nils-herrmann/clean_scirepeval huggingface dataset:
An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation Recommendation

This paper studies Cumulative Citation Recommendation (CCR) for Knowledge Base Acceleration (KBA). The CCR task aims to detect potential citations of a set of target entities with priorities from a volume of temporally-ordered stream corpus. Previous approaches for CCR that build an individual relevance model for each entity fail to handle unseen entities without annotation. A baseline solution is to build a global entity-unspecific model for all entities regardless of the relationship information among entities, which cannot guarantee to achieve satisfactory result for each entity. In this paper, we propose a novel entity class-dependent discriminative mixture model by introducing a latent entity class layer to model the correlations between entities and latent entity classes. The model can better adjust to different types of entities and achieve better performance when dealing with a broad range of entities. An extensive set of experiments has been conducted on TREC-KBA-2013 dataset, and the experimental results demonstrate that the proposed model can achieve the state-of-the-art performance.
Parallel Multilevel k-way Partitioning Scheme for Irregular Graphs

In this paper we present a parallel formulation of a multilevel k-way graph partitioning algorithm. The multilevel k-way partitioning algorithm reduces the size of the graph by collapsing vertices and edges (coarsening phase), finds a k-way partition of the smaller graph, and then it constructs a k-way partition for the original graph by projecting and refining the partition to successively finer graphs (uncoarsening phase). A key innovative feature of our parallel formulation is that it utilizes graph coloring to effectively parallelize both the coarsenin and the refinement during the uncoarsening phase. Our algorithm is able to achieve a high degree of concurrency, while maintaining the high quality partitions produced by the serial algorithm. We test our scheme on a large number of graphs from finite element methods, and transportation domains. Our parallel formulation on Cray T3D, produces high quality 128-way partitions on 128 processors in a little over two seconds, for graphs with a million vertices. Thus our parallel algorithm makes it possible to perform dynamic graph partition in adaptive computations without compromising quality.
The variational approximation for Bayesian inference

The influence of this Thomas Bayes' work was immense. It was from here that "Bayesian" ideas first spread through the mathematical world, as Bayes's own article was ignored until 1780 and played no important role in scientific debate until the 20th century. It was also this article of Laplace's that introduced the mathematical techniques for the asymptotic analysis of posterior distributions that are still employed today. And it was here that the earliest example of optimum estimation can be found, the derivation and characterization of an estimator that minimized a particular measure of posterior expected loss. After more than two centuries, we mathematicians, statisticians cannot only recognize our roots in this masterpiece of our science, we can still learn from it.
Achievable cases in an asynchronous environment

The paper deals with achievability of fault tolerant goals in a completely asynchronous distributed system. Fischer, Lynch, and Paterson [FLP] proved that in such a system "nontrivial agreement" cannot be achieved even in the (possible) presence of a single "benign" fault. In contrast, we exhibit two pairs of goals that are achievable even in the presence of up to t ≪ n/2 faulty processors, contradicting the widely held assumption that no nontrivial goals are attainable in such a system. The first pair deals with renaming processors so as to reduce the size of the initial name space. When only uniqueness is required of the new names, we present a lower bound of n + 1 on the size of the new name space, and a renaming algorithm which establishes an upper bound of n + t. In case the new names are required also to preserve the original order, a tight bound of 2t(n- t + 1) - 1 is obtained. The second pair of goals deals with the multi-slot critical section problem. We present algorithms for controlled access to a critical section. As for the number of slots required, a tight bound of t + 1 is proved in case the slots are identical. In the case of distinct slots the upper bound is 2t + 1.
Task-Aware Image Downscaling

Image downscaling is one of the most classical problems in computer vision that aims to preserve the visual appearance of the original image when it is resized to a smaller scale. Upscaling a small image back to its original size is a difficult and ill-posed problem due to information loss that arises in the downscaling process. In this paper, we present a novel technique called task-aware image downscaling to support an upscaling task. We propose an auto-encoder-based framework that enables joint learning of the downscaling network and the upscaling network to maximize the restoration performance. Our framework is efficient, and it can be generalized to handle an arbitrary image resizing operation. Experimental results show that our task-aware downscaled images greatly improve the performance of the existing state-of-the-art super-resolution methods. In addition, realistic images can be recovered by recursively applying our scaling model up to an extreme scaling factor of x128. We also validate our model’s generalization capability by applying it to the task of image colorization.
MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform.

