Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Acquisition of the Argument-Predicate Relations from a Frame-Annotated Corpus

This paper presents an approach to automatic acquisition of the argument-predicate relations from a semantically annotated corpus. We use SALSA, a German newspaper corpus manually annotated with role-semantic information based on frame semantics. Since the relatively small size of SALSA does not allow to estimate the semantic relatedness in the extracted argument-predicate pairs, we use a larger corpus for ranking. Two experiments have been performed in order to evaluate the proposed approach. In the first experiment we compare automatically extracted argument-predicate relations with the gold standard formed from associations provided by human subjects. In the second experiment we calculate correlation between automatic relatedness measure and human ranking of the extracted relations.
Dirichlet-tree cascaded Hough forests for continuous head pose estimation

We propose a hierarchical regression approach, Dirichlet-tree cascaded Hough forests (DCHF), which is based on deep learning for continuous head pose estimation in unconstrained environment, e.g., poses, illumination, occlusion, low image resolution, expressions and make-up. First, positive facial patches are learned and extracted from facial area to eliminate the influence of noise. Then, in order to estimate continuous head pose efficiently, multiple probability models are learned in four layers of the DCHF, i.e., the patch's classification, the head pose angles, and offset probabilities mapping in the Hough space in a hierarchical way. Moreover, our algorithm takes a weighted and cascaded Hough voting method, where each positive patch extracted from the face can cast the efficient vote for head pose estimation. Experimental results on different public databases demonstrate the robustness and accuracy of the proposed approach to continuous head pose estimation.
Distributed Adaptive Sliding Mode Control of Modular and Reconfigurable Robots

For modular and reconfigurable robots, it is very difficult to design effective controllers due to diverse configurations. Thus, a distributed adaptive sliding mode controller is proposed in this paper to satisfy the concept of modular software. Based on geometric formulation for the dynamics of rigid body, a robot dynamics system can be divided into some subsystems by a decomposing algorithm, and then an adaptive sliding mode controller is designed for each subsystem to handle the modeling uncertainty. These subsystem controllers together constitute a modular control network to achieve stable and reliable motion of a modular and reconfigurable robot. Finally, simulation results show the validity of the proposed distributed adaptive sliding mode controller.
Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences

Evaluating precisely the temporal variations of lesion volumes is very important for at least three types of practical applications: pharmaceutical trials, decision making for drug treatment or surgery, and patient follow-up. Here, the authors present a volumetric analysis technique, combining precise rigid registration of three-dimensional (3-D) (volumetric) medical images, nonrigid deformation computation, and flow-field analysis. Their analysis technique has two outcomes: the detection of evolving lesions and the quantitative measurement of volume variations. The originality of the authors' approach is that no precise segmentation of the lesion is needed but the approximative designation of a region of interest (ROI) which can be automated. They distinguish between tissue transformation (image intensity changes without deformation) and expansion or contraction effects reflecting a change of mass within the tissue. A real lesion is generally the combination of both effects. The method is tested with synthesized volumetric image sequences and applied, in a first attempt to quantify in vivo a mass effect, to the analysis of a real patient case with multiple sclerosis (MS).
Formatively assessing teamwork in technology-enabled twenty-first century classrooms: exploratory findings of a teamwork awareness programme in Singapore

Abstract Teamwork, one of the core competencies for the twenty-first century learner, is a critical skill for work and learning. However, assessing teamwork is complex, in particular, developing a measure of teamwork that is domain-generic and applicable across a wide range of learners. This paper documents one such study that leverages technology to help provide a formative assessment of teamwork. It focuses on the self and peer ratings of a teamwork measure and a pedagogical method, which was trialled as a teamwork awareness programme in a mainstream Secondary School in Singapore. This teamwork awareness programme was incorporated into the school’s Interdisciplinary Project Work curriculum. Findings of students’ experiences of the programme are described. The teamwork competency dimension of “team emotional support” was rated highest amongst students. Also, students’ report gains in teamwork awareness, mixed engagement in reflective practices, and on the real-world relevancy of the programme. Discussions and implications of the findings follow.
Seismic data interpolation and denoising in the frequency-wavenumber domain

I introduce a unified approach for denoising and interpolation of seismic data in the frequency-wavenumber (f-k) domain. First, an angular search in the f-k domain is carried out to identify a sparse number of dominant dips, not only using low frequencies but over the whole frequency range. Then, an angular mask function is designed based on the identified dominant dips. The mask function is utilized with the least-squares fitting principle for optimal denoising or interpolation of data. The least-squares fit is directly applied in the time-space domain. The proposed method can be used to interpolate regularly sampled data as well as randomly sampled data on a regular grid. Synthetic and real data examples are provided to examine the performance of the proposed method.
Independent component approach to the analysis of EEG and MEG recordings

