Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effective field theory of codimension-two branes

Distributional sources of matter on codimension-two and higher branes are only well-defined as regularized objects. Nevertheless, intuition from effective field theory suggests that the low-energy physics on such branes should be independent of any high-energy regularization scheme. In this paper, we address this issue in the context of a scalar field model where matter fields (the standard model) living on such a brane interact with bulk fields (gravity). The low-energy effective theory is shown to be consistent and independent of the regularization scheme, provided the brane couplings are renormalized appropriately at the classical level. We perform explicit computations of the classical renormalization group flows at tree and one-loop level, demonstrate that the theory is renormalizable against codimension-two divergences, and extend the analysis to several physical applications such as electrodynamics and brane localized kinetic terms.
Eliciting Subjectivity and Polarity Judgements on Word Senses

There has been extensive work on eliciting human judgements on the sentiment of words and the resulting annotated word lists have frequently been used for opinion mining applications in Natural Language Processing (NLP). However, this word-based approach does not take different senses of a word into account, which might differ in whether and what kind of sentiment they evoke. In this paper, we therefore introduce a human annotation scheme for judging both the subjectivity and polarity of word senses. We show that the scheme is overall reliable, making this a well-defined task for automatic processing. We also discuss three issues that surfaced during annotation: the role of annotation bias, hierarchical annotation (or underspecification) and bias in the sense inventory used.
Bayesian Multiple Target Localization

We consider the problem of quickly localizing multiple targets by asking questions of the form "How many targets are within this set" while obtaining noisy answers. This setting is a generalization to multiple targets of the game of 20 questions in which only a single target is queried. We assume that the targets are points on the real line, or in a two dimensional plane for the experiments, drawn independently from a known distribution. We evaluate the performance of a policy using the expected entropy of the posterior distribution after a fixed number of questions with noisy answers. We derive a lower bound for the value of this problem and study a specific policy, named the dyadic policy. We show that this policy achieves a value which is no more than twice this lower bound when answers are noisefree, and show a more general constant factor approximation guarantee for the noisy setting. We present an empirical evaluation of this policy on simulated data for the problem of detecting multiple instances of the same object in an image. Finally, we present experiments on localizing multiple faces simultaneously on real images.
High-frequency corrections to the detector response and their effect on searches for gravitational waves

Searches for gravitational waves with km-scale laser interferometers often involve the long-wavelength approximation to describe the detector response. The prevailing assumption is that the corrections to the detector response due to its finite size are small and the errors due to the long-wavelength approximation are negligible. Recently, however, Baskaran and Grishchuk (2004 Class. Quantum Grav. 21 4041) found that in a simple Michelson interferometer such errors can be as large as 10%. For more accurate analysis, these authors proposed to use a linear-frequency correction to the long-wavelength approximation. In this paper we revisit these calculations. We show that the linear-frequency correction is inadequate for certain locations in the sky and therefore accurate analysis requires taking into account the exact formula, commonly derived from the photon round-trip propagation time. Also, we extend the calculations to include the effect of Fabry–Perot resonators in the interferometer arms. Here we show that a simple approximation which combines the long-wavelength Michelson response with the single-pole approximation to the Fabry–Perot transfer function produces rather accurate results. In particular, the difference between the exact and the approximate formulae is at most 2–3% for those locations in the sky where the detector response is greater than half of its maximum value. We analyse the impact of such errors on detection sensitivity and parameter estimation in searches for periodic gravitational waves emitted by a known pulsar, and in searches for an isotropic stochastic gravitational-wave background. At frequencies up to 1 kHz, the effect of such errors is at most 1–2%. For higher frequencies, or if more accuracy is required, one should use the exact formula for the detector response.
Changing planetary rotation rescues the biological clock mutant lhy cca1 of Arabidopsis thaliana