A multiple sequence alignment program, MAFFT, has been developed. The CPU time is drastically reduced as compared with existing methods. MAFFT includes two novel techniques. (i) Homo logous regions are rapidly identified by the fast Fourier transform (FFT), in which an amino acid sequence is converted to a sequence composed of volume and polarity values of each amino acid residue. (ii) We propose a simplified scoring system that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. Two different heuristics, the progressive method (FFT-NS-2) and the iterative refinement method (FFT-NS-i), are implemented in MAFFT. The performances of FFT-NS-2 and FFT-NS-i were compared with other methods by computer simulations and benchmark tests; the CPU time of FFT-NS-2 is drastically reduced as compared with CLUSTALW with comparable accuracy. FFT-NS-i is over 100 times faster than T-COFFEE, when the number of input sequences exceeds 60, without sacrificing the accuracy.
DFuzzy: a deep learning-based fuzzy clustering model for large graphs

Graph clustering is successfully applied in various applications for finding similar patterns. Recently, deep learning- based autoencoder has been used efficiently for detecting disjoint clusters. However, in real-world graphs, vertices may belong to multiple clusters. Thus, it is obligatory to analyze the membership of vertices toward clusters. Furthermore, existing approaches are centralized and are inefficient in handling large graphs. In this paper, a deep learning-based model ‘DFuzzy’ is proposed for finding fuzzy clusters from large graphs in distributed environment. It performs clustering in three phases. In first phase, pre-training is performed by initializing the candidate cluster centers. Then, fine tuning is performed to learn the latent representations by mining the local information and capturing the structure using PageRank. Further, modularity is used to redefine clusters. In last phase, reconstruction error is minimized and final cluster centers are updated. Experiments are performed over real-life graph data, and the performance of DFuzzy is compared with four state-of-the-art clustering algorithms. Results show that DFuzzy scales up linearly to handle large graphs and produces better quality of clusters when compared to state-of-the-art clustering algorithms. It is also observed that deep structures can help in getting better graph representations and provide improved clustering performance.
Driver fatigue detection based on eye tracking and dynamk, template matching

A vision-based real-time driver fatigue detection system is proposed for driving safely. The driver's face is located, from color images captured in a car, by using the characteristic of skin colors. Then, edge detection is used to locate the regions of eyes. In addition to being used as the dynamic templates for eye tracking in the next frame, the obtained eyes' images are also used for fatigue detection in order to generate some warning alarms for driving safety. The system is tested on a Pentium III 550 CPU with 128 MB RAM. The experiment results seem quite encouraging andpromising. The system can reach 20 frames per second for eye tracking, and the average correct rate for eye location and tracking can achieve 99.1% on four test videos. The correct rate for fatigue detection is l00%, but the average precision rate is 88.9% on the test videos.
Words in the brain's language.

If the cortex is an associative memory, strongly connected cell assemblies will form when neurons in different cortical areas are frequently active at the same time. The cortical distributions of these assemblies must be a consequence of where in the cortex correlated neuronal activity occurred during learning. An assembly can be considered a functional unit exhibiting activity states such as full activation ("ignition") after appropriate sensory stimulation (possibly related to perception) and continuous reverberation of excitation within the assembly (a putative memory process). This has implications for cortical topographies and activity dynamics of cell assemblies forming during language acquisition, in particular for those representing words. Cortical topographies of assemblies should be related to aspects of the meaning of the words they represent, and physiological signs of cell assembly ignition should be followed by possible indicators of reverberation. The following postulates are discussed in detail: (1) assemblies representing phonological word forms are strongly lateralized and distributed over perisylvian cortices; (2) assemblies representing highly abstract words such as grammatical function words are also strongly lateralized and restricted to these perisylvian regions; (3) assemblies representing concrete content words include additional neurons in both hemispheres; (4) assemblies representing words referring to visual stimuli include neurons in visual cortices; and (5) assemblies representing words referring to actions include neurons in motor cortices. Two main sources of evidence are used to evaluate these proposals: (a) imaging studies focusing on localizing word processing in the brain, based on stimulus-triggered event-related potentials (ERPs), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI), and (b) studies of the temporal dynamics of fast activity changes in the brain, as revealed by high-frequency responses recorded in the electroencephalogram (EEG) and magnetoencephalogram (MEG). These data provide evidence for processing differences between words and matched meaningless pseudowords, and between word classes, such as concrete content and abstract function words, and words evoking visual or motor associations. There is evidence for early word class-specific spreading of neuronal activity and for equally specific high-frequency responses occurring later. These results support a neurobiological model of language in the Hebbian tradition. Competing large-scale neuronal theories of language are discussed in light of the data summarized. Neurobiological perspectives on the problem of serial order of words in syntactic strings are considered in closing.
Nanopower CMOS Relaxation Oscillators With Sub-100  $\hbox{ppm}/^{\circ}\hbox{C}$ Temperature Coefficient