Multichannel recordings of the electromagnetic fields emerging from neural currents in the brain generate large amounts of data. Suitable feature extraction methods are, therefore, useful to facilitate the representation and interpretation of the data. Recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings. In addition, ICA has been applied to the analysis of brain signals evoked by sensory stimuli. This paper reviews our recent results in this field.
PKA Phosphorylation of NDE 1 Is DISC 1 / PDE 4 Dependent and Modulates Its Interaction with LIS 1 and NDEL 1

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Combined Convex Technique on Delay-Dependent Stability for Delayed Neural Networks

In this brief, by employing an improved Lyapunov-Krasovskii functional (LKF) and combining the reciprocal convex technique with the convex one, a new sufficient condition is derived to guarantee a class of delayed neural networks (DNNs) to be globally asymptotically stable. Since some previously ignored terms can be considered during the estimation of the derivative of LKF, a less conservative stability criterion is derived in the forms of linear matrix inequalities, whose solvability heavily depends on the information of addressed DNNs. Finally, we demonstrate by two numerical examples that our results reduce the conservatism more efficiently than some currently used methods.
Cosmetics: They Influence More Than Caucasian Female Facial Attractiveness

The study explored whether 4 Caucasian women would be evaluated differently on 4 social measures depending on whether they were presented with or without makeup. Participants—152 men and 171 women—were split into 2 groups and were presented with the women's facial photographs either with or without cosmetics. Women presented wearing cosmetics were perceived as healthier and more confident than when presented without. Participants also awarded women wearing makeup with a greater earning potential and with more prestigious jobs than the same women without cosmetics. The results suggest that women can successfully employ cosmetics to manipulate how they are assessed, which may be advantageous in social situations where women may be judged on their appearance, such as job interviews.
Scalable software defined network controllers

Software defined networking (SDN) introduces centralized controllers to dramatically increase network programmability. The simplicity of a logical centralized controller, however, can come at the cost of control-plane scalability. In this demo, we present McNettle, an extensible SDN control system whose control event processing throughput scales with the number of system CPU cores and which supports control algorithms requiring globally visible state changes occurring at flow arrival rates. Programmers extend McNettle by writing event handlers and background programs in a high-level functional programming language extended with shared state and memory transactions. We implement our framework in Haskell and leverage the multicore facilities of the Glasgow Haskell Compiler (GHC) and runtime system. Our implementation schedules event handlers, allocates memory, optimizes message parsing and serialization, and reduces system calls in order to optimize cache usage, OS processing, and runtime system overhead. Our experiments show that McNettle can serve up to 5000 switches using a single controller with 46 cores, achieving throughput of over 14 million flows per second, near-linear scaling up to 46 cores, and latency under 200 μs for light loads and 10 ms with loads consisting of up to 5000 switches.
Reusable Self-Adaptation through Bidirectional Programming

In self-adaptive systems, an adaptation strategy can apply to several implementations of a target system. Reusing this strategy requires models of the target system that are independent of its implementation. In particular, configuration files must be transformed into abstract configurations, but correctly synchronizing these two representations is not trivial. We propose an approach that uses putback-based bidirectional programming to guarantee that this synchronization is correct by construction. We demonstrate the correctness of our approach and how it handles typical features of configuration files, such as implicit default values and context overriding. We also show that our approach can be used to migrate configuration files from one implementation to another. We illustrate our approach with a case study, where we use the same abstract model to adapt two web server implementations. For each implementation, we provide a bidirectional program that correctly synchronizes the configuration file with an abstract model of the configuration. A first scenario demonstrates that the same changes on the abstract model produce, for each implementation, a new configuration that correctly reflects the changes made to the abstract model, without side effects. A second scenario validates the migration of a configuration file from the format used by one web server implementation to another.
A specific deficit in spatial memory acquisition in post‐traumatic stress disorder and the role of sleep in its consolidation

