nils-herrmann/clean_scirepeval huggingface dataset:
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration

Image restoration is a long-standing problem in low-level computer vision with many interesting applications. We describe a flexible learning framework based on the concept of nonlinear reaction diffusion models for various image restoration problems. By embodying recent improvements in nonlinear diffusion models, we propose a dynamic nonlinear reaction diffusion model with time-dependent parameters (i.e., linear filters and influence functions). In contrast to previous nonlinear diffusion models, all the parameters, including the filters and the influence functions, are simultaneously learned from training data through a loss based approach. We call this approach TNRD—Trainable Nonlinear Reaction Diffusion. The TNRD approach is applicable for a variety of image restoration tasks by incorporating appropriate reaction force. We demonstrate its capabilities with three representative applications, Gaussian image denoising, single image super resolution and JPEG deblocking. Experiments show that our trained nonlinear diffusion models largely benefit from the training of the parameters and finally lead to the best reported performance on common test datasets for the tested applications. Our trained models preserve the structural simplicity of diffusion models and take only a small number of diffusion steps, thus are highly efficient. Moreover, they are also well-suited for parallel computation on GPUs, which makes the inference procedure extremely fast.
A review of analytical techniques for gait data. Part 2: neural network and wavelet methods.

Multivariate gait data have traditionally been challenging to analyze. Part 1 of this review explored applications of fuzzy, multivariate statistical and fractal methods to gait data analysis. Part 2 extends this critical review to the applications of artificial neural networks and wavelets to gait data analysis. The review concludes with a practical guide to the selection of alternative gait data analysis methods. Neural networks are found to be the most prevalent non-traditional methodology for gait data analysis in the last 10 years. Interpretation of multiple gait signal interactions and quantitative comparisons of gait waveforms are identified as important data analysis topics in need of further research.
Molecular de-novo design through deep reinforcement learning

This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model.
Organizational acculturation and social networking

For large global enterprises, providing adequate resources for organizational acculturation, the process in which employees learn about an organization's culture, remains a challenge. We present results from a survey of 802 users from an enterprise social networking site that identifies two groups of employees (new to the company and geographically distant from headquarters) that perceive higher benefit from using a SNS to learn about the organization's values and beliefs. In addition, we observe regional differences in viewing behaviors between two groups of new employees. These results suggest that a SNS can also potentially contribute to the information-seeking and sense-making activities that underlie organization acculturation.
An Automatic System for Extracting Figures and Captions in Biomedical PDF Documents

Figures in biomedical articles often constitute direct evidence of experimental results. Image analysis methods can be coupled with text-based methods to improve knowledge discovery. However, automatically harvesting figures along with their associated captions from full-text articles remains challenging. In this paper, we present an automatic system for robustly harvesting figures from biomedical literature. Our approach relies on the idea that the PDF specification of the document layout can be used to identify encoded figures and figure boundaries within the PDF and enforce constraints among figure-regions. This allows us to harvest fragments of figures (subfigures), from the PDF, correctly identify subfigures that belong to the same figure, and identify the captions associated with each figure. Our method simultaneously recovers figures and captions and applies additional filtering process to remove irrelevant figures such as logos, to eliminate text passages that were incorrectly identified as captions, and to re-group subfigures to generate a putative figure. Finally, we associate figures with captions. Our preliminary experiments suggest that our method achieves an accuracy of 95% in harvesting figures-caption pairs from a set of 2, 035 full-text biomedical documents from Bio Creative III, containing 12, 574 figures.
Segmentation of overlapping cervical cells: A variational method with star-shape prior

Accurate and automatic detection and delineation of cervical cells are two critical precursor steps to automatic Pap smear image analysis and detecting pre-cancerous changes in the uterine cervix. To overcome noise and cell occlusion, many segmentation methods resort to incorporating shape priors, mostly enforcing elliptical shapes (e.g. ). However, elliptical shapes do not accurately model cervical cells. In this paper, we propose a new continuous variational segmentation framework with star-shape prior using directional derivatives to segment overlapping cervical cells in Pap smear images. We show that our star-shape constraint better models the underlying problem and outperforms state-of-the-art methods in terms of accuracy and speed.
Toward Refocused Optical Mouse Sensors for Outdoor Optical Flow Odometry