Background: Pervasive, 24-hour rhythms from the biological clock affect diverse biological processes in metabolism and behaviour, including the human cell division cycle and sleep-wake cycle, nightly transpiration and energy balance in plants, and seasonal breeding in both plants and animals. The clock mechanism in the laboratory model plant species Arabidopsis thaliana is complex, in part due to the multiple interlocking, negative feedback loops that link the clock genes. Clock gene mutants are powerful tools to manipulate and understand the clock mechanism and its effects on physiology. The LATE ELONGATED HYPOCOTYL and CIRCADIAN CLOCK ASSOCIATED 1 genes encode dawn-expressed, Myb-related repressor proteins that delay the expression of other clock genes until late in the day. Double mutant plants (lhy cca1) have low-amplitude, short-period rhythms that have been used in multiple studies of the plant circadian clock. Results: We used in vivo imaging of several luciferase (LUC) reporter genes to test how the rhythmic gene expression of wild-type and lhy cca1 mutant plants responded to light:dark cycles. Red, blue and red+blue light were similarly able to entrain these gene expression rhythms. The timing of expression rhythms in double mutant plants showed little or no response to the duration of light under 24h light:dark cycles (dusk sensitivity), in contrast to the wild type. As the period of the mutant clock is about 18h, we tested light:dark cycles of different duration (T cycles), simulating altered rotation of planet Earth. lhy cca1 double mutants regained as much dusk sensitivity in 20h T cycles as the wild type in 24h cycles, though the phase of the rhythm in the mutants was much earlier than wild type. The severe, triple lhy cca1 gi mutants also regained dusk sensitivity in 20h cycles. The double mutant showed some dusk sensitivity under 28h cycles. lhy cca1 double mutants under 28h cycles with short photoperiods, however, had the same apparent phase as wild-type plants. Conclusion: Simulating altered planetary rotation with light:dark cycles can reveal normal circadian performance in clock mutants that have been described as arrhythmic under standard conditions. The features rescued here comprise a dynamic behaviour (apparent phase under 28h cycles) and a dynamic property (dusk sensitivity under 20h cycles). These conditional clock phenotypes indicate that parts of the clock mechanism continue to function independently of LHY and CCA1, despite the major role of these genes in wild-type plants under standard conditions. Accessibility: Most results here will be published only in this format, citable by the DOI. Data and analysis are publicly accessible on the BioDare resource (www.biodare.ed.ac.uk), as detailed in the links below. Transgenic lines are linked to Stock Centre IDs below (Table 7).
A Genetic Algorithm Based on Modularity Density for Detecting Community Structure in Complex Networks

The problem of community structure detection in complex networks has been intensively investigated in recent years. Many algorithms were proposed based on optimization modularity. To overcome the solution limitation drawbacks of modularity function, as a new measure, modularity density for measuring the community structure was introduced. In this paper, we propose a genetic algorithm for detecting community structure in complex networks based on optimization modularity density. Experiments on synthetic and real life networks show the high capability of the method to successfully detect the network structure, particularly for the cases where the community structure is obscure.
Articulatory inversion of spanish speech signals by means of machine learning techniques

The speech inverse problem focuses on retrieving the best articulatory vectors for the task of synthesizing a target voice signal. In this research the problem is approached from a neuromotor level, and by means of some machine learning techniques. Specifically, speech articulators are represented on the midsagittal plane, and controlled by muscular activations grouped in the articulatory vector. The movement of the tongue, by effect of muscular contraction, is derived from a TSK Fuzzy Inference System. On its side, an Echo State Network is used to model the glottal airflow. Later, Continuous Genetic Algorithms evolve populations of articulatory configurations in order to approximate acoustic features of target spanish vowels, and of consonants /m/,/n/,/f/ and/s/. The recovered midsagittal configurations along with subjective tests performed by a group of evaluators, positively verify the effectiveness of these techniques.
Community effects of the opening of the Niagara casino.

AIMS The impacts on the community of the opening of a casino in Niagara Falls are studied. DESIGN The study uses a pre/post design for the community data, with pre/post data from Ontario as a whole as a comparison. SETTING The study site is the city of Niagara Falls, Ontario, where a casino opened in early December, 1996. PARTICIPANTS Using random-digit dialing, telephone interviews were conducted with adult residents of Niagara Falls in 1996 and 1997, and with adult residents of Ontario in 1995 and 1997. MEASUREMENTS Aside from demographic variables, measures included general attitudes to gambling, expectations about (1996) and experiences with (1997) the casino's opening, extent of participation in 11 types of gambling and 18 items on problems with gambling: five key items from a standard gambling problems score (SOGS), five life-area problems items, and items on pressures from others concerning the respondent's gambling and on gambling problems among family and friends. FINDINGS Attitudes to gambling remained stable in Niagara Falls, while there was some evidence of decline in approval in Ontario as a whole. While strong majorities of 1996 respondents had expected many positive and negative effects on The Community of the Casino's opening, Significantly fewer respondents in 1997 reported actually experiencing most of these effects. While a small increase in employment was found, it fell far below projections, a result probably reflecting displacement effects. The rate and level of casino gambling increased in Ontario, but increased even more in Niagara Falls, with little displacement of other gambling. Reported gambling problems increased significantly in Niagara Falls for two of 10 gambling problem items and for the short SOGS score, while rates were generally stable or declining in the province. Pressure from others about gambling rose significantly in Niagara Falls (in contrast to the province), and reported rates of family members or friends with gambling problems also rose substantially. There was an increasing trend in Niagara Falls for all 18 problem indicators. CONCLUSIONS The casino's opening brought more gambling by local residents, and an increase in reported gambling problems; yet support for the casino, already strong, if anything grew. At least in the short term, problems from the increased availability of gambling manifested themselves not in the public arena but rather in the arena of private life.
A Novel Approach to Improve the Accuracy of Web Retrieval

