nils-herrmann/clean_scirepeval huggingface dataset:
A 1.7 ps Equivalent Bin Size and 4.2 ps RMS FPGA TDC Based on Multichain Measurements Averaging Method

A high precision and high resolution time-to-digital converter (TDC) based on multichain measurements averaging method is implemented in a 40 nm fabrication process Virtex-6 FPGA. The results of the detailed theoretical analysis and the simulation with the MATLAB tool based on a complete TDC module show that the resolution limitation determined by the intrinsic cell delay of plain tapped-delay chain can be overcame, which results in an improvement on both resolution and precision without increasing the dead time. The test results agree with the simulation results quite well. In such a TDC, the input signal is connected to multiple tapped-delay chains simultaneously (the number of the chains is M), and each chain is just a plain TDC and generates a timestamp for a hit signal. Therefore, M timestamps should be obtained in total, which, after averaging, give the final timestamp. A TDC with 1.7 ps equivalent bin size, 1.5 ps averaged bin size and 4.2 ps RMS has been implemented with M being 16, which performs much better than the plain TDC constructed of a single tapped delay chain having 42.3 ps equivalent bin size, 24.0 ps averaged bin size resolution and 13.2 ps RMS precision. The comparisons of equivalent bin size and averaged bin size show that the nonlinearity is improved with a larger M. Due to the real time integral nonlinearity (INL) calibration and averaging calculation, the multichain TDC is almost insensitive to the process voltage and temperature (PVT) variations.
Reference configurations for ear-EEG steady-state responses

Ear-EEG is a non-invasive EEG recording method, where EEG is recorded from electrodes placed in the ear. Ear-EEG could be implemented into hearing aids, and provide neurofeedback for e.g. objective hearing assessment through measurements of the auditory steady-state response. In cases where the objective is to measure a specific feature of an event-related potential, there will be a subject specific optimal reference configuration. This work presents a method for optimizing the reference configuration for steady-state type potentials. For given electrode positions, the method maximizes the signal-to-noise (SNR) ratio of the first harmonic of the steady-state response. This is obtained by estimating a set of weights applied to the electrode signals. The method was validated on a dataset recorded from 12 subjects. The weights were estimated from one part of the dataset, and the validation was performed on another part of the dataset. For all subjects the proposed method demonstrated a robust SNR estimate, yielding on par or better SNR compared to other well-known methods.
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks. The only underlying LSTM structure that has been explored so far is a linear chain. However, natural language exhibits syntactic properties that would naturally combine words to phrases. We introduce the Tree-LSTM, a generalization of LSTMs to tree-structured network topologies. TreeLSTMs outperform all existing systems and strong LSTM baselines on two tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task 1) and sentiment classification (Stanford Sentiment Treebank).
Variable Rate Image Compression with Recurrent Neural Networks

Although image compression has been actively studied for decades, there has been relatively little research on learning to compress images with modern neural networks. Standard approaches, such as those employing patch-based autoencoders, have shown a great deal of promise but cannot compete with popular image codecs because they fail to address three questions: 1) how to effectively binarize activations: in the absence of binarization, a bottleneck layer alone tends not to lead to efficient compression; 2) how to achieve variable-rate encoding: a standard autoencoder generates a fixed-length code for each fixed-resolution input patch, resulting in the same cost for lowand high-entropy patches, and requiring the network to be completely retrained to achieve different compression rates; and 3) how to avoid block artifacts: patch-based approaches are prone to block discontinuities. We propose a general framework for variable-rate image compression and a novel architecture based on convolutional and deconvolutional recurrent networks, including LSTMs, that address these issues and report promising results compared to existing baseline codecs. We evaluate the proposed methods on a large-scale benchmark consisting of tiny images (32× 32), which proves to be very challenging for all the methods.
Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language

This article presents a measure of semantic similarity in an is-a taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their e ectiveness.
Image Sharpening via Sobolev Gradient Flows