Posttraumatic stress disorder (PTSD) is characterized by the presence of anatomo‐functional hippocampal alterations. To date, the ability to orient within the environment, which relies on hippocampal integrity, has never been investigated in PTSD. We hypothesized that the ability to form a cognitive map of the environment would be impaired in PTSD. Moreover, spatial memory consolidation benefits from postlearning sleep. Because PTSD individuals often complain about sleep disturbances, we hypothesized that any sleep effect on memory performance would be hampered in these subjects. Twenty‐two subjects, all survivors of the L'Aquila 2009 earthquake, were divided into a PTSD and a control group, based on clinical evaluation. After an acquisition phase, they were tested twice (“test” and “retest”) on a virtual navigation task. In addition, participants were administered the Digit Span and Task Switching. Subjective sleep quality and sleep disturbances were also assessed. The two testing sessions were on consecutive mornings, interspersed with a night of sleep. During the acquisition phase, the PTSD group took more than twice as long to form a cognitive map of the environment compared to the control group. However, once this phase was successfully completed, the two groups did not differ at test, but they tendentially differed at postsleep retest. Additional analyses comparing performances between groups on test–retest difference scores confirm that sleep‐dependent consolidation may be differentially affected in the two groups. Our findings are strictly confined to the navigation performance, excluding a generalized cognitive deficit. PTSD also reported more subjective sleep disturbances and shorter sleep time than controls, which were correlated to worse performance at retest. The specific deficit in the formation of a cognitive map reported in PTSD may be related to hippocampal dysfunctions as well as to the sleep disturbances experienced by these patients. The possible deficiency of sleep‐dependent spatial performance improvement should however be confirmed by further studies comprising a wake control group. © 2011 Wiley Periodicals, Inc.
Statistical Part-of-Speech Guessing for German: Support Vector Classifiers versus Voting

In this paper, I present a statistical-based approach to the part-of-speech guessing problem. I see assigning a part-of-speech, such as Adjective or Noun, as a classification problem. My guessing framework, which relies on automated learning of a language model, is described in detail. The rich feature analysis presented is suitable for linguistic data, such as the ones observed in German. I use a large margin classifier learning algorithm to select relevant features and learn appropriate labelling. The system is evaluated using a German corpus.
Wolfsoniana Smart Museum. A Pilot Plant Installation of the PALM-Cities Project

This demo paper describes the pilot plant of the PALM-Cities Project, installed at a modern art museum in Genoa. The objective of the system is offering visitors a novel and immersive experience when visiting the exhibit. In fact, owing to automatic identification technologies, such as Near Field Communication (NFC) and the Quick Response Code (QR Code), people can interact with the surrounding environment by means of their own devices, e.g., smartphones or tablets, receiving personalized contents as they move in a given location. Different resources for the same artwork in the museum are delivered to different users on the basis of their preferences, such as, e.g., language, age and device.
A 0.5 V class AB quasi FGMOS pseudo fully differential CMOS op-amp with rail-to-rail input/output swing

This paper presents a 0.5 V pseudo fully differential CMOS op-amp with rail-to-rail input/output swing. The circuit is designed based on class AB input and output stages. In the design, quasi FGMOS transistors are employed. The proposed amplifier is designed using 0.18 μm CMOS technology, and the simulation results show rail-to-rail input and output swings. The open-loop gain and gain-bandwidth product show 73.3 dB and 12.6 MHz. The CMRR is 73.2 dB (at 1 kHz) and the power consumption is 27.9 μW.
Protecting Minorities in Large Binary Elections: A Test of Storable Votes Using Field Data

Abstract The legitimacy of democratic systems requires the protection of minority preferences while ideally treating every voter equally. During the 2006 student elections at Columbia University, we asked voters to rank the importance of different contests and to choose where to cast a single extra "bonus vote," had one been available  a simple version of Storable Votes. We then constructed distributions of intensities and electoral outcomes and estimated the probable impact of the bonus vote through bootstrapping techniques. The bonus vote performs well: when minority preferences are particularly intense, the minority wins at least one contest with 15-30 percent probability; when the minority wins, aggregate welfare increases with 85-95 percent probability. The paper makes two contributions: it tests the performance of storable votes in a setting where preferences were not controlled, and it suggests the use of bootstrapping techniques when appropriate replications of the data cannot be obtained.
Iterative Closest Labeled Point for tactile object shape recognition