This paper investigates the use of refocused optical mouse sensors for odometry in the field of outdoor robotic navigation. Optical mouse sensors like the ADNS-2610 are small, inexpensive, non-contact devices, which integrate a complementary metal-oxide semiconductor camera and DSP hardware to provide 2-D optical displacement measurements. Current research indicates that vertical height variance contributes as a dominant cause of systematic error to horizontal displacement measurements, which raises significant problems for irregular environments encountered in outdoor robotic navigation. In this paper, we propose two approaches to mitigate this systematic error induced by height variance. The efficacy and robustness of the proposed approaches are tested by experimentation on an asphalt concrete road surface and by simulation.
Data Remanence in Flash Memory Devices

Data remanence is the residual physical representation of data that has been erased or overwritten. In non-volatile programmable devices, such as UV EPROM, EEPROM or Flash, bits are stored as charge in the floating gate of a transistor. After each erase operation, some of this charge remains. Security protection in microcontrollers and smartcards with EEPROM/Flash memories is based on the assumption that information from the memory disappears completely after erasing. While microcontroller manufacturers successfully hardened already their designs against a range of attacks, they still have a common problem with data remanence in floating-gate transistors. Even after an erase operation, the transistor does not return fully to its initial state, thereby allowing the attacker to distinguish between previously programmed and not programmed transistors, and thus restore information from erased memory. The research in this direction is summarised here and it is shown how much information can be extracted from some microcontrollers after their memory has been ‘erased’.
Acquisition of Cognitive Skill

A framework for skill acquisition is proposed that includes two major stages in the development of a cognitive skill: a declarative stage in which facts about the skill domain are interpreted and a procedural stage in which the domain knowledge is directly embodied in procedures for performing the skill. This general framework has been instantiated in the ACT system in which facts are encoded in a propositional network and procedures are encoded as productions. Knowledge compilation is the process by which the skill transits from the declarative stage to the procedural stage. It consists of the subprocesses of composition, which collapses sequences of productions into single productions, and proceduralization, which embeds factual knowledge into productions. Once proceduralized, further learning processes operate on the skill to make the productions more selective in their range of applications. These processes include generalization, discrimination, and strengthening of productions. Comparisons are made to similar concepts from past learning theories. How these learning mechanisms apply to produce the power law speedup in processing time with practice is discussed.
Fast moving-object detection in H.264/AVC compressed domain for video surveillance

This paper discusses a novel fast approach for moving object detection in H.264/AVC compressed domain for video surveillance applications. The proposed algorithm initially segments out edges from regions with motion at macroblock level by utilizing the gradient of quantization parameter over 2D-image space. A spatial median filtering of the segmented edges followed by weighted temporal accumulation accounts for whole object segmentation. To attain sub-macroblock (4×4) level precision, the size of macroblocks (in bits) is interpolated using a two tap filter. Partial decoding rules out the complexity involved in full decoding and gives fast foreground segmentation results. Compared to other compressed domain techniques, the proposed approach allows the video streams to be encoded with different quantization parameters across macroblocks thereby increasing flexibility in bit rate adjustment.
A Survey of User Modelling in Social Media Websites

With the widespread of social media websites in the internet, and the huge number of users participating and generating infinite number of contents in these websites, the need for personalisation increases dramatically to become a necessity. One of the major issues in personalisation is building users’ profiles, which depend on many elements; such as the used data, the application domain they aim to serve, the representation method and the construction methodology. Recently, this area of research has been a focus for many researchers, and hence, the proposed methods are increasing very quickly. This survey aims to discuss the available user modelling techniques for social media websites, and to highlight the weakness and strength of these methods and to provide a vision for future work in user modelling in social media websites.
Verb-specific constraints in sentence processing: separating effects of lexical preference from garden-paths.

