FLuc-GFP cells were also imaged with BLI on days 0 and 14. Results The number of signal voids in the brain (representing iron-labeled cancer cells) on day 0 was significantly higher in mice receiving 4T1BR5 cells compared to mice receiving 4T1BR5-FLuc-GFP cells (p < 0.0001). Mice that received 4T1BR5 cells also had significantly higher total brain tumour burden and number of brain metastases than mice that received 4T1BR5-FLuc-GFP cells (p < 0.0001). Conclusions By employing highly sensitive cellular MRI tools, we demonstrate that engineered cells did not form tumours as well as their naïve counterparts, which appear to primarily be due to a reduction in cell arrest. These results indicate that engineering cancer cells with reporter genes may alter their tropism towards particular organs and highlight another important consideration for research groups that use reporter gene imaging to track metastatic cancer cell fate in vivo.
Relative Frequencies of Alternating and Nonalternating Prime Knots and Composite Knots in Random Knot Spaces

ABSTRACT This article reports the results of an investigation into the average behavior of the knot spectrum (of knots up to 16 crossings) of a family of random knot spaces. A knot space in this family consists of random polygons of a given length in a spherical confinement of a given radius. The knot spectrum is the distribution of all knot types within a random knot space and is based on the probabilities that a randomly (and uniformly) chosen polygon from this knot space forms different knot types. We show that the relative spectrum of knots, when divided into groups by their crossing number, remains unexpectedly robust as these knot spaces vary. The relative spectrum for a given crossing number c is Pc(u)/Pc, where Pc is the probability that a uniformly chosen random polygon has crossing number c, and Pc(u) is the probability that the chosen polygon has crossing number c and is from the group of knots defined by the characteristic u (such as “alternating prime,” “nonalternating prime,” or “composite”). Specifically, for a fixed crossing number c, the results show that tighter confinement conditions favor alternating prime knots, that is Pc(A)/Pc (where A stands for “alternating prime”) increases as the confinement radius decreases, and that the average Pc(N) (where N stands for “nonalternating prime”) behaves similar to the average Pc − 1(A). We then use our simulations to speculate on limiting behavior.
The steganography of the typography in the digital printing technology

The steganography is the art and science of hiding the information. In this work the information is being hidden in a form of text or a typography applied by using the printing colors. Graphics visible to a human eye contains the hidden typography, visible only in the near infrared part of the spectrum. Steganography is achieved by using the values of the colors specially evaluated and adjusted for each printing technique separately.
Darknet Security: A Categorization of Attacks to the Tor Network

In the darknet security topic, it is important to analyze the threats that characterize the network. This paper deeply investigates the literature of attacks against the Tor network, presenting the most relevant threats in this context. In order to provide an important tool for the research community, we propose an exhaustive taxonomy based on the target of the attack. Such taxonomy represents a characterization scheme to identify cyber-attacks related to darknet environments and better understand their functioning. The proposed work should therefore be considered an important step forward in the darknet security ﬁeld.
DW-PathSim: a distributed computing model for topic-driven weighted meta-path-based similarity measure in a large-scale content-based heterogeneous information network*

ABSTRACT From the past, several studies in the information network mining have been mainly designed for single-typed objects and links, called the homogeneous information network (HoIN). These HoIN-based approaches are definitely unsuitable for multi-typed objects and links, known as the heterogeneous information network (HIN). There is no doubt that most of the real-world networks are not only composed in a complex heterogeneous manner but also are extremely large in size. The big size of these networks is one of the most challenging issues that influence directly the system's performance. In this paper, our studies are mainly focused on improving the topic-driven weighted similarity measurement between same-typed objects in HIN, based on the meta-path-based mechanism, called W-PathSim. Moreover, our contributions in this paper also aim to optimize the performance of the W-PathSim model in the manner of very large-scaled HIN by combining the proposed W-PathSim model with the approach of distributed computing of ‘graph-frames’ on Spark, called DW-PathSim. The DW-PathSim not only supports in tackling the problem of weighted meta-path-based similarity searching in HINs but also the distributed computing problem on the big networked data. We test the DW-PathSim model with the real-world DBLP dataset in order to demonstrate the effectiveness of our proposed models.
A Multicore Exact Algorithm for Addition Sequence

: This paper addresses one of the NP-complete problems that is used to reduce the number of computation for multi-exponentiation operation. The problem is the addition sequence for a set of n -positive integers X = { m 1 , m 2 , . , m n } such that 2≤ m i < m i+1 , 1≤ i < n and n≥ 2. We suggest a parallel hybrid algorithm that finds a minimal length addition sequence for X . The algorithm traverses the search tree using breadth-first and branch-and-bound strategies. We also measure the effectiveness of the suggested algorithm on a multicore system and by using C++ language with OpenMP. The experimental results on sets of different sizes and different number of cores show that the proposed parallel hybrid algorithm for minimal length addition sequences is faster than the fastest sequential algorithm with maximum speed up 2.7.