nils-herrmann/clean_scirepeval huggingface dataset:
Open Database of Epileptic EEG with MRI and Postoperational Assessment of Foci—a Real World Verification for the EEG Inverse Solutions

This paper introduces a freely accessible database http://eeg.pl/epi , containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical electroencephalography (EEG) recording (10–20 system) with inter-ictal discharges marked separately by an expert, (2) a full set of magnetic resonance imaging (MRI) scans for calculations of the realistic forward models, (3) structural placement of the epileptogenic zone, recognized by electrocorticography (ECoG) and post-surgical results, plotted on pre-surgical MRI scans in transverse, sagittal and coronal projections, (4) brief clinical description of each case. The main goal of this project is evaluation of possible improvements of localization of epileptic foci from the surface EEG recordings. These datasets offer a unique possibility for evaluating different EEG inverse solutions. We present preliminary results from a subset of these cases, including comparison of different schemes for the EEG inverse solution and preprocessing. We report also a finding which relates to the selective parametrization of single waveforms by multivariate matching pursuit, which is used in the preprocessing for the inverse solutions. It seems to offer a possibility of tracing the spatial evolution of seizures in time.
An Over-110-GHz-Bandwidth 2:1 Analog Multiplexer in 0.25-μm InP DHBT Technology

This paper presents an over-110-GHz-bandwidth 2:1 analog multiplexer (AMUX) for ultra-broadband digital-to-analog (D/A) conversion subsystems. The AMUX was designed and fabricated by using newly developed $\pmb{0.25-\mu \mathrm{m}}$ -emitter-width InP double heterojunction bipolar transistors (DHBTs), which have a peak $\pmb{f_{\mathrm{T}}}$ and $\pmb{ f\displaystyle \max}$ of 460 and 480 GHz, respectively. The AMUX IC consists of lumped building blocks, including data-input linear buffers, a clock-input limiting buffer, an AMUX core, and an output linear buffer. The measured 3-dB bandwidth for data and clock paths are both over 110 GHz. In addition, it measures and obtains time-domain large-signal sampling operations of up to 180 GS/s. A 224-Gb/s (112-GBaud) four-level pulse-amplitude modulation (PAM4) signal was successfully generated by using this AMUX. To the best of our knowledge, this AMUX IC has the broadest bandwidth and the fastest sampling rate compared with any other previously reported AMUXes.
Towards an intelligent textbook: eye gaze based attention extraction on materials for learning and instruction in physics

In this paper, we propose the attention extraction method on a textbook of physics by using eye tracking glasses. We prepare a document including text and tasks in physics, and record reading behavior on the document from 6-grade students. The result confirms that students pay attention to a different region depending on situations (using the text as a material to learn the content, or using it as hints to solve tasks) and their comprehension.
Unsupervised learning of feature hierarchies

The applicability of machine learning methods is often limi ted by the amount of available labeled data, and by the ability (or inability) of the de signer to produce good internal representations and good similarity measures for the input data vectors. The aim of this thesis is to alleviate these two limitations by proposing al gorithms tolearngood internal representations, and invariant feature hierarchies from u nlabeled data. These methods go beyond traditional supervised learning algorithms, and rely on unsupervised, and semi-supervised learning. In particular, this work focuses on “deep learning” methods , a et of techniques and principles to train hierarchical models. Hierarchical mod els produce feature hierarchies that can capture complex non-linear dependencies among the observed data variables in a concise and efficient manner. After training, these mode ls can be employed in real-time systems because they compute the representation by a very fast forward propagation of the input through a sequence of non-linear transf ormations. When the paucity of labeled data does not allow the use of traditional supervi s d algorithms, each layer of the hierarchy can be trained in sequence starting at the bott om by using unsupervised or semi-supervised algorithms. Once each layer has been train ed, the whole system can be fine-tuned in an end-to-end fashion. We propose several unsu pervised algorithms that can be used as building block to train such feature hierarchi es. We investigate algorithms that produce sparse overcomplete representations a nd fe tures that are invariant to known and learned transformations. These algorithms are designed using the Energy-
Discovery of significant parameters in kidney dialysis data sets by K-means algorithm

The contributing factors for kidney dialysis such as creatinine, sodium, urea plays an important role in deciding the survival prediction of the patients as well as the need for undergoing kidney transplantation. Several attempts have been made to derive automated decision making procedure for earlier prediction. This preliminary study investigates the importance of clustering technique for identifying the influence of kidney dialysis parameters. A simple K-means algorithm is used to elicit knowledge about the interaction between many of these measured parameters and patient survival. The clustering procedure predicts the survival period of the patients who is undergoing the dialysis procedure.
Fully Connected Guided Image Filtering

