Miracle-dz/UniFAR-FTUs huggingface dataset:
Sharp dichotomies for regret minimization in metric spaces

The Lipschitz multi-armed bandit (MAB) problem generalizes the classical multi-armed bandit problem by assuming one is given side information consisting of a priori upper bounds on the difference in expected payoff between certain pairs of strategies. Classical results of Lai-Robbins [28] and Auer et al. [3] imply a logarithmic regret bound for the Lipschitz MAB problem on finite metric spaces. Recent results on continuum-armed bandit problems and their generalizations imply lower bounds of √t, or stronger, for many infinite metric spaces such as the unit interval. Is this dichotomy universal? We prove that the answer is yes: for every metric space, the optimal regret of a Lipschitz MAB algorithm is either bounded above by any f ε w(log t), or bounded below by any g ∈ o(√t). Perhaps surprisingly, this dichotomy does not coincide with the distinction between finite and infinite metric spaces; instead it depends on whether the completion of the metric space is compact and countable. Our proof connects upper and lower bound techniques in online learning with classical topological notions such as perfect sets and the Cantor-Bendixson theorem. We also consider the full-feedback (a.k.a., best-expert) version of Lipschitz MAB problem, termed the Lipschitz experts problem, and show that this problem exhibits a similar dichotomy. We proceed to give nearly matching upper and lower bounds on regret in the Lipschitz experts problem on uncountable metric spaces. These bounds are of the form Θ(tγ), where the exponent γ ε [1/2, 1] depends on the metric space. To characterize this dependence, we introduce a novel dimensionality notion tailored to the experts problem. Finally, we show that both Lipschitz bandits and Lipschitz experts problems become completely intractable (in the sense that no algorithm has regret o(t)) if and only if the completion of the metric space is non-compact.
Stay-In-Place FRP Form for Concrete Columns

Advanced fibre reinforced polymer (FRP) composites can be used in combination with traditional materials in a number of different applications such as slabs, beams, columns and piles. The advantages of combined construction include the cost-effectiveness and the ability to optimize the cross section based on material properties of each component. The most efficient use of FRP stay-in-place form is for concrete columns, as it eliminates the need for internal reinforcement and protects concrete against environmental effects. This paper provides a thorough review of the basic principles and research findings to date on the stay-in-place FRP forms for concrete columns. The various design issues including confinement modelling, axial-flexural behaviour, time-dependent behaviour, buckling and slenderness, pile driving, seismic behaviour, connections and modular construction, shear behaviour, fatigue performance, and nondestructive testing and inspection of stay-in-place FRP-concrete columns are discussed. The study to date shows the feasibility and effectiveness of the system for civil engineering applications.
An adaptive conflict-resolving neural-fuzzy classifier

This paper presents a novel soft computing classifier design based on neural networks and a constructive conflict-resolving algorithm. Specifically, the classifier is a hybrid model using the supervised Fuzzy ARTMAP (FAM) network and the Dynamic Decay Algorithm (DDA). The proposed model, called FAM-DDA, inherits the advantages of the predecessors, i.e., stable and incremental learning, compounded by dynamic width adjustment of the weights of conflicting classes. Classification performance of the FAM-DDA is evaluated using a number of benchmark datasets. The results are analyzed and compared with those from a number of machine learning approaches. Implications of the effectiveness of the FAM-DDA are discussed. Key-Words:-Adaptive Resonance Theory, Fuzzy ARTMAP, Dynamic Decay Algorithm, Pattern Classification
Multilingual Semantic Parsing with a Pipeline of Linear Classifiers

I describe a fast multilingual parser for semantic dependencies. The parser is implemented as a pipeline of linear classifiers trained with support vector machines. I use only first order features, and no pair-wise feature combinations in order to reduce training and prediction times. Hyper-parameters are carefully tuned for each language and sub-problem. The system is evaluated on seven different languages: Catalan, Chinese, Czech, English, German, Japanese and Spanish. An analysis of learning rates and of the reliance on syntactic parsing quality shows that only modest improvements could be expected for most languages given more training data; Better syntactic parsing quality, on the other hand, could greatly improve the results. Individual tuning of hyper-parameters is crucial for obtaining good semantic parsing quality.
Detection of Complex Salient Regions