Immediate effects of verb-specific syntactic (subcategorization) information were found in a cross-modal naming experiment, a self-paced reading experiment, and an experiment in which eye movements were monitored. In the reading studies, syntactic misanalysis effects in sentence complements (e.g., "The student forgot the solution was...") occurred at the verb in the complement (e.g., was) for matrix verbs typically used with noun phrase complements but not for verbs typically used with sentence complements. In addition, a complementizer effect for sentence-complement-biased verbs was not due to syntactic misanalysis but was correlated with how strongly a particular verb prefers to be followed by the complementizer that. The results support models that make immediate use of lexically specific constraints, especially constraint-based models, but are problematic for lexical filtering models.
Improved Regularization of Convolutional Neural Networks with Cutout

Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR10, CIFAR-100, and SVHN datasets, yielding new state-ofthe-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code available at https://github.com/ uoguelph-mlrg/Cutout.
Spice-compatible modeling of high injection and propagation of minority carriers in the substrate of Smart Power ICs

Classical substrate noise analysis considers the silicon resistivity of an integrated circuit only as doping dependent besides neglecting diffusion currents as well. In power circuits minority carriers are injected into the substrate and propagate by drift–diffusion. In this case the conductivity of the substrate is spatially modulated and this effect is particularly important in high injection regime. In this work a description of the coupling between majority and minority drift–diffusion currents is presented. A distributed model of the substrate is then proposed to take into account the conductivity modulation and its feedback on diffusion processes. The model is expressed in terms of equivalent circuits in order to be fully compatible with circuit simulators. The simulation results are then discussed for diodes and bipolar transistors and compared to the ones obtained from physical device simulations and measurements. 2014
Testosterone and dominance in men.

In men, high levels of endogenous testosterone (T) seem to encourage behavior intended to dominate--to enhance one's status over--other people. Sometimes dominant behavior is aggressive, its apparent intent being to inflict harm on another person, but often dominance is expressed nonaggressively. Sometimes dominant behavior takes the form of antisocial behavior, including rebellion against authority and low breaking. Measurement of T at a single point in time, presumably indicative of a man's basal T level, predicts many of these dominant or antisocial behaviors. T not only affects behavior but also responds to it. The act of competing for dominant status affects male T levels in two ways. First, T rises in the face of a challenge, as if it were an anticipatory response to impending competition. Second, after the competition, T rises in winners and declines in losers. Thus, there is a reciprocity between T and dominance behavior, each affecting the other. We contrast a reciprocal model, in which T level is variable, acting as both a cause and effect of behavior, with a basal model, in which T level is assumed to be a persistent trait that influences behavior. An unusual data set on Air Force veterans, in which data were collected four times over a decade, enables us to compare the basal and reciprocal models as explanations for the relationship between T and divorce. We discuss sociological implications of these models.
Asset Pricing Models and Financial Market Anomalies

This article develops a framework that applies to single securities to test whether asset pricing models can explain the size, value, and momentum anomalies. Stock level beta is allowed to vary with firm-level size and book-to-market as well as with macroeconomic variables. With constant beta, none of the models examined capture any of the market anomalies. When beta is allowed to vary, the size and value effects are often explained, but the explanatory power of past return remains robust. The past return effect is captured by model mispricing that varies with macroeconomic variables.
The Clipmap: A Virtual Mipmap

We describe theclipmap, a dynamic texture representation that efficiently caches textures of arbitrarily large size in a finite amount of physical memory for rendering at real-time rates. Further, we describe a software system for managing clipmaps that supports integration into demanding real-time applications. We show the scale and robustness of this integrated hardware/software architecture by reviewing an application virtualizing a 170 gigabyte texture at 60 Hertz. Finally, we suggest ways that other rendering systems may exploit the concepts underlying clipmaps to solve related problems.
FPGP: Graph Processing Framework on FPGA A Case Study of Breadth-First Search

Large-scale graph processing is gaining increasing attentions in many domains. Meanwhile, FPGA provides a power-efficient and highly parallel platform for many applications, and has been applied to custom computing in many domains. In this paper, we describe FPGP (FPGA Graph Processing), a streamlined vertex-centric graph processing framework on FPGA, based on the interval-shard structure. FPGP is adaptable to different graph algorithms and users do not need to change the whole implementation on the FPGA. In our implementation, an on-chip parallel graph processor is proposed to both maximize the off-chip bandwidth of graph data and fully utilize the parallelism of graph processing. Meanwhile, we analyze the performance of FPGP and show the scalability of FPGP when the bandwidth of data path increases. FPGP is more power-efficient than single machine systems and scalable to larger graphs compared with other FPGA-based graph systems.
A Fractal-Based Circularly Polarized UHF RFID Reader Antenna

