Miracle-dz/UniFAR-FTUs huggingface dataset:
A low cost power quality management tool for different load types

Any deviation of voltage or current from the ideal sinusoidal waveform is a power quality (PQ) disturbance. The economic loss caused by destruction of equipment due to poor quality i.e. leading or lagging VARs is significant and must be avoided. This paper shows the different kind of disturbances associated with linear and nonlinear loads. A low cost power quality monitoring system is developed by designing Virtual Instruments (VIs) using LabVIEW™ as a platform. With LabVIEW™ as development platform, real signals from chroma programming are used in LabVIEW algorithm by Data Acquisition (DAQ) card to acquire and digitize the input line signal to obtain the results. The results are obtained by simulating the VIs over different load conditions such as linear and nonlinear load. Further this paper also provide an approach which leads to fundamental idea regarding the reactive power control.
ATSC DTV Receiver Implementation

Receivers of broadcast digital television (DTV) service operate in an often difficult environment of electrical interference, multiple other TV signals in close frequency proximity, multipath, wide dynamic range input signals, and uncertain antenna choice and installation. Receivers must demodulate and decode the signal and optimize its processing for different display technologies-a process that can include format conversion between progressive and interlaced scanning and different screen pixel counts. Data that supports the new services enabled by digital transmission must be decoded and processed. Receiver designers must implement the required functions cost-effectively even as they strive to provide performance and feature differentiation from their competitors' products. This paper describes all of the DTV receiver functions and references the associated standards. Emphasis is given to the difficult areas of signal reception and demodulation and to sections of the receiver that enable attractive and recognizable consumer features.
Inter-dinucleotide distances in the human genome: an analysis of the whole-genome and protein-coding distributions

Summary We study the inter-dinucleotide distance distributions in the human genome, both in the whole-genome and protein-coding regions. The inter-dinucleotide distance is defined as the distance to the next occurrence of the same dinucleotide. We consider the 16 sequences of inter-dinucleotide distances and two reading frames. Our results show a period-3 oscillation in the protein-coding inter-dinucleotide distance distributions that is absent from the whole-genome distributions. We also compare the distance distribution of each dinucleotide to a reference distribution, that of a random sequence generated with the same dinucleotide abundances, revealing the CG dinucleotide as the one with the highest cumulative relative error for the first 60 distances. Moreover, the distance distribution of each dinucleotide is compared to the distance distribution of all other dinucleotides using the Kullback-Leibler divergence. We find that the distance distribution of a dinucleotide and that of its reversed complement are very similar, hence, the divergence between them is very small. This is an interesting finding that may give evidence of a stronger parity rule than Chargaff’s second parity rule.
Translating Phrases in Neural Machine Translation

Phrases play an important role in natural language understanding and machine translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is difficult to integrate them into current neural machine translation (NMT) which reads and generates sentences word by word. In this work, we propose a method to translate phrases in NMT by integrating a phrase memory storing target phrases from a phrase-based statistical machine translation (SMT) system into the encoder-decoder architecture of NMT. At each decoding step, the phrase memory is first re-written by the SMT model, which dynamically generates relevant target phrases with contextual information provided by the NMT model. Then the proposed model reads the phrase memory to make probability estimations for all phrases in the phrase memory. If phrase generation is carried on, the NMT decoder selects an appropriate phrase from the memory to perform phrase translation and updates its decoding state by consuming the words in the selected phrase. Otherwise, the NMT decoder generates a word from the vocabulary as the general NMT decoder does. Experiment results on the Chinese to English translation show that the proposed model achieves significant improvements over the baseline on various test sets.
Rigid thinking about deformables: do children sometimes overgeneralize the shape bias?

ABSTRACT Young children learning English are biased to attend to the shape of solid rigid objects when learning novel names. This study seeks further understanding of the processes that support this behavior by examining a previous finding that three-year-old children are also biased to generalize novel names for objects made from deformable materials by shape, even after the materials are made salient. In two experiments, we examined the noun generalizations of 72 two-, three- and four-year-old children with rigid and deformable stimuli. Data reveal that three-year-old, but not two- or four-year-old, children generalize names for deformable things by shape, and that this behavior is not due to the syntactic context of the task. We suggest this behavior is an overgeneralization of three-year-old children's knowledge of how rigid things are named and discuss the implications of this finding for a developmental account of the origins of the shape bias.
Detection of objects in moving images and implementation of the purification algorithm on analog CNN and DSP processors

