nils-herrmann/clean_scirepeval huggingface dataset:
Semantic Perceptual Image Compression Using Deep Convolution Networks

It has long been considered a significant problem to improve the visual quality of lossy imageand video compression. Recent advances in computing power together with the availabilityof large training data sets has increased interest in the application of deep learning cnnsto address image recognition and image processing tasks. Here, we present a powerful cnntailored to the specific task of semantic image understanding to achieve higher visual qualityin lossy compression. A modest increase in complexity is incorporated to the encoder whichallows a standard, off-the-shelf jpeg decoder to be used. While jpeg encoding may beoptimized for generic images, the process is ultimately unaware of the specific content ofthe image to be compressed. Our technique makes jpeg content-aware by designing andtraining a model to identify multiple semantic regions in a given image. Unlike objectdetection techniques, our model does not require labeling of object positions and is able toidentify objects in a single pass. We present a new cnn architecture directed specifically toimage compression, which generates a map that highlights semantically-salient regions sothat they can be encoded at higher quality as compared to background regions. By addinga complete set of features for every class, and then taking a threshold over the sum of allfeature activations, we generate a map that highlights semantically-salient regions so thatthey can be encoded at a better quality compared to background regions. Experimentsare presented on the Kodak PhotoCD dataset and the MIT Saliency Benchmark dataset, in which our algorithm achieves higher visual quality for the same compressed size whilepreserving PSNR1.
Asymmetrical control with phase lock loop for induction cooking appliances

This paper presents an asymmetrical control with phase lock loop for series resonant inverters. This control strategy is used in full-bridge topologies for induction cookers. The operating frequency is automatically tracked to maintain a small constant lagging phase angle when load parameters change. The switching loss is minimized by operating the IGBT in the zero voltage resonance modes. The output power can be adjusted by using asymmetrical voltage cancellation control which is regulated with a PWM duty cycle control strategy.
Wireless Sensor Networks for Early Detection of Forest Fires

We present the design and evaluation of a wireless sensor network for early detection of forest fires. We first present the key aspects in modeling forest fires. We do this by analyzing the Fire Weather Index (FWI) System, and show how its different components can be used in designing efficient fire detection systems. The FWI System is one of the most comprehensive forest fire danger rating systems in North America, and it is backed by several decades of forestry research. The analysis of the FWI System could be of interest in its own right to researchers working in the sensor network area and to sensor manufacturers who can optimize the communication and sensing modules of their products to better fit forest fire detection systems. Then, we model the forest fire detection problem as a k-coverage problem in wireless sensor networks. In addition, we present a simple data aggregation scheme based on the FWI System. This data aggregation scheme significantly prolongs the network lifetime, because it only delivers the data that is of interest to the application. We validate several aspects of our design using simulation.
The thermoregulatory theory of yawning: what we know from over 5 years of research

Over the past 5 years numerous reports have confirmed and replicated the specific brain cooling and thermal window predictions derived from the thermoregulatory theory of yawning, and no study has found evidence contrary to these findings. Here we review the comparative research supporting this model of yawning among homeotherms, while highlighting a recent report showing how the expression of contagious yawning in humans is altered by seasonal climate variation. The fact that yawning is constrained to a thermal window of ambient temperature provides unique and compelling support in favor of this theory. Heretofore, no existing alternative hypothesis of yawning can explain these results, which have important implications for understanding the potential functional role of this behavior, both physiologically and socially, in humans and other animals. In discussion we stress the broader applications of this work in clinical settings, and counter the various criticisms of this theory.
Some Faces are More Equal than Others: Hierarchical Organization for Accurate and Efficient Large-Scale Identity-Based Face Retrieval