Motivated by some recent work in active contour applications, we study the use of Sobolev gradients for PDE-based image diffusion and sharpening. We begin by studying, for the case of isotropic diffusion, the gradient descent/ascent equation obtained by modifying the usual metric on the space of images, which is the L metric, to a Sobolev metric. We present existence and uniqueness results for the Sobolev isotropic diffusion, derive a number of maximum principles, and show that the differential equations are stable and well-posed both in the forward and backward directions. This allows us to apply the Sobolev flow in the backward direction for sharpening. Favorable comparisons to the well-known Shock Filter for sharpening are demonstrated. Finally, we continue to exploit this same well posed behavior both forward and backward in order to formulate new constrained gradient flows on higher order energy functionals which preserve the first order energy of the original image for interesting combined smoothing and sharpening effects.
Internal Uniplanar Antenna for LTE/GSM/UMTS Operation in a Tablet Computer

With the use of an internal printed loop matching circuit, a small-size uniplanar antenna with eight-band LTE/GSM/UMTS operation in the tablet computer is proposed. The obtained impedance bandwidth across the operating bands can reach about 262/1610 MHz for the LTE/GSM/UMTS bands, respectively. Only with the antenna size of $40\times 15\times 0.8\ {\rm mm}^{3}$, the proposed planar antenna has the compact operation with more than 30% antenna size reduction. Furthermore, the measured peak gains and antenna efficiencies are about 2.81/4.97 dBi and 79/87% for the LTE/GSM/UMTS bands, respectively.
Advanced Cooling for Power Electronics

Power electronics devices such as MOSFET's, GTO's, IGBT's, IGCT's etc. are now widely used to efficiently deliver electrical power in home electronics, industrial drives, telecommunication, transport, electric grid and numerous other applications. This paper discusses cooling technologies that have evolved in step to remove increasing levels of heat dissipation and manage junction temperatures to achieve goals for efficiency, cost, and reliability. Cooling technologies rely on heat spreading and convection. In applications that use natural or forced air cooling, water heat pipes provide efficient heat spreading for size and weight reduction. Previous concepts are reviewed and an improved heat sink concept with staggered fin density is described for more isothermal cooling. Where gravity can drive liquid flow, thermosiphons provide efficient heat transport to remote fin volumes that can be oriented for natural and/or forced air cooling. Liquid cold plates (LCP's) offer the means to cool high heat loads and heat fluxes including double sided cooling for the highest density packaging. LCP's can be used both in single phase cooling systems with aqueous or oil based coolants and in two-phase cooling systems with dielectric fluids and refrigerants. Previous concepts are reviewed and new concepts including an air cooled heat sink, a thermosiphon heat sink, a vortex flow LCP and a shear flow direct contact cooling concept are described.
Dynamic path-based software watermarking

Software watermarking is a tool used to combat software piracy by embedding identifying information into a program. Most existing proposals for software watermarking have the shortcoming that the mark can be destroyed via fairly straightforward semantics-preserving code transformations. This paper introduces path-based watermarking, a new approach to software watermarking based on the dynamic branching behavior of programs. The advantage of this technique is that error-correcting and tamper-proofing techniques can be used to make path-based watermarks resilient against a wide variety of attacks. Experimental results, using both Java bytecode and IA-32 native code, indicate that even relatively large watermarks can be embedded into programs at modest cost.
Iterative (turbo) soft interference cancellation and decoding for coded CDMA

The presence of both multiple-access interference (MAI) and intersymbol interference (ISI) constitutes a major impediment to reliable communications in multipath code-division multiple-access (CDMA) channels. In this paper, an iterative receiver structure is proposed for decoding multiuser information data in a convolutionally coded asynchronous multipath DS-CDMA system. The receiver performs two successive softoutput decisions, achieved by a soft-input soft-output (SISO) multiuser detector and a bank of single-user SISO channel decoders, through an iterative process. At each iteration, extrinsic information is extracted from detection and decoding stages and is then used asa priori information in the next iteration, just as in Turbo decoding. Given the multipath CDMA channel model, a direct implementation of a sliding-window SISO multiuser detector has a prohibitive computational complexity. A low-complexity SISO multiuser detector is developed based on a novel nonlinear interference suppression technique, which makes use of both soft interference cancellation and instantaneous linear minimum mean-square error filtering. The properties of such a nonlinear interference suppressor are examined, and an efficient recursive implementation is derived. Simulation results demonstrate that the proposed low-complexity iterative receiver structure for interference suppression and decoding offers significant performance gain over the traditional noniterative receiver structure. Moreover, at high signal-to-noise ratio, the detrimental effects of MAI and ISI in the channel can almost be completely overcome by iterative processing, and single-user performance can be approached.
A framework for facial surgery simulation

