Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping the extended frontiers of escapism: binge-watching and hyperdiegetic exploration

ABSTRACT Through a micro-ethnographic engagement with consumers’ binge-watching experiences of the web-TV series House of Cards, this videography explores what we consider to be the ‘extended frontiers’ of escapism. In contrast to passive/active classifications of escapism which risk reducing escapist fare to a textual resource which can be categorised discretely at the point of consumption, we consider ‘sustained encounters’ with escapist fare as appropriable textures characterised by ongoing and less immediately discernible processes. Drawing upon the concept of hyperdiegesis, we consider potentially ‘projective’ forms of narrative transportation in binge-watching; heterochronic breaks from normal patterns of time; and post-object behaviours. In doing so, we outline how forms of escapism traditionally considered passive may under certain conditions represent much richer and more complex enterprises than previously imagined.
Handwritten digits recognition using OpenCV Machine

The automated recognition of handwritten digits is a largely studied problem which connects the fields of Computer Vision and Machine Learning and has many applications in real life. In this project, I detail an introductory investigation of the performance of classification in several contexts. Namely, relying on the OpenCV implementations of k-Nearest Neighbor, Random Forests, and Support Vector Machines classifiers, I compare the error rates obtained using several preprocessings of the dataset as well as various choices of features. The best obtained error rate on the MNIST dataset [2] is 0.81%, obtained with a Support Vector Machines classifier with a Gaussian kernel, using deskewing and blurring as preprocessing and using as features a combination of pixel intensities and histograms of orientations of gradients.
PaperTonnetz: Music Composition with Interactive Paper

Tonnetze are space-based musical representations that lay out individual pitches in a regular structure. They are primarily used for analysis with visualization tools or on paper and for performance with button-based tablet or tangible interfaces. This paper first investigates how properties of Tonnetze can be applied in the composition process, including the two-dimensional organization of pitches, based on a chord or on a scale. We then describe PaperTonnetz, a tool that lets musicians explore and compose music with Tonnetz representations by making gestures on interactive paper. Unlike screen-based interactive Tonnetz systems that treat the notes as playable buttons, PaperTonnetz allows composers to interact with gestures, creating replayable patterns that represent pitch sequences and/or chords. We describe the results of an initial test of the sys- tem in a public setting, and how we revised PaperTonnetz to better support three activities: discovering, improvising and assembling musical sequences in a Tonnetz. We con- clude with a discussion of directions for future research with respect to creating novel paper-based interactive music representations to support musical composition.
Timing over Tuning: Overcoming the Shortcomings of a Line Attractor during a Working Memory Task

How the brain stores information about a sensory stimulus in working memory is not completely known. Clues about the mechanisms responsible for working memory can be gleaned by recording from neurons during the performance of a delayed response task. I focus on the data recorded during such an experiment, a classic tactile discrimination task. I describe how the observed variability in the firing rate during a trial suggests that the type of attractor that is responsible for holding the stimulus information is not a fixed-point type attractor. I propose an alternate mechanism to a line attractor that allows the network to hold the value of an analog stimulus variable for the duration of the delay period, but rather than maintain a constant level of activity, the cells' firing rate varies throughout the delay period. I describe how my proposed mechanism offers a substantial advantage over a line attractor: The tuning requirements of cell to cell connections are greatly eased from that of a line attractor. To accommodate a change in the length of the delay period, I show that the network can be altered by changing a single parameter - the timing of an executive signal that originates outside of the network. To demonstrate the mechanism, as well as the tuning benefits, I use a well known model of propagation in neuronal networks.
Human position estimation in Intelligent Space for an active information display

In this paper, we introduce a new active information display system which is based on Intelligent Space and a projector mounted mobile robot. This system is able to afford a human with relevant information by projecting it on where the human is looking such as a wall, a floor, and a door. For this system, Intelligent Space should recognize location of human. To estimate position of human in Intelligent Space using multiple camera system, we propose a human detection method based on the Histogram of Oriented Gradients(HOG) feature descriptors and the boosting technique in each camera and a position estimation method using multiple view geometry. The performance of proposed method for human position is verified by real-time experiments.
Sparse Diffusion Steepest-Descent for One Bit Compressed Sensing in Wireless Sensor Networks

This letter proposes a sparse diffusion steepest-descent algorithm for one bit compressed sensing in wireless sensor networks. The approach exploits the diffusion strategy from distributed learning in the one bit compressed sensing framework. To estimate a common sparse vector cooperatively from only the sign of measurements, steepest-descent is used to minimize the suitable global and local convex cost functions. A diffusion strategy is suggested for distributive learning of the sparse vector. Simulation results show the effectiveness of the proposed distributed algorithm compared to the state-of-the-art non distributive algorithms in the one bit compressed sensing framework.
Fire pattern analysis and synthesis using EigenFires and motion transitions