This paper presents a novel method for hierarchically organizing large face databases, with application to efficient identity-based face retrieval. The method relies on metric learning with local binary pattern (LBP) features. On one hand, LBP features have proved to be highly resilient to various appearance changes due to illumination and contrast variations while being extremely efficient to calculate. On the other hand, metric learning (ML) approaches have been proved very successful for face verification ‘in the wild’, i.e. in uncontrolled face images with large amounts of variations in pose, expression, appearances, lighting, etc. While such ML based approaches compress high dimensional features into low dimensional spaces using discriminatively learned projections, the complexity of retrieval is still significant for large scale databases (with millions of faces). The present paper shows that learning such discriminative projections locally while organizing the database hierarchically leads to a more accurate and efficient system. The proposed method is validated on the standard Labeled Faces in the Wild (LFW) benchmark dataset with millions of additional distracting face images collected from photos on the internet.
Supporting Collaborative Process Knowledge Management in New Product Development Teams

Knowledge centric activities of developing new products and services are becoming the primary source of sustainable competitive advantage in an era characterized by short product life cycles, dynamic markets and complex processes. We Ž . view new product development NPD as a knowledge-intensive activity. Based on a case study in the consumer electronics Ž . industry, we identify problems associated with knowledge management KM in the context of NPD by cross-functional collaborative teams. We map these problems to broad Information Technology enabled solutions and subsequently translate these into specific system characteristics and requirements. A prototype system that meets these requirements developed to capture and manage tacit and explicit process knowledge is further discussed. The functionalities of the system include functions for representing context with informal components, easy access to process knowledge, assumption surfacing, review of past knowledge, and management of dependencies. We demonstrate the validity our proposed solutions using scenarios drawn from our case study. q 1999 Elsevier Science B.V. All rights reserved.
KSTR: Keyword-Aware Skyline Travel Route Recommendation

With the popularity of social media (e.g., Facebook and Flicker), users could easily share their check-in records and photos during their trips. In view of the huge amount of check-in data and photos in social media, we intend to discover travel experiences to facilitate trip planning. Prior works have been elaborated on mining and ranking existing travel routes from check-in data. We observe that when planning a trip, users may have some keywords about preference on his/her trips. Moreover, a diverse set of travel routes is needed. To provide a diverse set of travel routes, we claim that more features of Places of Interests (POIs) should be extracted. Therefore, in this paper, we propose a Keyword-aware Skyline Travel Route (KSTR) framework that use knowledge extraction from historical mobility records and the user's social interactions. Explicitly, we model the "Where, When, Who" issues by featurizing the geographical mobility pattern, temporal influence and social influence. Then we propose a keyword extraction module to classify the POI-related tags automatically into different types, for effective matching with query keywords. We further design a route reconstruction algorithm to construct route candidates that fulfill the query inputs. To provide diverse query results, we explore Skyline concepts to rank routes. To evaluate the effectiveness and efficiency of the proposed algorithms, we have conducted extensive experiments on real location-based social network datasets, and the experimental results show that KSTR does indeed demonstrate good performance compared to state-of-the-art works.
A Reconfigurable Circularly Polarized Microstrip Antenna With a Slotted Ground Plane

This letter describes a square patch antenna with a switchable circular polarization (CP) sense. The proposed antenna has four L-shaped slots on the ground plane, and the CP radiation can be generated by current perturbation due to the slotted ground plane. Because the CP sense of the proposed antenna is altered by the current path that is redirected with switched p-i-n diodes on the slots, the CP direction of the proposed antenna can be simply switched between the right-handed and left-handed CPs. As the slot and bias circuits are not placed on the patch side, the proposed antenna has a simple structure and can radiate a CP wave without altering the main beam direction. The experimental results show that antennas have excellent switchable radiation performance at 2.4 GHz.
Processing Sliding Window Multi-Joins in Continuous Queries over Data Streams

We study sliding window multi-join processing in continuous queries over data streams. Several algorithms are reported for performing continuous, incremental joins, under the assumption that all the sliding windows fit in main memory. The algorithms include multiway incremental nested loop joins (NLJs) and multi-way incremental hash joins. We also propose join ordering heuristics to minimize the processing cost per unit time. We test a possible implementation of these algorithms and show that, as expected, hash joins are faster than NLJs for performing equi-joins, and that the overall processing cost is influenced by the strategies used to remove expired tuples from the sliding windows.
Presenteeism in the workplace : A review and research agenda