General purpose search engines utilize a very simple view on text documents: They consider them as bags of words. It results that after indexing, the semantics of documents is lost. In this paper, we introduce a novel approach to improve the accuracy of Web retrieval. We utilize the WordNet and WordNet SenseRelate All Words Software as main tools to preserve the semantics of the sentences of documents and user queries. Nouns and verbs in the WordNet are organized in the tree hierarchies. The word meanings are presented by numbers that reference to the nodes on the semantic tree. The meaning of each word in the sentence is calculated when the sentence is analyzed. The goal is to put each noun and verb of the sentence on the right place on the tree. Taking this information into account, it is possible to solve the ambiguity problem for the query keywords and create the indicative summaries taking into account query words, and semantically related hypernyms and synonyms.
On the achievability of interference alignment in the K-user constant MIMO interference channel

Interference alignment in the K-user MIMO interference channel with constant channel coefficients is considered. A novel constructive method for finding the interference alignment solution is proposed for the case where the number of transmit antennas equals the number of receive antennas (NT = NR = N), the number of transmitter-receiver pairs equals K = N + 1, and all interference alignment multiplexing gains are one. The core of the method consists of solving an eigenvalue problem that incorporates the channel matrices of all interfering links. This procedure provides insight into the feasibility of signal vector spaces alignment schemes in finite dimensional MIMO interference channels.
Consensus for agents with general dynamics using optimistic optimization

An important challenge in multiagent systems is consensus, in which the agents must agree on certain controlled variables of interest. So far, most consensus algorithms for agents with nonlinear dynamics exploit the specific form of the nonlinearity. Here, we propose an approach that only requires a black-box simulation model of the dynamics, and is therefore applicable to a wide class of nonlinearities. This approach works for agents communicating on a fixed, connected network. It designs a reference behavior with a classical consensus protocol, and then finds control actions that drive the nonlinear agents towards the reference states, using a recent optimistic optimization algorithm. By exploiting the guarantees of optimistic optimization, we prove that the agents achieve practical consensus. A representative example is further analyzed, and simulation results on nonlinear robotic arms are provided.
Self-organising symbolic aggregate approximation for real-time fault detection and diagnosis in transient dynamic systems

The development of accurate fault detection and diagnosis (FDD) techniques are an important aspect of monitoring system health, whether it be an industrial machine or human system. In FDD systems where real-time or mobile monitoring is required there is a need to minimise computational overhead whilst maintaining detection and diagnosis accuracy. Symbolic Aggregate Approximation (SAX) is one such method, whereby reduced representations of signals are used to create symbolic representations for similarity search. Data reduction is achieved through application of the Piecewise Aggregate Approximation (PAA) algorithm. However, this can often lead to the loss of key information characteristics resulting in misclassification of signal types and a high risk of false alarms. This paper proposes a novel methodology based on SAX for generating more accurate symbolic representations, called Self-Organising Symbolic Aggregate Approximation (SOSAX). Data reduction is achieved through the application of an optimised PAA algorithm, Self-Organising Piecewise Aggregate Approximation (SOPAA). The approach is validated through the classification of electrocardiogram (ECG) signals where it is shown to outperform standard SAX in terms of inter-class separation and intra-class distance of signal types.
Robot Self-localization with Optimized Error Minimizing for Soccer Contest