The goal of interest point detectors is to find, in an unsupervised way, keypoints easy to extract and at the same time robust to image transformations. We present a novel set of saliency features based on image singularities that takes into account the region content in terms of intensity and local structure. The region complexity is estimated by means of the entropy of the gray-level information; shape information is obtained by measuring the entropy of significant orientations. The regions are located in their representative scale and categorized by their complexity level. Thus, the regions are highly discriminable and less sensitive to confusion and false alarm than the traditional approaches. We compare the novel complex salient regions with the state-of-the-art keypoint detectors. The presented interest points show robustness to a wide set of image transformations and high repeatability as well as allow matching from different camera points of view. Besides, we show the temporal robustness of the novel salient regions in real video sequences, being potentially useful for matching, image retrieval, and object categorization problems.
Seeing [u] aids vocal learning: Babbling and imitation of vowels using a 3D vocal tract model, reinforcement learning, and reservoir computing

We present a model of imitative vocal learning consisting of two stages. First, the infant is exposed to the ambient language and forms auditory knowledge of the speech items to be acquired. Second, the infant attempts to imitate these speech items and thereby learns to control the articulators for speech production. We model these processes using a recurrent neural network and a realistic vocal tract model. We show that vowel production can be successfully learnt by imitation. Moreover, we find that acquisition of [u] is impaired if visual information is discarded during imitation. This might give sighted infants an advantage over blind infants during vocal learning, which is in agreement with experimental evidence.
Validation of a Pediatric Primary Care Network in a US Metropolitan Region as a Community-Based Infectious Disease Surveillance System

This cross-sectional study used Geographic Information System methods to compare sociodemographic and clinical characteristics of children enrolled and not enrolled in a primary care network to determine the suitability of the network to estimate population-based disease rates. We validated the network surveillance system by comparing invasive pneumococcal disease rates between network and nonnetwork children using population-based surveillance data. Among the study population of 130300 children, network children were more likely to be female, Black, non-Hispanic, younger, and receive Medicaid. These differences varied across neighborhoods, however, adjusting for neighborhood characteristics did not significantly change observed differences. Rates of invasive pneumococcal disease were not significantly different between network and non-network children. Significant demographic and clinical differences existed between network and non-network children and varied over small areas. Observed population rates of an infectious disease did not significantly differ suggesting that the network can potentially provide valid disease estimates for the community population.
Python for Information Theoretic Analysis of Neural Data

Information theory, the mathematical theory of communication in the presence of noise, is playing an increasingly important role in modern quantitative neuroscience. It makes it possible to treat neural systems as stochastic communication channels and gain valuable, quantitative insights into their sensory coding function. These techniques provide results on how neurons encode stimuli in a way which is independent of any specific assumptions on which part of the neuronal response is signal and which is noise, and they can be usefully applied even to highly non-linear systems where traditional techniques fail. In this article, we describe our work and experiences using Python for information theoretic analysis. We outline some of the algorithmic, statistical and numerical challenges in the computation of information theoretic quantities from neural data. In particular, we consider the problems arising from limited sampling bias and from calculation of maximum entropy distributions in the presence of constraints representing the effects of different orders of interaction in the system. We explain how and why using Python has allowed us to significantly improve the speed and domain of applicability of the information theoretic algorithms, allowing analysis of data sets characterized by larger numbers of variables. We also discuss how our use of Python is facilitating integration with collaborative databases and centralised computational resources.
High frequency of promoter hypermethylation of the death-associated protein-kinase gene in nasopharyngeal carcinoma and its detection in the peripheral blood of patients.

PURPOSE Death-associated protein (DAP)-kinase gene is frequently inactivated by promoter hypermethylation in cancer. The aim of this study was to evaluate the promoter methylation status of the DAP-kinase gene in nasopharyngeal carcinoma (NPC). EXPERIMENTAL DESIGN The methylation status was evaluated by methylation-specific PCR (MSP). Thirty-two NPC biopsy specimens, plasma and buffy coat of 12 patients, 5 NPC cell lines, 3 normal nasopharyngeal biopsy tissues, and 2 normal nasopharyngeal epithelial primary cultures were included in this study. RESULTS There was no promoter hypermethylation in all 3 normal nasopharyngeal tissues and 2 normal nasopharyngeal primary cultures. Hypermethylation was found in 24 (75%) NPC primary tumor biopsies and 4 (80%) NPC cell lines. Of the 24 patients with hypermethylation of DAP-kinase promoter in the primary tumors, 12 patients had their plasma and buffy coat DNA available for MSP study. Hypermethylated DAP-kinase promoter was detectable in 5 patients in the plasma but not in the buffy coat, 2 patients in the buffy coat but not in the plasma, and 1 patient in both plasma and buffy coat. Four patients had no detectable hypermethylated DAP-kinase promoter in both plasma and buffy coat. Hypermethylation of DAP-kinase promoter was found in both early- and late-stage NPC. CONCLUSIONS Our results show that hypermethylation of the DAP-kinase promoter is a common early event in NPC. The high frequency of identification of hypermethylated DAP-kinase promoter in plasma and buffy coat of NPC patients illustrates its potential clinical application as tumor marker for the diagnosis and monitoring of treatment result.
Customer Relationship Investments, Value to the Customer, and Value to the Firm: Integrating Attributes and Benefits