We introduce novel approaches of intuitive and easy‐to‐use realistic fire animation, starting from real‐life fire by image‐based techniques and statistical analysis. The results can be utilized as a pre‐rendered sequence of images in video games, motion graphics, and cinematic visual effects. Instead of physics‐based simulation, we employ an example‐based principal component analysis and introduce “EigenFires. ” We visualize the main features of various fire samples to analyze their tracks and synthesize a new fire by combining various fire samples, recorded with high frame rates, in order to edit given sequences of fire animations. For this purpose, we present how to recognize similarity of the shapes of fire in order to change the pattern from one style of fire to another distinct style of fire procedurally. Our techniques require very little parameter tuning, compared with conventional physically based fire synthesis, video textures, and dynamic textures. A similar level of visually pleasing compressed fire is also easily produced by using principal component analysis techniques. Copyright © 2013 John Wiley & Sons, Ltd.
Towards automated design improvement through combinatorial optimisation

In this paper we present a novel approach to the problem of automated design improvement: by treating objectoriented design as a combinatorial optimisation of metrics we have developed a prototype software engineering tool capable of improving a design with respect to a conflicting set of goals. As the prioritisation of different goals is determined by weights associated with each metric, we also describe here a method of assigning coherent weights to a set of metrics based on object-oriented design heuristics. The combinatorial optimisation approach to automated design improvement is illustrated here by means of a simple case study, which shows the effect of applying our prototype tool to a small inheritance hierarchy. Results indicate that a balance between metrics has been achieved, as several potentially conflicting design goals are accommodated.
A Combined Structural-Algebraic Approach for the Regularization of Coupled Systems of DAEs

The automated modeling of multi-physical dynamical systems is usually realized by coupling different subsystems together via certain interface or coupling conditions. This approach results in large-scale high-index differential-algebraic equations (DAEs). Since the direct numerical simulation of these kind of systems leads to instabilities and possibly non-convergence of the numerical methods a regularization or remodeling of the system is required. In many simulation environments a kind of structural analysis based on the sparsity pattern of the system is used to determine the index and a reduced system model. However, this approach is not reliable for certain problem classes, in particular we show that it is not suited for coupled systems of DAEs. We will present a new approach for the regularization of coupled dynamical systems that combines the structural analysis, in particular the Signature Method [11], with classical algebraic regularization techniques and thus allows to handle so-called structurally singular systems and also enables a proper treatment of redundancies or inconsistencies in the system.
ADVISOR 2.1: a user-friendly advanced powertrain simulation using a combined backward/forward approach

ADVISOR 2.1 is the latest version of the National Renewable Energy Laboratory's advanced vehicle simulator. It was first developed in 1994 to support the US Department of Energy hybrid propulsion system program and is designed to be accurate, fast, flexible, easily sharable, and easy to use. This paper presents the model, focusing on its combination of forward- and backward-facing simulation approaches, and evaluates the model in terms of its design goals. ADVISOR predicts acceleration time to within 0.7% and energy use on the demanding US06 to within 0.6% for an underpowered series hybrid vehicle (0-100 km/h in 20 s). ADVISOR simulates vehicle performance on standard driving cycles between 2.6 and 8.0 times faster than a representative forward-facing vehicle model. Due in large part to ADVISOR's powerful graphical user interface and Web presence, over 800 users have downloaded ADVISOR from 45 different countries. Many of these users have contributed their own component data to the ADVISOR library.
Frog Oocytes to Unveil the Structure and Supramolecular Organization of Human Transport Proteins

Structural analyses of heterologously expressed mammalian membrane proteins remain a great challenge given that microgram to milligram amounts of correctly folded and highly purified proteins are required. Here, we present a novel method for the expression and affinity purification of recombinant mammalian and in particular human transport proteins in Xenopus laevis frog oocytes. The method was validated for four human and one murine transporter. Negative stain transmission electron microscopy (TEM) and single particle analysis (SPA) of two of these transporters, i.e., the potassium-chloride cotransporter 4 (KCC4) and the aquaporin-1 (AQP1) water channel, revealed the expected quaternary structures within homogeneous preparations, and thus correct protein folding and assembly. This is the first time a cation-chloride cotransporter (SLC12) family member is isolated, and its shape, dimensions, low-resolution structure and oligomeric state determined by TEM, i.e., by a direct method. Finally, we were able to grow 2D crystals of human AQP1. The ability of AQP1 to crystallize was a strong indicator for the structural integrity of the purified recombinant protein. This approach will open the way for the structure determination of many human membrane transporters taking full advantage of the Xenopus laevis oocyte expression system that generally yields robust functional expression.
Notice of RetractionOne-lane traffic flow simulation model hybrid from built-in beam imaginary slope and cellular automaton