This paper presents a linear time fully connected guided filter by introducing the minimum spanning tree (MST) to the guided filter (GF). Since the intensity based filtering kernel of GF is apt to overly smooth edges and the fixed-shape local box support region adopted by GF is not geometric-adaptive, our filter introduces an extra spatial term, the tree similarity, to the filtering kernel of GF and substitutes the box window with the implicit support region by establishing all-pairs-connections among pixels in the image and assigning the spatial-intensity-aware similarity to these connections. The adaptive implicit support region composed by the pixels with large kernel weights in the entire image domain has a big advantage over the predefined local box window in presenting the structure of an image for the reason that: 1, MST can efficiently present the structure of an image, 2, the kernel weight of our filter considers the tree distance defined on the MST. Due to these reasons, our filter achieves better edge-preserving results. We demonstrate the strength of the proposed filter in several applications. Experimental results show that our method produces better results than state-of-the-art methods.
Risk-RRT∗: A robot motion planning algorithm for the human robot coexisting environment

In the human robot coexisting environment, to reach the goal efficiently and safely is very meaningful for the mobile service robot. In this paper, a Risk based Rapidly-exploring Random Tree for optimal motion planning (Risk-RRT∗) algorithm is proposed by combining the comfort and collision risk (CCR) map with the RRT∗ algorithm, which provides a variant of the RRT∗ algorithm in the dynamic human robot coexisting environment. In the experiments, the time cost in the navigation process and the length of the trajectory are utilized for the evaluation of the proposed algorithm. A comparison with the Risk-RRT algorithm is carried out and experimental results reveal that our proposed algorithm can achieve a better performance than that of the Risk-RRT in both static and dynamic environments.
Finding Plagiarisms among a Set of Programs with JPlag

JPlag is a web service that finds pairs of similar programs among a given set of programs. It has successfully been used in practice for detecting plagiarisms among student Java program submissions. Support for the languages C, C++ and Scheme is also available. We describe JPlag’s architecture and its comparsion algorithm, which is based on a known one called Greedy String Tiling. Then, the contribution of this paper is threefold: First, an evaluation of JPlag’s performance on several rather different sets of Java programs shows that JPlag is very hard to deceive. More than 90 percent of the 77 plagiarisms within our various benchmark program sets are reliably detected and a majority of the others at least raise suspicion. The run time is just a few seconds for submissions of 100 programs of several hundred lines each. Second, a parameter study shows that the approach is fairly robust with respect to its configuration parameters. Third, we study the kinds of attempts used for disguising plagiarisms, their frequency,
Tag suggestion and localization in user-generated videos based on social knowledge

Nowadays, almost any web site that provides means for sharing user-generated multimedia content, like Flickr, Facebook, YouTube and Vimeo, has tagging functionalities to let users annotate the material that they want to share. The tags are then used to retrieve the uploaded content, and to ease browsing and exploration of these collections, e.g. using tag clouds. However, while tagging a single image is straightforward, and sites like Flickr and Facebook allow also to tag easily portions of the uploaded photos, tagging a video sequence is more cumbersome, so that users just tend to tag the overall content of a video. Moreover, the tagging process is completely manual, and often users tend to spend as few time as possible to annotate the material, resulting in a sparse annotation of the visual content. A semi-automatic process, that helps the users to tag a video sequence would improve the quality of annotations and thus the overall user experience. While research on image tagging has received a considerable attention in the latest years, there are still very few works that address the problem of automatically assigning tags to videos, locating them temporally within the video sequence. In this paper we present a system for video tag suggestion and temporal localization based on collective knowledge and visual similarity of frames. The algorithm suggests new tags that can be associated to a given keyframe exploiting the tags associated to videos and images uploaded to social sites like YouTube and Flickr and visual features.
Cannabidiol: an overview of some pharmacological aspects.

Over the past few years, considerable attention has focused on cannabidiol (CBD), a major nonpsychotropic constituent of cannabis. The authors present a review on the chemistry of CBD and discuss the anticonvulsive, antianxiety, antipsychotic, antinausea, and antirheumatoid arthritic properties of CBD. CBD does not bind to the known cannabinoid receptors, and its mechanism of action is yet unknown. It is possible that, in part at least, its effects are due to its recently discovered inhibition of anandamide uptake and hydrolysis and to its antioxidative effect.
Coupling Matrix Synthesis of Nonreciprocal Lossless Two-Port Networks Using Gyrators and Inverters

