Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Bayesian Grasp Optimization with Wrench Space Analysis

Grasp planning and optimization have been hot topics in the last decade. Due to the complexity of generating good grasps in analytic form for arbitrary objects, most of the proposed approaches rely on sampling strategies: candidate grasps are generated according to some criteria and then ranked with some quality metric. Uniform sampling around heuristic pre-grasps [1], [2], simulated annealing [3], randomized trees [4] and active learning [5] are stateof-the-art techniques to generate grasp candidates. In thi s work we propose the use of Bayesian Optimization methods [6] to tackle the problem. Our work exploits a sequential sampling strategy, where the results from previous trials convey information to guide the next samples. We show experimentally that, depending on object’s shape properti es, sequential decision may reduce significantly the number of trials necessary to achieve quasi-optimal grasps with resp ect to random search. We use the OpenRave Simulation Environment [7] to develop our methods. The simulation environment consists of a manipulator arm, the Barrett hand, and a few objects of different complexities (Fig. 1). A grasp is parameterize d by the x and y positions with respect to an object-centered reference frame. A trial consists in placing the open robot hand at the chosen configuration ( x,y), then close the fingers until they contact with the object surface, and finally compu te the grasp quality metric. The objective is to optimize the grasp parametersx and y such as to obtain the highest quality value. We use a wrench space based metric as in [3] able to evaluate both force-closure and non-force-clos ure grasps. Each trial feeds a Gaussian Process Regression mode l [8] that estimates the expected value and variance of the quality metric. These measures can be evaluated easily at any point on the state space. To decide the next point to try, we maximize at each time step a form of Expected Improvement function (EI) with exploitation-vs-exploita tion control [9]. This function is derived from the Gaussian process in a straightforward manner. To search for its globa l maximum we use the DIRECT algorithm [10]. This is a global optimization method that works by partitioning the space in intervals (DIRECT stands for DIvide RECTangles), evaluating the EI in their center, and choosing, at each time step, the interval where the EI can be maximal for any bound on the function derivative (Lipschitz constant). We perfor med experiments in simulation to evaluate our approach with the
Using Parallel Corpora for Word Sense Disambiguation

Word Sense Disambiguation (WSD) is the Natural Language Processing (NLP) task that consists in selecting the correct sense of a polysemous word in a given context. Most state-of-the-art WSD systems are supervised classifiers that are trained on manually sense-tagged corpora, which are very time-consuming and expensive to build. In order to overcome this acquisition bottleneck (sense-tagged corpora are scarce for languages other than English), we decided to take a multilingual approach to WSD, that builds up the sense inventory on the basis of the Europarl parallel corpus [3]. Using translations from a parallel corpus implicitly deals with the granularity problem as finer sense distinctions are only relevant as far as they are lexicalized in the target translations. It also facilitates the integration of WSD in multilingual applications such as multilingual Information Retrieval (IR) or Machine Translation (MT).
From gamification to pervasive game in mapping urban accessibility

In this paper we present the design and implementation of two different game applications, designed and developed in order to extend and motivate the community of mPASS, an urban accessibility mapping system. On the one hand, mPASS relays to collect a sufficiently dense, detailed and trustworthy amount of data. On the other hand, its niche goal somehow limits its natural audience, which is not enough to reach the critical mass of information needed by the system in order to provide effective accessibility maps for its users. To overcome this problem, we resorted to the use of a gamification strategy to develop both a traditional prizing add-on and a novel mobile game targeting young and adults players, aiming to include them into the data contributors community. Design process and development description of both games are presented, highlighting the design iterations of the concepts, motivated by contribution gathered from feedback sessions, focus group and experience prototyping.
An Analysis of Web Documents Retrieved and Viewed

The placement of Websites in ranked retrieval and the viewing patterns of Web search engine users is a crucial issue for Web site owners and Web search engines. However, little largescale research has examined the viewing patterns of users of commercial Web search engines. The research results reported here address three questions, which are: (1) How many pages of results do Web search engine users’ examine? (2) How many Web documents do Web search engine users’ view when searching the Web? , and (3) How relevant are the Web documents that they are viewing? We present findings from large-scale research into the page viewing patterns of users of the FAST commercial Web search engine. Using data samples representing thousands of users, we examine common patterns concerning the number of pages of results viewed, the number of pages viewed and the relationship between the number of queries, the number of actual Web sites visited, and time between multiple site visits. The implications for Web search engines and services, Web sites and Web users are discussed.
Operational feedback considering social contingency for robot teleoperation