Presenteeism refers to attending work while ill. Although it is a subject of intense interest to scholars in occupational medicine, relatively few organizational scholars are familiar with the concept. This article traces the development of interest in presenteeism, considers its various conceptualizations, and explains how presenteeism is typically measured. Organizational and occupational correlates of attending work when ill are reviewed, as are medical correlates of resulting productivity loss. It is argued that presenteeism has important implications for organizational theory and practice, and a research agenda for organizational scholars is presented.
Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach

In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from a small (n≤35) set of labeled data of the same MRI contrast, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (r≥0.97) also with the expected lesion volume.
Techniques of Cluster Algorithms in Data Mining

An overview of cluster analysis techniques from a data mining point of view is given. This is done by a strict separation of the questions of various similarity and distance measures and related optimization criteria for clusterings from the methods to create and modify clusterings themselves. In addition to this general setting and overview, the second focus is used on discussions of the essential ingredients of the demographic cluster algorithm of IBM's Intelligent Miner, based Condorcet's criterion.
Real-Time Illegal Parking Detection in Outdoor Environments Using 1-D Transformation

With decreasing costs of high-quality surveillance systems, human activity detection and tracking has become increasingly practical. Accordingly, automated systems have been designed for numerous detection tasks, but the task of detecting illegally parked vehicles has been left largely to the human operators of surveillance systems. We propose a methodology for detecting this event in real time by applying a novel image projection that reduces the dimensionality of the data and, thus, reduces the computational complexity of the segmentation and tracking processes. After event detection, we invert the transformation to recover the original appearance of the vehicle and to allow for further processing that may require 2-D data. We evaluate the performance of our algorithm using the i-LIDS vehicle detection challenge datasets as well as videos we have taken ourselves. These videos test the algorithm in a variety of outdoor conditions, including nighttime video and instances of sudden changes in weather.
Measurement of an ozone generator using a phase-shifted PWM full bridge inverter

This paper proposes the measurement of an ozone generator using a phase-shifted pulse width modulation (PWM) full bridge inverter as a power supply. The method of electrode parameter measurement for an ozone generator is presented. An electrode set is represented by an equivalent circuit by parallel capacitor and resistor. The test was performed with high-frequency high-voltage ac power supply. Then voltage and charge characteristics were obtained leading to calculating values of the parallel capacitor and resistor. The validity of the obtained parameters is verified with a comparison between simulation and experimental results. It has proved that the proposed equivalent circuit with parameters obtained from the proposed method is valid which is in agreement with experimental results. It can be used for power supply and electrode design for generating ozone gas. The correctness of the proposed technique is verified by both simulation and experimental results.
Rumors in a Network: Who's the Culprit?

We provide a systematic study of the problem of finding the source of a rumor in a network. We model rumor spreading in a network with the popular susceptible-infected (SI) model and then construct an estimator for the rumor source. This estimator is based upon a novel topological quantity which we term rumor centrality. We establish that this is a maximum likelihood (ML) estimator for a class of graphs. We find the following surprising threshold phenomenon: on trees which grow faster than a line, the estimator always has nontrivial detection probability, whereas on trees that grow like a line, the detection probability will go to 0 as the network grows. Simulations performed on synthetic networks such as the popular small-world and scale-free networks, and on real networks such as an internet AS network and the U.S. electric power grid network, show that the estimator either finds the source exactly or within a few hops of the true source across different network topologies. We compare rumor centrality to another common network centrality notion known as distance centrality. We prove that on trees, the rumor center and distance center are equivalent, but on general networks, they may differ. Indeed, simulations show that rumor centrality outperforms distance centrality in finding rumor sources in networks which are not tree-like.
Sequence-to-Sequence Models Can Directly Translate Foreign Speech