A fundamental proposition in marketing strategy is that a customer relationship orientation is positively related to customer value and firm value. However, the mechanisms of this relationship have yet to be explored in detail, especially in service industries. Our paper addresses this issue by proposing a model that identifies important intermediate variables between customer relationship investments and both increased customer and firm value, namely, attributes, benefits, satisfaction, trust, commitment, and loyalty. Results show that customers purchase information education services with specific benefits that stem from corresponding attributes. These may be leveraged to create a competitive advantage, satisfaction, and repeat buying behavior, resulting in value for customers, and ease in attracting customers, thus enhancing value to the firm.
Probability Density Methods for Smooth Function Approximation and Learning in Populations of Tuned Spiking Neurons

This article proposes a new method for interpreting computations performed by populations of spiking neurons. Neural firing is modeled as a rate-modulated random process for which the behavior of a neuron in response to external input can be completely described by its tuning function. I show that under certain conditions, cells with any desired tuning functions can be approximated using only spike coincidence detectors and linear operations on the spike output of existing cells. I show examples of adaptive algorithms based on only spike data that cause the underlying cell-tuning curves to converge according to standard supervised and unsupervised learning algorithms. Unsupervised learning based on principal components analysis leads to independent cell spike trains. These results suggest a duality relationship between the random discrete behavior of spiking cells and the deterministic smooth behavior of their tuning functions. Classical neural network approximation methods and learning algorithms based on continuous variables can thus be implemented within networks of spiking neurons without the need to make numerical estimates of the intermediate cell firing rates.
Human behaviour in different social medias: A case study of Twitter and Disqus

Contemporary modern world has witnessed the widespread emergence of online social media and similar technologies. Peoples' behaviour over different social network platform has become an interesting topic of research. In this study, we investigate whether people express analogous identity over different platforms and analysis of different social platform usage contributes to reveal more of a person. We analyse people's usage pattern in two major online platforms, the most widely used social media platform Twitter and a major online commenting platform Disqus. We extract linguistic features and infer personality traits from both of these platforms. Our study reveals differential relationship between personality traits and Disqus and Twitter usage. We also find that social media has an influence on a person's discussion topic. People share opinion on varieties of topics and entities exclusively in Twitter and Disqus. Moreover Disqus provides stronger assessment of a person's sentiment over a topic or entity. Combination of these two profiles gives an extensive view of a user's interest and sensitivity which justify the inference that people use different social network for different purposes and single social network analysis is not enough to build a comprehensive virtual identity of a person.
A multi-timescale strength model of alpha-helical protein domains

Here we report a constitutive model that characterizes the strength of an alpha-helical protein domain subjected to tensile deformation, covering more than ten orders of magnitude in timescales. The model elucidates multiple physical mechanisms of failure in dependence on the deformation rate, quantitatively linking atomistic simulation results with experimental strength measurements of alpha-helical protein domains. The model provides a description of the strength of alpha-helices based on fundamental physical parameters such as the H-bond energy and the polypeptide’s persistence length, showing that strength is controlled by energetic, nonequilibrium processes at high rates and by thermodynamical, equilibrium processes at low rates. Our model provides a novel perspective on the strength of protein domains at ultra-slow pulling speeds relevant under physiologic and experimental conditions.
Dictionary attacks using keyboard acoustic emanations

We present a dictionary attack that is based on keyboard acoustic emanations. We combine signal processing and efficient data structures and algorithms, to successfully reconstruct single words of 7-13 characters from a recording of the clicks made when typing them on a keyboard. Our attack does not require any training, and works on an individual recording of the typed word (may be under 5 seconds of sound). The attack is very efficient, taking under 20 seconds per word on a standard PC. We demonstrate a 90% or better success rate of finding the correct word in the top 50 candidates identified by the attack, for words of 10 or more characters, and a success rate of 73% over all the words we tested. We show that the dominant factors affecting the attack's success are the word length, and more importantly, the number of repeated characters within the word. Our attack can be used as an effective acoustic-based password cracker. Our attack can also be used as part of an acoustic long-text reconstruction method, that is much more efficient and requires much less text than previous approaches.
3-dimensional electrokinetic tweezing