CNN Universal Machines that contain two different processors working interactively with each other, have an important impact for image processing applications with their advanced computing features. These processors are named as ACE16k processor which is the hardware implementation of cellular neural networks and Digital Signal Processor (DSP). Bi-i Cellular Vision System is a CNN Universal Machine that also has these characteristics. In this study, certain objects in moving images are detected and their features are extracted. By using these features, a purification algorithm is implemented on the Bi-i Cellular Vision System. This algorithm is implemented with two different applications. In the first application, the algorithm is implemented only on a Digital Signal Processor. In the second one, it is run in coordination with both an ACE16k processor and a Digital Signal Processor. These applications are evaluated by comparing the obtained results in terms of run-time.
Differential effects of Smad3 targeting in a murine model of chronic kidney disease

Transforming growth factor (TGF)‐β1 has a pivotal role in the pathogenesis of progressive kidney diseases that are characterized by fibrosis. The main intracellular signaling pathway of TGF‐β1 is the Smad system, where Smad2 and Smad3 play a central role in transcriptional regulation of target genes involved in extracellular matrix (ECM) metabolism. This study analyzes the hypothesis that blockade of Smad3 attenuates the development of TGF‐β1‐driven renal fibrosis. This was examined in vivo in a transgenic model of TGF‐β1‐induced chronic kidney disease with Smad3 or without Smad3 expression and in vitro in mesangial cells and glomerular endothelial cells with Smad2/3 inhibitors or Smad3‐knockdown. Electron microscopy was used for evaluation of morphological changes, real‐time polymerase chain reaction for detection of RNA expression, and immunohistochemistry for localization of ECM components. Matrix metalloproteinase (MMP) level was assessed by gelatin zymography electrophoresis and located by in situ zymography. The results show TGF‐β1‐induced mesangial matrix expansion, tubulointerstitial fibrosis, and tubular basement membrane thickening that are attenuated by Smad3 deletion, whereas TGF‐β1‐induced glomerular basement membrane thickening is not shown. The amount and distribution profile of MMP‐2 may suggest a role of the enzyme herein. We conclude that Smad3 targeting is not exclusively beneficial as Smad3 has diverse transcriptional regulatory effects in different cell types in the kidney.
Review of the Fisheries Indicators for Monitoring the Impacts of Fishing on Fish Communities

ABSTRACT The high demand for fish products has led to important economic issues including the emergence of new markets, especially in developing countries where fishing represents an important economic sector and the main source of animal protein for local communities. The worldwide fisheries have overall passed their peak and many stocks are overfished despite the increasing efforts towards the developpement of fisheries assessment and management tools using global and analytical models. These complex models used for fisheries management, especially in many developing countries, require large amounts of data whose collection and analysis are very costly and expensive. The current studies of the effects of overexploitation on fish communities use an indicator-based approach as an alternative to improve the assessment and management of the fisheries resources. This latter approach is cheaper, simpler and more efficient to establish a link between disruption and its effect on a biotic component. The currently most commonly used indicators have provided successfully results in many studies cases carried out on fish communities. Thus, management approaches based on indicators in the recent years has improved our understanding of the structure and functioning of ecosystems, and therefore, facilitated the interpretation of biological phenomena.
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction

We present the Mind the Gap Model (MGM), an approach for interpretable feature extraction and selection. By placing interpretability criteria directly into the model, we allow for the model to both optimize parameters related to interpretability and to directly report a global set of distinguishable dimensions to assist with further data exploration and hypothesis generation. MGM extracts distinguishing features on real-world datasets of animal features, recipes ingredients, and disease co-occurrence. It also maintains or improves performance when compared to related approaches. We perform a user study with domain experts to show the MGM's ability to help with dataset exploration.
Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities

We introduce a solid harmonic wavelet scattering representation, invariant to rigid motion and stable to deformations, for regression and classification of 2D and 3D signals. Solid harmonic wavelets are computed by multiplying solid harmonic functions with Gaussian windows dilated at different scales. Invariant scattering coefficients are obtained by cascading such wavelet transforms with the complex modulus nonlinearity. We study an application of solid harmonic scattering invariants to the estimation of quantum molecular energies, which are also invariant to rigid motion and stable with respect to deformations. A multilinear regression over scattering invariants provides close to state of the art results over small and large databases of organic molecules.
Super-resolution based on improved sparse coding

A sparse dictionary model for image superresolution is presented, which unifies the feature patches of high-resolution (HR) and low-resolution images using sparse dictionary coding. This method builds a sparse association between middle-frequency and high-frequency image components and realizes simultaneously match searching and optimization methods. Comparison with sparse coding method shows sparse dictionary is more compact and effective. Sparse K-SVD algorithm is applied for optimization to speed up sparse coding. Some experiments with real images show that our method outperforms other learning-based super-resolution algorithms.
A support vector machine-based method for LPV-ARX identification with noisy scheduling parameters