We present a recurrent encoder-decoder deep neural network architecture that directly translates speech in one language into text in another. The model does not explicitly transcribe the speech into text in the source language, nor does it require supervision from the ground truth source language transcription during training. We apply a slightly modified sequence-to-sequence with attention architecture that has previously been used for speech recognition and show that it can be repurposed for this more complex task, illustrating the power of attention-based models. A single model trained end-to-end obtains state-of-the-art performance on the Fisher Callhome Spanish-English speech translation task, outperforming a cascade of independently trained sequence-to-sequence speech recognition and machine translation models by 1.8 BLEU points on the Fisher test set. In addition, we find that making use of the training data in both languages by multi-task training sequence-to-sequence speech translation and recognition models with a shared encoder network can improve performance by a further 1.4 BLEU points.
A tunable flipflop-based frequency divider up to 113 GHz and a fully differential 77GHz push-push VCO in SiGe BiCMOS technology

We present a tunable flipflop-based frequency divider and a fully differential push-push VCO designed in a 200GHz fT SiGe BiCMOS technology. A new technique for tuning the sensitivity of the divider in the frequency range of interest is presented. The chip works from 60GHz up to 113GHz. The VCO is based on a new topology which allows generating differential push-push outputs. The VCO shows a tuning range larger than 7GHz. The phase noise is 75dBc/Hz at 100kHz offset. The chip shows a frequency drift of 12.3MHz/C. The fundamental signal suppression is larger than 50dB. The output power is 2×5dBm. At a 3.3V supply, the circuits consume 35mA and 65mA, respectively.
Techniques for Learning Binary Stochastic Feedforward Neural Networks

Stochastic binary hidden units in a multi-layer perceptron (MLP) network give at least three potential benefits when compared to deterministic MLP networks. (1) They allow to learn one-to-many type of mappings. (2) They can be used in structured prediction problems, where modeling the internal structure of the output is important. (3) Stochasticity has been shown to be an excellent regularizer, which makes generalization performance potentially better in general. However, training stochastic networks is considerably more difficult. We study training using M samples of hidden activations per input. We show that the case M = 1 leads to a fundamentally different behavior where the network tries to avoid stochasticity. We propose two new estimators for the training gradient and propose benchmark tests for comparing training algorithms. Our experiments confirm that training stochastic networks is difficult and show that the proposed two estimators perform favorably among all the five known estimators.
Could Global Software Development Benefit from Agile Methods?

At first glance, agile methods and global software development might seem incompatible. Agile methods stress continuous face-to-face communication, whereas communication has been reported as the biggest problem of global software development. One challenge to solve is how to apply agile practices in settings where continuous face-to-face interaction is missing. However, agile methods have been successfully used in distributed projects, indicating that they could benefit global software development. This paper discusses potential benefits and challenges of adopting agile methods in global software development. The literature on real industrial case studies reporting on experiences of using agile methods in distributed projects is still scarce. Therefore we suggest further research on the topic. We present our plans for research in companies using agile methods in their distributed projects. We also intend to test the use of agile principles in globally distributed student projects developing software for industrial clients
What we can do and what we cannot do with fMRI

Functional magnetic resonance imaging (fMRI) is currently the mainstay of neuroimaging in cognitive neuroscience. Advances in scanner technology, image acquisition protocols, experimental design, and analysis methods promise to push forward fMRI from mere cartography to the true study of brain organization. However, fundamental questions concerning the interpretation of fMRI data abound, as the conclusions drawn often ignore the actual limitations of the methodology. Here I give an overview of the current state of fMRI, and draw on neuroimaging and physiological data to present the current understanding of the haemodynamic signals and the constraints they impose on neuroimaging data interpretation.
Learning online alignments with continuous rewards policy gradient

Sequence-to-sequence models with soft attention had significant success in machine translation, speech recognition, and question answering. Though capable and easy to use, they require that the entirety of the input sequence is available at the beginning of inference, an assumption that is not valid for instantaneous translation and speech recognition. To address this problem, we present a new method for solving sequence-to-sequence problems using hard online alignments instead of soft offline alignments. The online alignments model is able to start producing outputs without the need to first process the entire input sequence. A highly accurate online sequence-to-sequence model is useful because it can be used to build an accurate voice-based instantaneous translator. Our model uses hard binary stochastic decisions to select the timesteps at which outputs will be produced. The model is trained to produce these stochastic decisions using a standard policy gradient method. In our experiments, we show that this model achieves encouraging performance on TIMIT and Wall Street Journal (WSJ) speech recognition datasets.