Traditionally, continuous car-following modeling method and cellular automaton method are the two most typical traffic modeling methods. Both of them have their limitations. The former has difficulties in parameter calibration and is short off in randomization, while the later can't reveal the basic continuance characteristic of vehicle speeds. And continuous vehicle speed is the basis of experimental verification. To this end, a method hybrid from imaginary slope algorithm based on built-in beam deflection curve and cellular automaton method is proposed. It supposes that every vehicle has its undulating target speed, the staying values of which vary according to the rules similar to that of a cellular automaton. In this way a simulation model for running vehicles is obtained, which has continuance and randomization uniformly and obeys the restriction conditions for the vehicle displacements, the vehicle speeds and the vehicle accelerations.
Low Complexity GFDM Receiver Based on Sparse Frequency Domain Processing

Generalized frequency division multiplexing (GFDM) is a multi-carrier modulation scheme. In contrast to the traditional orthogonal frequency division multiplexing (OFDM), it can benefit from transmitting multiple symbols per sub-carrier. GFDM targets block based transmission which is enabled by circular pulse shaping of the individual sub- carriers. In this paper we propose a low complexity design for demodulating GFDM signals based on a sparse representation of the pulse-shaping filter in frequency domain. The proposed scheme is compared to receiver concepts from previous work and the performance is assessed in terms of bit error rates for AWGN and Rayleigh multipath fading channels. The results show, that for high-order QAM signaling, the error performance can be significantly improved with interference cancellation at reasonable computational cost.
Supermanifold Forms and Integration. A Dual Theory

We investigate forms on supermanifolds defined as Lagrangians of ``copaths'' (that is, systems of equations, which may or may not specify submanifolds). For this, we consider direct products $M^{n|m}\times\Bbb R^{r|s}$ and study isomorphisms corresponding to simultaneously advancing the number of additional parameters $r|s$ and the number of equations. We define an exteriour differential in terms of variational derivatives w.r. t. a copath and establish its main properties. In the resulting stable picture we obtain infinite complexes $\D:\Om{r}{s}\to\Om{r+1}{s}$ for $M^{n|m}$, where $0 \le s \le m$ and $r$ can be any integer. For $r\ge 0$ a canonical isomorphism with forms constructed as Lagrangians of $r|s$-paths is established. We discover the ``lacking half'' of forms on supermanifolds: $r|s$-forms with $r<0$, previously unknown except for $s=m$. (They have been partly replaced earlier by an augmentation of the ``non-negative'' part of the complexes.) All these results are new. The study of these questions is in progress now.
A Rapid Image Approximation for Keypatches Building

A new type of local features with visual semantics, keypatches, was proposed to improve performances of image retrieval methods [1]. These features are obtained by approximating the content of scanning windows using the predefined geometrical patterns. To build keypatches quickly, this paper proposes a new approximation based on a novel Compact Gradient Mapping Table (CGMT). The method includes two stages. First, the approximation candidates can be obtained quickly by constructing and looking up the Compact Gradient Mapping Table based on the distribution sharing of the contours characteristics of the patterns. Then, Hough transform needs to be applied only in a very small parameter space defined by the candidates for the final approximation. To demonstrate the effectiveness of the proposed method, we have approximated 1628 image patches from 100 images. Results show the proposed method significantly outperforms the previous Hough transform-based approximation in speed with a very small accuracy loss.
Poly-logarithmic Approximation for Maximum Node Disjoint Paths with Constant Congestion

We consider the Maximum Node Disjoint Paths (MNDP) problem in undirected graphs. The input consists of an undirected graph G = (V, E) and a collection {(s1, t1),. , (sk, tk)} of k source-sink pairs. The goal is to select a maximum cardinality subset of pairs that can be routed/connected via node-disjoint paths. A relaxed version of MNDP allows up to c paths to use a node, where c is the congestion parameter. We give a polynomial time algorithm that routes Ω(OPT/poly log k) pairs with O(1) congestion, where OPT is the value of an optimum fractional solution to a natural multicommodity flow relaxation. Our result builds on the recent breakthrough of Chuzhoy [17] who gave the first poly-logarithmic approximation with constant congestion for the Maximum Edge Disjoint Paths (MEDP) problem.
Near-Optimal Ambiguity Sets for Distributionally Robust Optimization