A low temperature coefficient (TC) current reference and a curvature current source are realized by transistors with different gate-oxide thicknesses. These two current sources are used to realize oscillators with low TCs. These two oscillators of 1.4 MHz and 28 kHz, respectively, are fabricated in the 0.18-μm CMOS process, and their areas are 0.072 and 0.16 mm2, respectively. For the 1.4-MHz oscillator, its power is 615 nW with a supply voltage of 1.2 V. The measured average TC is 56.4 ppm/°C for a temperature of -20 °C to 80 °C. The calculated figures of merit (FOM1 and FOM2) are -131 and 103 dB, respectively. For the 28-kHz oscillator, its power is 40.2 nW with a supply voltage of 1.2 V. The measured average TC is 95.5 ppm/°C for a temperature of -20 °C to 80 °C. The calculated FOM2 is 92 dB.
Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures

This paper introduces a novel approach for generating videos called Synchronized Deep Recurrent Attentive Writer (Sync-DRAW). Sync-DRAW can also perform text-to-video generation which, to the best of our knowledge, makes it the first approach of its kind. It combines a Variational Autoencoder(VAE) with a Recurrent Attention Mechanism in a novel manner to create a temporally dependent sequence of frames that are gradually formed over time. The recurrent attention mechanism in Sync-DRAW attends to each individual frame of the video in sychronization, while the VAE learns a latent distribution for the entire video at the global level. Our experiments with Bouncing MNIST, KTH and UCF-101 suggest that Sync-DRAW is efficient in learning the spatial and temporal information of the videos and generates frames with high structural integrity, and can generate videos from simple captions on these datasets.
DESIGN AND SIMULATIONS OF IDC SENSOR USING COMSOL MULTYPHYSICS AND DIELECTRIC SPECTROSCOPY OF LTCC MATERIALS

In this paper are shown relative permittivity εr and loss tangent tan δ, of LTCC ceramics manufactured by Heraeus Circuit Materials Division, which are tested for frequencies at range 10 MHz to 1 GHz at room temperature. The measurements system for dielectric spectroscopy and characterization of LTCC materials were conducted on the impedance analyzer Agilent 4191. Obtained results due dielectric characterization were used in 3D modeling and simulations of IDC sensor for environmental monitoring. Index Terms — Dielectric Properties; Dielectric spectroscopy; LTCC; Sensors; IDC; Permittivity; COMSOL; Simulations; Buletinul AGIR nr. 3/2013 ● iulie-septembrie _____________________________________________________________________________________ 63 DESIGN AND SIMULATIONS OF IDC SENSOR USING COMSOL MULTIPHYSICS AND DIELECTRIC SPECTROSCOPY OF LTCC MATERIALS Buletinul AGIR nr. 4/2012 ● octombrie-decembrie 2 2 can provide scientists and engineers with valuable information to properly incorporate the material into Figure 1. Principles of parallel plate capacitor. its intended application for more solid designs or to monitor a manufacturing process for improved quality control. A material is classified as “dielectric” if it has the ability to store energy when an external electric field is applied. The dielectric material increases the storage capacity of the parallel plate capacitor by neutralizing charges at the electrodes, which ordinarily would contribute to the external field. If an AC sinusoidal voltage source V is placed across the same capacitor , the resulting current I (1) will be made up of a charging current I c and a loss current I l that is related to the dielectric constant. The losses in the material can be represented as a conductance (G) in parallel with a capacitor (C).
Blockchain as a Service for IoT

A blockchain is a distributed and decentralized ledger that contains connected blocks of transactions. Unlike other ledger approaches, blockchain guarantees tamper proof storage of approved transactions. Due to its distributed and decentralized organization, blockchain is beeing used within IoT e.g. to manage device configuration, store sensor data and enable micro-payments. This paper presents the idea of using blockchain as a service for IoT and evaluates the performance of a cloud and edge hosted blockchain implementation.
Data mining models for student careers

This paper presents a data mining methodology to analyze the careers of University graduated students. We present different approaches based on clustering and sequential patterns techniques in order to identify strategies for improving the performance of students and the scheduling of exams. We introduce an ideal career as the career of an ideal student which has taken each examination just after the end of the corresponding course, without delays. We then compare the career of a generic student with the ideal one by using the different techniques just introduced. Finally, we apply the methodology to a real case study and interpret the results which underline that the more students follow the order given by the ideal career the more they get good performance in terms of graduation time and final grade.
Dynamic control and dead-time compensation method of an isolated dual-active-bridge DC-DC converter