A coupling matrix technique is presented for the synthesis of nonreciprocal lossless two-port networks. This technique introduces complex inverters to build the nonreciprocal transversal network corresponding to the nonreciprocal coupling matrix. It subsequently transforms this matrix into canonical topologies through complex similarity transformations. The complex inverters in the final topology are transformed into real inverters and gyrators for implementation simplicity. A second-order network example and a fourth-order network example, as well as an experimental validation, are then provided to illustrate the proposed technique. Finally, other possible implementations of the nonreciprocal gyrators are discussed.
BRUTE: A High Performance and Extensibile Traffic Generator

Evaluating the performance of high speed networks is a critical task due to the lack of reliable tools to generate traffic workloads at high rates and to the inaccessibility of network equipments. To address the problem of reliable device testing, we developed an extensible traffic generator, called BRUTE (Browny and RobUst Traffic Engine), running on top of Linux operating system. BRUTE takes advantage of the Linux kernel potential in order to accurately generate traffic flows up to very high bitrates. This software is an extensible framework that provides a number of optimized functions that easily allow the implementation of ad hoc modules for customized traffic patterns generation. A number of library modules implementing common traffic profiles (like CBR, Poisson process and Poissonian Arrival of Burst process) are already implemented and integrated into the package. Performance of BRUTE has been compared to the one of several widespread traffic generators over Fast-Ethernet and Gigabit-Ethernet networks. The results of our tests show the superior performance of BRUTE in terms of both the achieved throughput and the level of bitrate precision, under all possible values of the frame length.
Kernel regression in mixed feature spaces for spatio-temporal saliency detection

Spatio-temporal saliency detection has attracted lots of research interests due to its competitive performance on wide multimedia applications. For spatio-temporal saliency detection, existing bottom-up algorithms often over-simplify the fusion strategy, which results in the inferior performance than the human vision system. In this paper, a novel bottom-up spatio-temporal saliency model is proposed to improve the accuracy of attentional region estimation in videos through fully exploiting the merit of fusion. In order to represent the space constructed by several types of features such as location, appearance and temporal cues extracted from video, kernel regression in mixed feature spaces (KR-MFS) including three approximation entity-models is proposed. Using KR-MFS, a hybrid fusion strategy which considers the combination of spatial and temporal saliency of each individual unit and incorporates the impacts from the neighboring units is presented and embedded into the spatio-temporal saliency model. The proposed model has been evaluated on the publicly available dataset. Experimental results show that the proposed spatio-temporal saliency model can achieve better performance than the state-of-the-art approaches.
Self-Organization in Biological Systems

This book deals with self-organization in biological systems and constitutes a much-needed supplement to the mainstream treatment of behavioural ecology by Krebs & Davies (1997). In combination with Termites, Turtles and Traffic Jams by Michel Resnick (1994), it is an excellent textbook for courses on self-organized biological and social systems. In an admirable way it combines detailed experiments with mathematical models of behavioural ecology. Models based on differential equations are supplemented by individual-based models in Starlogo which can be downloaded from Camazine’s web site. Examples mainly concern social insects (such as ants and bees), but there are also chapters devoted to bacterial aggregates and fish shoals. The first seven chapters describe and analyse selforganization. Examples of self-organized behaviour are set against the usual explanations, which involve a leader, a blueprint, a recipe and a template. The authors also indicate how self-organization of behaviour may be studied experimentally, mathematically and by the combination of the two methods (Chapter 6). They introduce various types of models. As regards the evolution of behaviour, they suggest that self-organization and natural selection act together and that it is through selforganization that evolution becomes truly efficient (Chapter 7). Chapters 8–20 illustrate the process of self-organization with examples of grouping, foraging, dominance hierarchies, behavioural synchronization, building of nests and thermoregulation of swarms. The authors emphasize the operation of positive and negative feedback in all these processes. Chapter 8 shows the importance of chemotaxis (following increased concentrations of a chemical) in the life cycle of the famous slime mould, which alternates between phases in which single amoebae are solitary and ‘collective’ phases in which a great number of amoebae form a single, slug-like body. They describe, for instance, how a positive feedback mechanism brings (hungry) amoebae together and how this is accompanied by waves of c-AMP which may be concentric or have a spiral form (a curious difference that unfortunately is not explained). Three of the following chapters (9, 13 and 14) continue with the effects of chemotaxis, which may lead to the clustering of beetle larvae (Chapter 9), to the efficient way in which ants forage (Chapter 13) and to the complex patterns of swarming of the huge colonies of army ants (Chapter 14). In all this both a positive and a negative feedback are involved. The positive one implies that a larger group of organisms attracts more members, because
Automatic extraction of printed mathematical formulas using fuzzy logic and propagation of context