In this paper, we present a method that utilizes support vector machines (SVM) to identify linear parameter-varying (LPV) auto-regressive exogenous input (ARX) models corrupted by not only noise, but also uncertainties in the LPV scheduling variables. The proposed method employs SVM and takes advantage of the so-called “kernel trick” to allow for the identification of the LPV-ARX model structure solely based on the input-output data. The objective function, as defined in this paper, allows to consider uncertainties related to the LPV scheduling parameters, and hence results in a new formulation that provides a more accurate estimation of the LPV model in the presence of scheduling uncertainties. We further demonstrate the viability of the proposed LPV identification method through numerical examples, where we show that higher best fit rate (BFR) can be achieved under realistic noise conditions using the proposed method compared to the method initially proposed in [6].
Similarity, retrieval, and classification of motion capture data

Three-dimensional motion capture data is a digital representation of the complex spatiotemporal structure of human motion. Mocap data is widely used for the synthesis of realistic computer-generated characters in data-driven computer animation and also plays an important role in motion analysis tasks such as activity recognition. Both for efficiency and cost reasons, methods for the reuse of large collections of motion clips are gaining in importance in the field of computer animation. Here, an active field of research is the application of morphing and blending techniques for the creation of new, realistic motions from prerecorded motion clips. This requires the identification and extraction of logically related motions scattered within some data set. Such content-based retrieval of motion capture data, which is a central topic of this thesis, constitutes a difficult problem due to possible spatio-temporal deformations between logically related motions. Recent approaches to motion retrieval apply techniques such as dynamic time warping, which, however, are not applicable to large data sets due to their quadratic space and time complexity. In our approach, we introduce various kinds of relational features describing boolean geometric relations between specified body points and show how these features induce a temporal segmentation of motion capture data streams. By incorporating spatio-temporal invariance into the relational features and induced segments, we are able to adopt indexing methods allowing for flexible and efficient content-based retrieval in large motion capture databases. As a further application of relational motion features, a new method for fully automatic motion classification and retrieval is presented. We introduce the concept of motion templates (MTs), by which the spatio-temporal characteristics of an entire motion class can be learned from training data, yielding an explicit, compact matrix representation. The resulting class MT has a direct, semantic interpretation, and it can be manually edited, mixed, combined with other MTs, extended, and restricted. Furthermore, a class MT exhibits the characteristic as well as the variational aspects of the underlying motion class at a semantically high level. Classification is then performed by comparing a set of precomputed class MTs with unknown motion data and labeling matching portions with the respective motion class label. Here, the crucial point is that the variational (hence uncharacteristic) motion aspects encoded in the class MT are automatically masked out in the comparison, which can be thought of as locally adaptive feature selection.
Automatic Image‐Based Water Stage Measurement for Long‐Term Observations in Ungauged Catchments

Small‐scale and headwater catchments are mostly ungauged, even though their observation could help to improve the understanding of hydrological processes. However, it is expensive to build and maintain conventional measurement networks. Thus, the heterogeneous characteristics and behavior of catchments are currently not fully observed. This study introduces a method to capture water stage with a flexible low‐cost camera setup. By considering the temporal signature of the water surface, water lines are automatically retrieved via image processing. The image coordinates are projected into object space to estimate the actual water stage. This requires high‐resolution 3D models of the river bed and bank area, which are calculated in a local coordinate system with structure from motion, employing terrestrial as well as unmanned aerial vehicle imagery. A medium‐ and a small‐scale catchment are investigated to assess the accuracy and reliability of the introduced method. Results reveal that the average deviation between the water stages measured with the camera gauge and a reference gauge are below 6 mm in the medium‐scale catchment. Trends of water stage changes are captured reliably in both catchments. The developed approach uses a low‐cost camera design in combination with image‐based water level measurements and high‐resolution topography from structure from motion. In future, adding tracking algorithms can help to densify existing gauging networks.
Supervised speech dereverberation in noisy environments using exemplar-based sparse representations