In telerobotics systems for children, it is crucial that the teleoperation interface is intuitive and well-controllable in terms of communication delay so that the children do not become bored quickly. To this end, we consider an operational feedback design for the teleoperation interface that incorporates the idea of social contingency detection, which is borrowed from a developmental psychology literature. Because the idea itself is general and there has been no attempt to implement it in telerobotics systems to date, in this study, which aims at a better operational feedback design, we test some representative implementations in human-subject experiments and report the results.
Non-linear large signal PA modelling for switching-mode operation (class-F/continuous class-F)

This paper proposes a novel GaN HEMT non-linear model extraction method from small-signal cold S-parameters and large signal waveform measurements, here demonstrated in simulation. These waveform measurements are taken in simple 50Ω conditions, but will be shown to be suitable for switching-mode power amplifier (PA) designs. Class-F and Continuous Class-F switching modes will be demonstrated with this model and verified by comparison with existing work which uses load-pull measurements to achieve the same results.
A multichannel hierarchical approach to adaptive Volterra filters employing filtered-X affine projection algorithms

It is shown in this paper how the use of a recently introduced algebra, called V-vector algebra, can directly lead to the implementation of Volterra filters of any order P in the form of a multichannel filterbank. Each channel in this approach is modeled as a finite impulse response (FIR) filter, and the channels are hierarchically arranged according to the number of the filter coefficients. In such a way, it is also possible to devise models of reduced complexity by cutting the less relevant channels. This model is then used to derive efficient adaptation algorithms in the context of nonlinear active noise control. In particular, it is shown how the affine projection (AP) algorithms used in the linear case can be extended to a Volterra filter of any order P. The derivation of the so-called Filtered-X AP algorithms for nonlinear active noise controllers is easily obtained using the elements of the V-vector algebra. These algorithms can efficiently replace the standard LMS and NLMS algorithms usually applied in this field, especially when, in practical applications, a reduced-complexity multichannel structure can be exploited.
Exploring Parallel Tractability of Ontology Materialization

Materialization is an important reasoning service for applications built on the Web Ontology Language (OWL). To make materialization efficient in practice, current research focuses on deciding tractability of an ontology language and designing parallel reasoning algorithms. However, some well-known large-scale ontologies, such as YAGO, have been shown to have good performance for parallel reasoning, but they are expressed in ontology languages that are not parallelly tractable, i.e., the reasoning is inherently sequential in the worst case. This motivates us to study the problem of parallel tractability of ontology materialization from a theoretical perspective. That is, we aim to identify the ontologies for which materialization is parallelly tractable, i.e., in NC complexity. In this work, we focus on datalog rewritable ontology languages. We identify several classes of datalog rewritable ontologies (called parallelly tractable classes) such that materialization over them is parallelly tractable. We further investigate the parallel tractability of materialization of a datalog rewritable OWL fragment DHL (Description Horn Logic) and an extension of DHL that allows complex role inclusion axioms. Based on the above results, we analyze real-world datasets and show that many ontologies expressed in DHL or its extension belong to the parallelly tractable classes.
Census Data Mining – An Application

Because of data privacy regulations, census data are available for analysis only in aggregated form. Primary data (responses of persons) are aggregated in many cross tabulations for small geographical units. Thus the target objects of secondary analysis are small areas (enumeration districts or wards ). Any cell or marginal of a cross tabulation can be used as variable on these target objects. The target objects can be linked with other spatial objects (e.g. rivers, roads, railway lines) for spatial analyses. In this paper we discuss the special requirements that occur for this type of aggregate data mining including spatial analyses. We show an application of SubgroupMiner, which is an advanced subgroup mining system supporting multirelational hypotheses, efficient data base integration, discovery of causal subgroup structures, and visualization based interaction options.
Towards cognitive brain-computer interfaces : real-time monitoring of visual processing and control using electroencephalography

Brain-computer interfaces (BCIs) offer alternative communication pathways between the brain and its environment. They can be used to replace a defective biological function or to provide the user with new ways of interaction. Output BCIs, which are based on the reading of biological data, require the measurement of control signals as stable as possible in time and in the population. Identification and calibration of such signals are crucial steps in the conception of a BCI.The first part of this study focuses on BCIs using visual evoked potentials (VEPs) as control signals. A model is proposed to predict steady-state VEPs individually, i.e. to predict the response of a given subject’s brain to periodic visual stimulations. This model uses a linear summation of transient VEPs and an amplitude correction for quantitative prediction of the shape and spatial organization of the brain response to repeated stimulations. The simulated signals are then used as a basis of comparison for real-time decoding of electroencephalographic signals in a BCI.In the second part of this study, a paradigm is proposed for the development of cognitive BCIs, i.e. for the real-time measuring of high-level brain functions. The originality of the paradigm lies in the fact that correlates of cognition are measured continuously, instead of being observed on discrete events. An experiment with the purpose of discriminating between several levels of sustained visual attention is proposed, with the ambition of real-time measurement for the development of neurofeedback systems.
Automatic image quality assessment in optical fundus images