This paper describes a new method to segment printed mathematical documents precisely and extract formulas automatically from their images. Unlike classical methods, it is more directed towards segmentation rather than recognition, isolating mathematical formulas outside and inside text-lines. Our ultimate goal is to delimit parts of text that could disturb OCR applications, not yet trained for formula recognition and restructuring. The method is based on a global and a local segmentation. The global segmentation separates isolated formulas from the text lines using a primary labeling. The local segmentation propagates the context around the mathematical operators met to discard embedded formulas from plain text. The primary labeling identifies some mathematical symbols by models created at a learning step using fuzzy logic. The secondary labeling reinforces the results of the primary labeling and locates the subscripts and the superscripts inside the text. A heuristic has been defined that guides this automatic process. In this paper, the different modules making up the automated segmentation of mathematical document system are presented with examples of results. Experiments carried out on some commonly seen mathematical documents show that our proposed method can achieve quite satisfactory rates, making mathematical formula extraction more feasible for real-world applications. The average rate of primary labeling of mathematical operators is about 95.3% and their secondary labeling can improve the rate by about 4%. The formula extraction rate, evaluated with 300 formulas and 100 mathematical documents having variable complexity, is close to 93%
Intelligent Access to Digital Video: Informedia Project

Computer he Informedia Digital Video Library project' will establish a large, on-line digital video library featuring full-content and knowledge-based search and retrieval. Intelligent, automatic mecha nisms will be developed to populate the library. Search and retrieval from digital video, audio, and text libraries will take place via desktop computer over local-, metropolitan-, and wide-area networks. Initially, the library will be populated with 1,000 hours of raw and edited documentary and education videos drawn from video assets of WQED/Pittsburgh, Fairfax County (Virginia) Public Schools, and the Open University (United Kingdom). To assess the value of video reference libraries for enhanced learning at different ages, we will deploy the library at Carnegie Mellon University and local schools, from elementary school through high school. Our approach applies several techniques for content-based searching and video-sequence retrieval. Content is conveyed in both the narrative (speech and language) and the image. Only by the collaborative interaction of image, speech, and natural-language understanding technology can we successfully populate, segment, index, and search diverse video collections with satisfactory recall and precision. This collaborative interaction approach uniquely compensates for problems of interpretation and search in error-ridden and ambiguous data sets. We start with a highly accurate, speaker-independent, connected speech recognizer that automatically transcribes video soundtracks. A language-understanding system then analyzes and organizes the transcript and stores it in a full-text information retrieval system. This text database permits rapid retrieval of individual video segments that satisfy an arbitrary query on the basis of the words in the soundtrack and in associated annotations and credits. Image and language understanding lets us locate and delineate the corresponding "video paragraph" context through combined source information about camera cuts, object tracking, speaker changes, timing of audio and/or background music, and change in content of spoken words. Controls let the user interactively request corresponding video paragraphs to full volumes, browse the results, intelligently "skim" the returned content, and reuse the stored video objects in different ways.  1 illustrates a typical user retrieval display. The data and network architecture we have implemented provides a distributed data multilevel hierarchy and enables networking on commercial data services. To protect data rights in intellectual property and to provide security and privacy, we've incorporated network billing, variable pricing, and access control. All digital libraries share common technical and sociological issues, attributes, features, and challenges 2 The digital video library exacerbates many of these problems. Moreover, it generates new research challenges across diverse disciplines, beginningwith automated techniques to derive
Sampling Generative Networks: Notes on a Few Effective Techniques

We introduce several techniques for sampling and visualizing the latent spaces of generative models. Replacing linear interpolation with spherical linear interpolation prevents diverging from a model’s prior distribution and produces sharper samples. J-Diagrams and MINE grids are introduced as visualizations of manifolds created by analogies and nearest neighbors. We demonstrate two new techniques for deriving attribute vectors: bias-corrected vectors with data replication and synthetic vectors with data augmentation. Most techniques are intended to be independent of model type and examples are shown on both Variational Autoencoders and Generative Adversarial Networks. Generative; VAE; GAN; Sampling; Manifold
General 3D modelling of novel objects from a single view

In this paper we present a method for building models for grasping from a single 3D snapshot of a scene composed of objects of daily use in human living environments. We employ fast shape estimation, probabilistic model fitting and verification methods capable of dealing with different kinds of symmetries, and combine these with a triangular mesh of the parts that have no other representation to model previously unseen objects of arbitrary shape. Our approach is enhanced by the information given by the geometric clues about different parts of objects which serve as prior information for the selection of the appropriate reconstruction method. While we designed our system for grasping based on single view 3D data, its generality allows us to also use the combination of multiple views. We present two application scenarios that require complete geometric models: grasp planning and locating objects in camera images.