We show how to extend electrokinetic tweezing, which can manipulate any visible particles and has more favorable force scaling than optical actuation enabling manipulation of nanoscale objects to nanoscopic precision, from 2-dimensional control to the third dimension (3D). A novel and practical multilayer device is presented that can create both planar and vertical flow and electric field modes. Feedback control algorithms are developed and demonstrated in realistic simulations to show 3D manipulation of one and two particles independently. The design and control results presented here are the essential next step to go from current 2D manipulation capabilities to an experimental demonstration of nanoprecision 3D electrokinetic tweezing in a microfluidic system. Doing so requires integration with vision-based nano-precise 3D particle imaging, a capability that has been shown in the literature and which we are now combining with the 3D actuation and control methods demonstrated here.
Corrosion Measurements on Thin-Film Disks Using Optical Surface Analysis

Abstract The diminishing thickness of the carbon overcoat layer on a thin-film magnetic storage disk results in a greater susceptibility to corrosion and a need to move toward a fast and reliable corrosion analysis technique. A new method for quantifying corrosion levels on thin-film disks has been developed using an optical surface analyzer (OSA). High- and low-corrosion-level disks were used to develop and verify the necessary algorithms for surface analysis. The results from OSA were referenced to established methods such as scanning electron microscopy (SEM), scanning Auger microscopy (SAM), and ion chromatography (IC) wash techniques. A reliable method for identifying nickel- and cobalt-based corrosion and for separating corrosion from debris was developed. The OSA proved to be a faster and more sensitive corrosion-detection technique than SAM.
Role of a Community-to-Community Learning Strategy in the Institutionalization of Community Mobilization among Female Sex Workers in India

Introduction The institutionalization of community mobilization is not well understood in literature. This paper aims to understand the role of the community-to-community learning strategy in the institutionalization of community mobilization among sex workers communities across eight districts of Andhra Pradesh, India. Materials and Methods Data collected during baseline (March, 2010) and endline (June, 2012) under an HIV prevention project (SAKSHAM project) was used to investigate the strength (as score) of community mobilization based on two learning strategies: non-government organization (NGO)-to-community-based organization (CBO) strategy, and community-to-CBO strategy. The strength of community mobilization was assessed based on different parameters. The change in scores were computed as a percentage of the improvement to the total potential improvement from baseline to endline on specific indicators and overall. Results Most of the CBOs considered in the pre-post assessment had been registered during 2004–2008. At baseline, the community ownership and preparedness index scores for the eight CBOs under the community-to-CBO strategy ranged between 21.5 and 27.7 while the scores for the three CBOs under the NGO-to-CBO strategy ranged between 16.3 and 21.5. By endline, the strength of community mobilization among CBOs under the community-to-CBO strategy increased 18 points (equivalent to 23% potential improvement) whereas the strength of community mobilization among CBOs under the NGO-to-CBO strategy increased only 10 points (equivalent to 13% potential improvement). The average percentage difference in improvement between the strategies was 10% (p = 0.102). Further analyses indicate that a greater improvement in community-to-CBO learning strategy was noted around managerial capacities and engagement with stakeholders than other parameters. Conclusion The community –to- CBO learning strategy presents promising results for HIV prevention with regard to institutionalization of community mobilization among sex workers communities. Findings support the scaling-up of community mobilization initiatives within HIV prevention interventions using well trained community members in India and elsewhere.
CROSS OWNERSHIP AND INTERLOCKING DIRECTORATES BETWEEN BANKS AND LISTED FIRMS: AN EMPIRICAL ANALYSIS OF THE EFFECTS ON DEBT LEVERAGE AND COST OF DEBT IN THE ITALIAN CASE

Cross-ownership and interlocking directorates are two typical features of Italian capitalism involving both banks and non-financial companies. It is possible to refer to this phenomenon as “bank-firm connection”. The paper investigates the effects of “bank-firm connection” on the conditions of credit relationship. In particular, we find that the “bank-firm connection” lowers the leverage ratio of non financial companies, while it increases their cost of debt.