This paper presents dynamic control and a dead-time compensation method of an isolated dual-active-bridge (DAB) dc-dc converter. Conventional phase-shift control methods for the DAB converter may cause dc offsets in both inductor current and transformer magnetic flux density in transient states. The dynamic control method in this paper independently controls the diagonal switches in each H-bridge converter to modify the duty ratios in transient states. This method enables both inductor current and transformer magnetic flux density to be settled within a half switching period without any dc offsets. Moreover, the phase-shift error caused by the dead time is analyzed in terms of the switching angle. This analysis reveals that the dead time causes the switching-angle error in only one of the two H-bridge converters and may cause dc offsets. A new dead-time compensation method based on this analysis is proposed to eliminate the dc offsets and to achieve a good dynamic response. A 5-kW experimental system verifies the validity of the proposed control method.
Dirichlet Process

The Dirichlet process is a stochastic proces used in Bayesian nonparametric models of data, particularly in Dirichlet process mixture models (also known as infinite mixture models). It is a distribution over distributions, i.e. each draw from a Dirichlet process is itself a distribution. It is called a Dirichlet process because it has Dirichlet distributed finite dimensional marginal distributions, just as the Gaussian process, another popular stochastic process used for Bayesian nonparametric regression, has Gaussian distributed finite dimensional marginal distributions. Distributions drawn from a Dirichlet process are discrete, but cannot be described using a finite number of parameters, thus the classification as a nonparametric model.
A Survey of 3DTV Displays: Techniques and Technologies

The display is the last component in a chain of activity from image acquisition, compression, coding transmission and reproduction of 3-D images through to the display itself. There are various schemes for 3-D display taxonomy; the basic categories adopted for this paper are: holography where the image is produced by wavefront reconstruction, volumetric where the image is produced within a volume of space and multiple image displays where two or more images are seen across the viewing field. In an ideal world a stereoscopic display would produce images in real time that exhibit all the characteristics of the original scene. This would require the wavefront to be reproduced accurately, but currently this can only be achieved using holographic techniques. Volumetric displays provide both vertical and horizontal parallax so that several viewers can see 3-D images that exhibit no accommodation/convergence rivalry. Multiple image displays fall within three fundamental types: holoform in which a large number of views give smooth motion parallax and hence a hologram-like appearance, multiview where a series of discrete views are presented across viewing field and binocular where only two views are presented in regions that may occupy fixed positions or follow viewers' eye positions by employing head tracking. Holography enables 3-D scenes to be encoded into an interference pattern, however, this places constraints on the display resolution necessary to reconstruct a scene. Although holography may ultimately offer the solution for 3DTV, the problem of capturing naturally lit scenes will first have to be solved and holography is unlikely to provide a short-term solution due to limitations in current enabling technologies. Liquid crystal, digital micromirror, optically addressed liquid crystal and acoustooptic spatial light modulators (SLMs) have been employed as suitable spatial light modulation devices in holography. Liquid crystal SLMs are generally favored owing to the commercial availability of high fill factor, high resolution addressable devices. Volumetric displays provide both vertical and horizontal parallax and several viewers are able to see a 3-D image that exhibits no accommodation/convergence rivalry. However, the principal disadvantages of these displays are: the images are generally transparent, the hardware tends to be complex and non-Lambertian intensity distribution cannot be displayed. Multiple image displays take many forms and it is likely that one or more of these will provide the solution(s) for the first generation of 3DTV displays.
Glaucoma Detection Using Dwt Based Energy Features and Ann Classifier

Glaucoma is a disease in which fluid pressure in the eye increases continously, and damage the optic nerve and leads to vision loss.It is the second leading cause of blindness. For identification of disease in human eyes we are using clinical decision support system which is based on retinal image analysis technique,that used to extract structure,contextual or texture features.Texture features within images which gives accurate and efficicent glaucoma classification.For finding this texture features we use energy distribution over wavelet subband.In this paper we focus on fourteen features which is obtained from daubecies(db3),symlets(sym3),and biorthogonal(bio3.3,bio3.5,and bio3.7).We propose a novel technique to extract this energy signatures using 2D wavelet transform and passed these signatures to different feature ranking and feature selection strategies.The energy obtained from detailed coefficent are used to classify normal and glaucomatous image with high accuracy. This will be classified using support vector machines, sequential minimal optimization, random forest, naive Bayes and artificial neural network.We observed an accuracy of 94% using the ANN classifier.Performance graph is shown for all classifiers.Finally the defected region founded by segmentation and this will be post processed by morphological processing technique for smoothing operation.
ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification

In this work, we introduce a new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video. We do so by integrating state-of-the-art two-stream networks [42] with learnable spatio-temporal feature aggregation . The resulting architecture is end-to-end trainable for whole-video classification. We investigate different strategies for pooling across space and time and combining signals from the different streams. We find that: (i) it is important to pool jointly across space and time, but (ii) appearance and motion streams are best aggregated into their own separate representations. Finally, we show that our representation outperforms the two-stream base architecture by a large margin (13% relative) as well as outperforms other baselines with comparable base architectures on HMDB51, UCF101, and Charades video classification benchmarks.