Automatic image quality assessment (IQA) is a major issue in medical imaging ̧ but it is most often viewed as a signal processing or degradation/restoration problem whose relevance is most acute when e.g. evaluating lossy compression methods for image storage or transmission (e.g. [2], [1]). A much less frequently studied issue is having to decide whether a particular image is suitable for diagnosis purposes. This latter topic is especially pertinent in the context of tele-medicine operations where a remote operator is in charge of taking images that will eventually be analysed by a physician. In this context, the operator may not be familiar with the criteria guiding the assessment of images and some form of automated support should be provided. Our focus is more specifically on quality assessment of ophthalmic images captured by a high-quality (3-CCD) color camera. Although noise is kept to minimal levels, a number of factors may degrade the image:
Digital camera workflow for high dynamic range images using a model of retinal processing

We propose a complete digital camera workflow to capture and render high dynamic range (HDR) static scenes, from RAW sensor data to an output-referred encoded image. In traditional digital camera processing, demosaicing is one of the first operations done after scene analysis. It is followed by rendering operations, such as color correction and tone mapping. In our workflow, which is based on a model of retinal processing, most of the rendering steps are performed before demosaicing. This reduces the complexity of the computation, as only one third of the pixels are processed. This is especially important as our tone mapping operator applies local and global tone corrections, which is usually needed to well render high dynamic scenes. Our algorithms efficiently process HDR images with different keys and different content.
Heuristics for the Bandwidth Coloring Problem

The bandwidth coloring problem consists of assigning a color to each vertex of a graph, so that the absolute value of the difference between the colors of adjacent vertices is at least the value of the weight of the associated edge. This problem generalizes the classical vertex coloring problem and different heuristics have recently been proposed to obtain high quality solutions. In this paper we describe both memory-based and memory-less methods to solve the bandwidth coloring problem. In particular we propose new constructive and improvement methods based on tabu search and GRASP. Comparison of our results with previously reported instances and existing heuristics indicate that the methods we propose are competitive and require short computational times. Our findings also disclose that memory appears to play a more important role during improvement phases of search than during constructive phases.
Weierstrass coefficients of the canonical lifting

Given an ordinary elliptic curve [Formula: see text] over a field of characteristic [Formula: see text], there are functions [Formula: see text] and [Formula: see text] such that the curve [Formula: see text] where [Formula: see text] and [Formula: see text] is the canonical lifting of [Formula: see text]. Although these functions are not uniquely determined, we prove that they can be taken to be in [Formula: see text], defined for all ordinary elliptic curves of the given characteristic, and modular, with [Formula: see text] and [Formula: see text].
Compact, reliable efficiency

The work presents a multiphase DC-DC converter aimed to equip a C segment, next generation car having a 42/14 V automotive dual-voltage electric power system. The inverted power flow can be obtained by an additional DC-DC converter working in step-up mode or by using electronic interface based on a bilateral converter topology. The experimental result show that proposed solution has greater efficiency over the entire input voltage range.
Dcoument Clustering in Research Literature Based on NMF and Testor Theory

The paper proposes a new way of comprising the NMF and testor theory to cluster research literature. NMF method is good at dealing with high dimensional documents and clustering, while testor theory is used to find the topic of each cluster. The whole process is described in detai through an example of ten abstracts of Chinese science literature from magazines relative to environmental science. In the end, a case study about automatic classification of a conference proceeding (in Chinese) is given. The result shows the effectiveness of the whole method.
Self-healing and secure adaptive messaging middleware for business-critical systems

Current business-critical systems have stringent requirements for the significant and measurable increase in the end-to-end intelligence, security, scalability, self-adaptation and resilience. Existing state-of-the-art messaging systems achieve arbitrary resilience by a brute-force approach. Selfhealing is either rudimentary or non-existent. In this study we present a self-healing and secure adaptive messaging middleware that provides solutions to overcome limitations in robustness, resilience, self-adaptability, scalability, and assurance against security threats and erroneous input during run-time in the face of changing threats. This developed system supports a messaging infrastructure which enables adaptive functions and assurance against security vulnerabilities and erroneous input vulnerabilities to improve the reliability, robustness and dependability of business-critical infrastructures. It provides autonomous adjustments of the run-time configuration of the system in order to preserve and maintain optimal and uninterrupted operation, improvement of the strength of security and degree of trust in the system, and improvement of the assessability and verifiability of the trustworthiness of the system. The methodology used in this research is partly analytical and partly experimental. We develop the new core functionalities theoretically and validate them practically by prototyping.
Text Mining on Semi-structured E-Government Digital Archives of China