We propose a novel, Bayesian framework for assessing the relative strengths of data-driven ambiguity sets in distributionally robust optimization (DRO). The key idea is to measure the relative size between a candidate ambiguity set and an asymptotically optimal set as the amount of data grows large. This asymptotically optimal set is provably the smallest convex ambiguity set that satisfies a specific, Bayesian robustness guarantee, i.e., it is a subset of any other convex set that also satisfies this guarantee. Perhaps surprisingly, we show what existing, popular ambiguity set proposals that are based on statistical confidence regions are necessarily significantly larger than this asymptotically optimal set; the ratio of their sizes scales with the square root of the dimension of the ambiguity. These results suggest that current DRO models utilizing these popular ambiguity sets are unnecessarily conservative. Consequently, we also propose a new class of ambiguity sets which satisfy our Bayesian robustness guarantee, are tractable, enjoy the usual asymptotic convergence properties, and, most importantly, are only a small, explicitly known factor larger than the asymptotically optimal set. We discuss extensively how these results give rise to simple guidelines for practitioners with respect to selecting ambiguity sets and formulating DRO models, with special emphasis on the case of ambiguity sets for finite, discrete probability vectors. Computational evidence in portfolio allocation using real and simulated data confirm that these theoretical framework and results provide useful, practical insight into the empirical performance of DRO models in real applications, and that our new near-optimal sets outperform their traditional confidence region variants.
Assessing Mathematics Proficiency of Multilingual Students: The Case for Translanguaging in the Democratic Republic of the Congo

Monolingual content assessments of multilingual students remarkably fail their most essential endeavor—to provide meaningful information about their content proficiency. Multilingual students take years to catch up with their monolingual peers in terms of language proficiency, and it is inappropriate to assess them using a language that they do not fully understand. Because multilingual students do not behave as multiple monolinguals, translated tests are not a satisfactory solution. Test developers should produce assessments that enable multilingual students to use their entire linguistic repertoires and engage in their natural linguistic practices. This study examines a translingual administration of a mathematics assessment in the Democratic Republic of the Congo. The findings show that a translingual administration is more appropriate than a traditional administration. The findings, however, raise doubts about standardization and call for further deliberation on how to properly assess mathematics in similar contexts.
Effect of different environmental conditions on growth and production of mustard (brassica juncea) in semi-arid zone of Rajasthan

Light intensity, temperature, relative humidity, soil moisture content and various other local environmental factors directly influence crop growth and subsequently the production of crops. Light intensity directly manipulates the water content of plants, temperature and soil moisture content as well. Local environmental temperature controls germination rate at one side and play crucial role in pod formation and yield of crop on other side. An experiment was carried out to investigate the alterations in local environmental factors and its impact on growth and productivity of Mustard crop (Brassica juncea) grown under low light intensities at research farm of Bhagwant University, Ajmer (Rajasthan) during rabi (winter) seasons of 2017-18 and 2018-19. The observations reveal that the low light intensity directly reduced the temperature inside the shade net house. The relative humidity increased under low light environment. Soil moisture content was higher inside the low light intensity as compared with open area. The germination was faster in low light environment. Plant height, Leaf area and other growth traits was improved under low light intensity as compared to 100% light intensity of open area. Chlorophyll a and chlorophyll b were also altered under low light environment, which clearly exposed the effect of various local environmental factors at leaf level. Marginal impacts on seed yield and pod size were observed under low light conditions. This study may help to understand and select suitable environmental conditions under semi-arid zone of India.
Turbo-like Iterative Thresholding for SAR image recovery from compressed measurements

Compressive sensing (CS) has attracted many researchers since it offers a novel paradigm that one can acquire sparse signals at a sub-Nyquist rate without information losses. In [1][2][3], the authors have presented some schemes for CS application on remote sensing imaging, some of which are related to SAR. CS remote sensing imaging includes two steps: on-board encoding imaging and off-line decoding recovery. Based on the on-board encoding imaging scheme proposed in [1], this paper focuses on the off-line decoding recovery algorithm. We proposed a Turbolike Iterative Residual Thresholding algorithm (RTIT) to decode the compressed SAR data with approximately sparse property. The experimental results show that it outperforms the state-of-the-art Iterative Thresholding algorithm (IT).
An Algebraic Framework for Solving Proportional and Predictive Analogies

We present an approach to analogical reasoning which is inherently dependent on abstraction. While typical cognitive and AI models of analogy perform a direct mapping from objects of the base to objects of the target domain, our model performs mapping via abstraction. Abstraction is calculated as most specific generalization of the base and the target structure. In contrast to existing models, learning occurs as a side-effect of analogical reasoning. Our approach is based on the formally sound framework of anti-unification. It allows to deal with different kinds of analogy in a uniform way. After a description of the basic ideas of the approach, we will present examples from the domains of proportional and predictive analogy.