Exemplar-based techniques, where the noisy speech is decomposed as a linear combination of the speech and noise exemplars stored in a dictionary, have been successfully used for speech enhancement in noisy environments. This paper extends this technique to achieve speech dereverberation in noisy environments by means of a nonnegative approximation of the noisy reverberant speech in the frequency domain. A novel approach for estimating the room impulse response (RIR) together with the speech and noise estimates using a non-negative matrix deconvolution (NMD)-based technique is proposed. In addition, we extend an existing technique based on nonnegative matrix factorisation (NMF) that performs speech derever-beration in noise-free environments to noisy scenarios. New estimators for jointly obtaining the RIR and exemplar weights for the NMD and NMF-based formulations are presented. The proposed techniques are evaluated on the noise-free and noisy reverberant speech in the CHiME-2 WSJ0 database and are shown to yield better speech enhancement in terms of signal-to-distortion ratio (SDR), perceptual evaluation of speech quality (PESQ) and cepstral distance (CD) measures.
Maximum matching in graphs with an excluded minor

We present a new randomized algorithm for finding a maximum matching in <i>H-minor free</i> graphs. For every fixed <i>H</i>, our algorithm runs in <i>O</i>(<i>n</i><sup>3ω/(ω+3)</sup>) < <i>O</i>(<i>n</i><sup>1.326</sup>) time, where <i>n</i> is the number of vertices of the input graph and ω < 2.376 is the exponent of matrix multiplication. This improves upon the previous <i>O</i>(<i>n</i><sup>1.5</sup>) time bound obtained by applying the <i>O</i>(<i>mn</i><sup>1/2</sup>)-time algorithm of Micali and Vazirani on this important class of graphs. For graphs with <i>bounded genus</i>, which are special cases of <i>H</i>-minor free graphs, we present a randomized algorithm for finding a maximum matching in <i>O</i>(<i>n</i><sup>ω/2</sup>) < <i>O</i>(<i>n</i><sup>1.19</sup>) time. This extends a previous randomized algorithm of Mucha and Sankowski, having the same running time, that finds a maximum matching in a <i>planar</i> graphs. We also present a deterministic algorithm with a running time of <i>O</i>(<i>n</i><sup>1+ω/2</sup>) < <i>O</i>(<i>n</i><sup>2.19</sup>) for <i>counting</i> the number of <i>perfect</i> matchings in graphs with bounded genus. This algorithm combines the techniques used by the algorithms above with the counting technique of Kasteleyn. Using this algorithm we can also count, within the same running time, the number of <i>T-joins</i> in planar graphs. As special cases, we get algorithms for counting Eulerian subgraphs (<i>T</i> = &phis;) and odd subgraphs (<i>T = V</i>) of planar graphs.
Connecting Look and Feel: Associating the Visual and Tactile Properties of Physical Materials

For machines to interact with the physical world, they must understand the physical properties of objects and materials they encounter. We use fabrics as an example of a deformable material with a rich set of mechanical properties. A thin flexible fabric, when draped, tends to look different from a heavy stiff fabric. It also feels different when touched. Using a collection of 118 fabric samples, we captured color and depth images of draped fabrics along with tactile data from a high-resolution touch sensor. We then sought to associate the information from vision and touch by jointly training CNNs across the three modalities. Through the CNN, each input, regardless of the modality, generates an embedding vector that records the fabrics physical property. By comparing the embedding vectors, our system is able to look at a fabric image and predict how it will feel, and vice versa. We also show that a system jointly trained on vision and touch data can outperform a similar system trained only on visual data when tested purely with visual inputs.
Monocular depth cues in computer vision applications

In the computer vision field, if image depth information were available, many tasks could be posed from a different perspective for the sake of higher performance and robustness. In our thesis, we have demonstrated how coarse depth information can be integrated in different tasks following alternative strategies to obtain more precise and robust results in three computer vision applications: camera rotation parameters estimation, background estimation and pedestrian candidate generation.
Tourist Attraction Recommendation Service: An Approach, Architecture and Case Study

: The paper proposes a complex multi-model approach to recommendation systems design in the domain of tourist information support. Specifically, it proposes to construct a recommendation system as a composition of loosely coupled modules, implementing both personalized and non-personalized methods of recommendations and a coordination module responsible for adaptation of the whole system to the specific tourist and situation context.
Optimizing unique shortest paths for resilient routing and fast reroute in IP-based networks

Intradomain routing in IP networks follows shortest paths according to administrative link costs. When several equal-cost shortest paths exist, routers that use equal-cost multipath (ECMP) distribute the traffic over all of them. To produce single-shortest path (SSP) routing, a selection mechanism (tie-breaker) chooses just one of the equal-cost paths. Tie-breakers are poorly standardized and use information that may change over time, which makes SSP routing unpredictable. Therefore, link costs producing unique shortest paths (USP) are preferred.