Tactile data and kinesthetic cues are two important sensing sources in robot object recognition and are complementary to each other. In this paper, we propose a novel algorithm named Iterative Closest Labeled Point (iCLAP) to recognize objects using both tactile and kinesthetic information. The iCLAP first assigns different local tactile features with distinct label numbers. The label numbers of the tactile features together with their associated 3D positions form a 4D point cloud of the object. In this manner, the two sensing modalities are merged to form a synthesized perception of the touched object. To recognize an object, the partial 4D point cloud obtained from a number of touches iteratively matches with all the reference cloud models to identify the best fit. An extensive evaluation study with 20 real objects shows that our proposed iCLAP approach outperforms those using either of the separate sensing modalities, with a substantial recognition rate improvement of up to 18%.
Eastern Polynesian: The Linguistic Evidence Revisited

For the past fifty years, historical linguistics and archaeology have provided seemingly mutually corroboratory evidence for the settlement of east Polynesia. However, more recent findings in archaeology have shifted this relationship out of balance, calling previous conclusions into question. This paper first reviews the generally accepted archaeological and linguistic theories of east Polynesia’s settlement, then describes the recent archaeological findings, highlighting the areas where the evidence from the two disciplines is now discordant. In sections four and five, I examine the linguistic data from Eastern Polynesian languages and propose a new, contact-based model for the region. The new linguistic model ultimately demonstrates that the settlement of east Polynesia and the development of the Eastern Polynesian languages occurred in one major period of dispersal, with subsequent spheres of contact among central Polynesian communities, producing the pattern of cultural and linguistic traits we see today.
Secure Statistical Analysis of Distributed Databases, Emphasizing What We Don't Know

Over the past several years, the National Institute of Statistical Sciences (NISS) has developed methodology to perform statistical analyses that, in effect, integrate data in multiple, distributed databases, but without literally bringing the data together in one place. In this paper, we summarize that research, but focus on issues that are not understood. These include inability to perform exploratory analyses and visualizations, protections against dishonest participants, inequities between database owners and lack of measures of risk and utility.
Mixing time for the Ising model: A uniform lower bound for all graphs

Consider Glauber dynamics for the Ising model on a graph of n vertices. Hayes and Sinclair showed that the mixing time for this dynamics is at least n log n/f(∆), where ∆ is the maximum degree and f(∆) = Θ(∆ log ∆). Their result applies to more general spin systems, and in that generality, they showed that some dependence on ∆ is necessary. In this paper, we focus on the ferromagnetic Ising model and prove that the mixing time of Glauber dynamics on any n-vertex graph is at least (1/4 + o(1))n log n.
A Large, Uniform Sample of X-Ray-emitting Active Galactic Nuclei from the ROSAT All Sky and Sloan Digital Sky Surveys: The Data Release 5 Sample

We describe further results of a program aimed at yielding ∼104 fully characterized optical identifications of ROSAT X-ray sources. Our program employs X-ray data from the ROSAT All Sky Survey (RASS) and both optical imaging and spectroscopic data from the Sloan Digital Sky Survey (SDSS). RASS/SDSS data from 5740 deg2 of sky spectroscopically covered in SDSS Data Release 5 provide an expanded catalog of 7000 confirmed quasars and other active galactic nuclei (AGNs) that are probable RASS identifications. Again, in our expanded catalog the identifications as X-ray sources are statistically secure, with only a few percent of the SDSS AGNs likely to be randomly superposed on unrelated RASS X-ray sources. Most identifications continue to be quasars and Seyfert 1 galaxies with 15 < m < 21 and 0.01 < z < 4, but the total sample size has grown to include very substantial numbers of even quite rare AGNs, e.g., it now includes several hundreds of candidate X-ray-emitting BL Lac objects and narrow-line Seyfert 1 galaxies. In addition to exploring rare subpopulations, such a large total sample may be useful when considering correlations between the X-ray and the optical and may also serve as a resource list from which to select the "best" object (e.g., X-ray-brightest AGN of a certain subclass at a preferred redshift or luminosity) for follow-up X-ray spectral or alternate detailed studies.
Supervised Categorization of JavaScript TM using Program Analysis Features

Web pages often embed scripts for a variety of purposes, including advertising and dynamic interaction. Understanding embedded scripts and their purposes can often help to interpret or provide crucial information about the web page. I have developed a functionality-based categorization of JavaScript, the most widely used web page scripting language. I then view understanding embedded scripts as a text categorization problem. I show how traditional information retrieval methods can be augmented with the features distilled from the domain knowledge of JavaScript and program analysis to improve classification performance. I perform experiments on the standard WT10G web page corpus, and show that my techniques eliminate over 50% of errors over a standard text classification baseline. Subject Descriptors: H.3. 3 Information Search and Retrieval F.3. 2 Semantics of Programming Languages D.2. 8 Metrics