This paper proposed an integrated solution for robot self-localization in the RoboCup middle size league. Firstly, a kind of radial scan-lines are introduced to preprocess the images from the omni-directional camera so as to reduce the computational load and improve efficiency. The key points of this algorithm are minimizing the errors between the mark points and the detect points. In order to improve the self-location performance and reduce the effect by the dithering image, an optimized approach was presented and the extended Kalman filter was utilized to fuse visual information, odometry information and digital compass data. Secondly, before applying this approach into our robots, we built the simulate environment and compared the result to the existing particle filtering approach with 200 particles. Simulation result showed this approach is computational efficient and precise. Our method is computationally cheaper than the particle filtering approach. Finally, we tested the approach in the real RoboCup middle size league robot contest field. The experiments showed the optimized error minimizing self-localization provided high level capabilities.
A unified analysis of PWM converters in discontinuous modes

Three discontinuous operating modes of PWM (pulsewidth modulated) converters are considered: the discontinuous inductor current mode (DICM), the discontinuous capacitor voltage mode (DCVM), and a previously unidentified mode called the discontinuous quasi-resonant mode (DQRM). DC and small-signal AC analyses are applicable to all basic PWM converter topologies. Any particular topology is taken into account via its DC conversion ratio in the continuous conduction mode. The small-signal model is of the same order as the state-space averaged model for the continuous mode, and it offers improved predictions of the low-frequency dynamics of PWM converters in the discontinuous modes. It is shown that converters in discontinuous modes exhibit lossless damping similar to the effect of the current-mode programming. >
Matrix factorization approach to behavioral mode analysis from acceleration data

The field of Movement Ecology is experiencing a period of rapid growth in availability of data, and like many other fields is turning to data science for tools and methods to cope with the new challenges and opportunities that this presents. One rich and interesting source of data is the bio-logger. These small electronic devices are attached to animals free to roam in their natural habitats, and report back readings from multiple sensors, including GPS and accelerometer bursts. A common use of this accelerometer data is for supervised learning of behavioral modes. However, there is a need for unsupervised analysis tools as well, due to the inherent difficulties of obtaining a labeled dataset, which in some cases is either infeasible or does not successfully encompass the full repertoire of behavioral modes of interest. Here we present a matrix factorization based clustering method that allows either a soft or a hard partitioning of acceleration measurements, as well as a straight-forward way of drawing insight into the complex movements themselves. The method is validated by comparing the partitions with a labeled dataset, and is further compared to standard methods highlighting the advantages of the new method.
Neural Correlates of Empathy for Physical and Psychological Pain

Empathy is known as the ability to share and understand someone else’s feelings. Previous research has either addressed the neural correlates of empathy for pain or social exclusion, but no study has examined empathy for physical and psychological (social) pain simultaneously. Forty-seven participants completed our novel “Social Interaction Empathy Task” during electroencephalogram (EEG) recording. Participants had to observe and rate the intensity of physical and psychological pain in social interactions from a first- and third-person perspective. At the behavioral level, subjects did not differentiate between the perspectives and rated physically painful scenarios as more painful than psychologically painful and neutral interactions. Psychologically painful pictures were also rated as more painful than neutral pictures. Analysis of event-related potentials (ERPs) revealed an early and a late response with a higher ERP response to physical and psychological pain compared to neutral interactions. Moreover, a significant difference emerged between the two dimensions of painful interactions. Furthermore, we found that the activity over frontal regions for discrimination of painful interactions was lateralized to the right hemisphere. Moreover, we detected significant correlations with the self-rated perspective taking ability. This suggests the psychological and physical pain qualities are processed differently but both are related to empathic traits. We further suggest that the right hemisphere may be specifically involved in the processing of empathy-related tasks.
Shrinking the hypervisor one subsystem at a time: a userspace packet switch for virtual machines

Efficient and secure networking between virtual machines is crucial in a time where a large share of the services on the Internet and in private datacenters run in virtual machines. To achieve this efficiency, virtualization solutions, such as Qemu/KVM, move toward a monolithic system architecture in which all performance critical functionality is implemented directly in the hypervisor in privileged mode. This is an attack surface in the hypervisor that can be used from compromised VMs to take over the virtual machine host and all VMs running on it. We show that it is possible to implement an efficient network switch nfor virtual machines as an unprivileged userspace component running in the host system including the driver for the upstream network adapter. Our network switch relies on functionality already present in the KVM hypervisor and requires no changes to Linux, the host operating system, and the guest. Our userspace implementation compares favorably to the existing in-kernel implementation with respect to throughput and latency. We reduced per-packet overhead by using a run-to-completion model an are able to outperform the unmodified system for VM-to-VM traffic by a large margin when packet rates are high.