This letter presents the design of a coplanar waveguide (CPW) circularly polarized antenna for the central frequency 900 MHz, it comes in handy for radio frequency identification (RFID) short-range reading applications within the band of 902-928 MHz where the axial ratio of proposed antenna model is less than 3 dB. The proposed design has an axial-ratio bandwidth of 36 MHz (4%) and impedance bandwidth of 256 MHz (28.5%).
Irregularity detection on low tension electric installations by neural network ensembles

The volume of energy loss that Brazilian electric utilities have to deal with has been ever increasing. The electricity concessionaries are suffering significant and increasing loss in the last years, due to theft, measurement errors and many other kinds of irregularities. Therefore, there is a great concern from those companies to identify the profile of irregular customers, in order to reduce the volume of such losses. This paper presents the proposal of an intelligent system, composed of two neural networks ensembles, which intends to increase the level of accuracy in the identification of irregularities among low tension consumers. The data used to test the proposed system are from Light S.A. Company, the Rio de Janeiro concessionary. The results obtained presented a significant increase in the identification of irregular customers when compared to the current methodology employed by the company.
Light-Head R-CNN: In Defense of Two-Stage Object Detector

In this paper, we first investigate why typical two-stage methods are not as fast as single-stage, fast detectors like YOLO [26, 27] and SSD [22]. We find that Faster RCNN [28] and R-FCN [17] perform an intensive computation after or before RoI warping. Faster R-CNN involves two fully connected layers for RoI recognition, while RFCN produces a large score maps. Thus, the speed of these networks is slow due to the heavy-head design in the architecture. Even if we significantly reduce the base model, the computation cost cannot be largely decreased accordingly. We propose a new two-stage detector, Light-Head RCNN, to address the shortcoming in current two-stage approaches. In our design, we make the head of network as light as possible, by using a thin feature map and a cheap R-CNN subnet (pooling and single fully-connected layer). Our ResNet-101 based light-head R-CNN outperforms state-of-art object detectors on COCO while keeping time efficiency. More importantly, simply replacing the backbone with a tiny network (e.g, Xception), our LightHead R-CNN gets 30.7 mmAP at 102 FPS on COCO, significantly outperforming the single-stage, fast detectors like YOLO [26, 27] and SSD [22] on both speed and accuracy. Code will be made publicly available.
Frequency assignment: Theory and applications

In this paper we introduce the minimum-order approach to frequency assignment and present a theory which relates this approach to the traditional one. This new approach is potentially more desirable than the traditional one. We model assignment problems as both frequency-distance constrained and frequency constrained optimization problems. The frequency constrained approach should be avoided if distance separation is employed to mitigate interference. A restricted class of graphs, called disk graphs, plays a central role in frequency-distance constrained problems. We introduce two generalizations of chromatic number and show that many frequency assignment problems are equivalent to generalized graph coloring problems. Using these equivalences and recent results concerning the complexity of graph coloring, we classify many frequency assignment problems according to the "execution time efficiency" of algorithms that may be devised for their solution. We discuss applications to important real world problems and identify areas for further work.
Shared Predictive Cross-Modal Deep Quantization

With explosive growth of data volume and ever-increasing diversity of data modalities, cross-modal similarity search, which conducts nearest neighbor search across different modalities, has been attracting increasing interest. This paper presents a deep compact code learning solution for efficient cross-modal similarity search. Many recent studies have proven that quantization-based approaches perform generally better than hashing-based approaches on single-modal similarity search. In this paper, we propose a deep quantization approach, which is among the early attempts of leveraging deep neural networks into quantization-based cross-modal similarity search. Our approach, dubbed shared predictive deep quantization (SPDQ), explicitly formulates a shared subspace across different modalities and two private subspaces for individual modalities, and representations in the shared subspace and the private subspaces are learned simultaneously by embedding them to a reproducing kernel Hilbert space, where the mean embedding of different modality distributions can be explicitly compared. In addition, in the shared subspace, a quantizer is learned to produce the semantics preserving compact codes with the help of label alignment. Thanks to this novel network architecture in cooperation with supervised quantization training, SPDQ can preserve intramodal and intermodal similarities as much as possible and greatly reduce quantization error. Experiments on two popular benchmarks corroborate that our approach outperforms state-of-the-art methods.