The Chinese government has devoted to e-Government development for decades. Digital archives have been built up in almost every level of Chinese government hierarchy. The structure features of the archives in Chinese government are discussed first in this paper. Then, it proposes a practical approach for text mining of these semi-structured archives. With the help of Semantic Web related technology, knowledge extracted through mining can be stored and managed in ontology base for administrators, and can be retrieved through ontology presentation system for end-users. Finally, a case study in a provincial archives bureau of China is introduced, which shows that knowledge among the archives especially dynamic knowledge can be easily mined and utilized in this way. It helps a lot in policy making, emergency decision support, and government routines.
JNK (c-Jun NH2-terminal Kinase) Is a Target for Antioxidants in T Lymphocytes*

AP-1 has been shown to behave as a redox-sensitive transcription factor that can be activated by both oxidant and antioxidant stimuli. However, the mechanisms involved in the activation of AP-1 by antioxidants are largely unknown. In this study we show that the structurally unrelated antioxidant agents pyrrolidine dithiocarbamate (PDTC), butylated hydroxyanisole, and Nacetylcysteine activated JNK (c-Jun NH2-terminal kinase) in Jurkat T cells. This activation differed substantially from that mediated by phorbol 12-myristate 13-acetate (PMA) and Ca2+ ionophore or produced by costimulation with antibodies against the T cell receptor-CD3 complex and to CD28. The activation of JNK by classical T cell stimuli was transient, whereas that mediated by PDTC and butylated hydroxyanisole (but not N-acetylcysteine) was sustained. The kinetics of JNK activation correlated with the expression of c-jun which was transient after stimulation with PMA plus ionophore and prolonged in response to PDTC, which also transiently induced c-fos. In addition, JNK activation by PMA plus ionophore was sensitive to inhibitors of signaling pathways involving Ca2+, protein kinase C, and tyrosine phosphorylation, which failed to inhibit the activation mediated by PDTC. Transfection of trans-dominant negative expression vectors of ras and raf, together with AP-1-dependent reporter constructs, as well as Western blot analysis using anti-ERK (extracellular signal-regulated kinase) antibodies, indicated that the Ras/Raf/ERK pathway did not appear to mediate the effect of the antioxidant. However, the combined treatment with PDTC and PMA, two agents that synergize on AP-1 activation, resulted in the persistent phosphorylation of ERK-2. In conclusion, our results identify JNK as a target of antioxidant agents which can be regulated differentially under oxidant and antioxidant conditions.
TX: Algorithmic Energy Saving for Distributed Dense Matrix Factorizations

The pressing demands of improving energy efficiency for high performance scientific computing have motivated a large body of solutions using Dynamic Voltage and Frequency Scaling (DVFS) that strategically switch processors to low-power states, if the peak processor performance is unnecessary. Although OS level solutions have demonstrated the effectiveness of saving energy in a black-box fashion, for applications with variable execution patterns, the optimal energy efficiency can be blundered away due to defective prediction mechanism and untapped load imbalance. In this paper, we propose TX, a library level race-tohalt DVFS scheduling approach that analyzes Task Dependency Set of each task in distributed Cholesky/LU/QR factorizations to achieve substantial energy savings OS level solutions cannot fulfill. Partially giving up the generality of OS level solutions per requiring library level source modification, TX leverages algorithmic characteristics of the applications to gain greater energy savings. Experimental results on two clusters indicate that TX can save up to 17.8% more energy than state-of-the-art OS level solutions with negligible 3.5% on average performance loss.
The Complexity of Learning Tree Patterns from Example Graphs

This article investigates the problem of learning tree patterns that return nodes with a given set of labels, from example graphs provided by the user. Example graphs are annotated by the user as being either positive or negative. The goal is then to determine whether there exists a tree pattern returning tuples of nodes with the given labels in each of the positive examples, but in none of the negative examples, and furthermore, to find one such pattern if it exists. These are called the satisfiability and learning problems, respectively. This article thoroughly investigates the satisfiability and learning problems in a variety of settings. In particular, we consider example sets that (1) may contain only positive examples, or both positive and negative examples, (2) may contain directed or undirected graphs, and (3) may have multiple occurrences of labels or be uniquely labeled (to some degree). In addition, we consider tree patterns of different types that can allow, or prohibit, wildcard labeled nodes and descendant edges. We also consider two different semantics for mapping tree patterns to graphs. The complexity of satisfiability is determined for the different combinations of settings. For cases in which satisfiability is polynomial, it is also shown that learning is polynomial. (This is nontrivial as satisfying patterns may be exponential in size.) Finally, the minimal learning problem, that is, that of finding a minimal-sized satisfying pattern, is studied for cases in which satisfiability is polynomial.