The accurate prediction of the post-surgical facial shape is of paramount importance for surgical planning in facial surgery. In this paper we present a framework for facial surgery simulation which is based on volumetric finite element modeling. We contrast conventional procedures for surgical planning against our system by accompanying a patient during the entire process of planning, medical treatment and simulation. In various preprocessing steps a 3D physically based facial model is reconstructed from CT and laser range scans. All geometric and topological changes are modeled interactively using Alias.#8482; Applying fully 3D volumetric elasticity allows us to represent important volumetric effects such as incompressibility in a natural and physically accurate way. For computational efficiency, we devised a novel set of prismatic shape functions featuring a globally C1-continuous surface in combination with a C0 interior. Not only is it numerically accurate, but this construction enables us to compute smooth and visually appealing facial shapes.
ODELS WITH A DVERSARIAL T RAINING

We introduce causal implicit generative models (CiGMs): models that allow sampling from not only the true observational but also the true interventional distributions. We show that adversarial training can be used to learn a CiGM, if the generator architecture is structured based on a given causal graph. We consider the application of conditional and interventional sampling of face images with binary feature labels, such as mustache, young. We preserve the dependency structure between the labels with a given causal graph. We devise a two-stage procedure for learning a CiGM over the labels and the image. First we train a CiGM over the binary labels using a Wasserstein GAN where the generator neural network is consistent with the causal graph between the labels. Later, we combine this with a conditional GAN to generate images conditioned on the binary labels. We propose two new conditional GAN architectures: CausalGAN and CausalBEGAN. We show that the optimal generator of the CausalGAN, given the labels, samples from the image distributions conditioned on these labels. The conditional GAN combined with a trained CiGM for the labels is then a CiGM over the labels and the generated image. We show that the proposed architectures can be used to sample from observational and interventional image distributions, even for interventions which do not naturally occur in the dataset.
Image segmentation and analysis via multiscale gradient watershed hierarchies

Multiscale image analysis has been used successfully in a number of applications to classify image features according to their relative scales. As a consequence, much has been learned about the scale-space behavior of intensity extrema, edges, intensity ridges, and grey-level blobs. We investigate the multiscale behavior of gradient watershed regions. These regions are defined in terms of the gradient properties of the gradient magnitude of the original image. Boundaries of gradient watershed regions correspond to the edges of objects in an image. Multiscale analysis of intensity minima in the gradient magnitude image provides a mechanism for imposing a scale-based hierarchy on the watersheds associated with these minima. This hierarchy can be used to label watershed boundaries according to their scale. This provides valuable insight into the multiscale properties of edges in an image without following these curves through scale-space. In addition, the gradient watershed region hierarchy can be used for automatic or interactive image segmentation. By selecting subtrees of the region hierarchy, visually sensible objects in an image can be easily constructed.
An isolated bipolar DC-DC converter for energy storage integration in marine vessels

Integrated power systems (IPSs) with medium voltage direct current (MVDC) distribution are gaining importance in civil and defense marine vessels as they promise to provide cleaner, more reliable operation of vessels along with reduced fuel consumption. The integration of battery energy storage systems (BESSs) in the MVDC distribution system enables peak shaving of generators, optimal scheduling of generators, and near instantaneous power reserve. In this paper, an isolated bipolar dc-dc converter is presented which interfaces the BESS with the bipolar dc distribution bus. The proposed converter uses high frequency transformer isolation which helps in protecting the BESS against rapid discharge in the event of short circuit faults on the dc bus. Detailed steady-state analysis of the proposed converter is presented in this paper. A closed loop control system is developed in the MATLAB®/Simulink simulation platform which regulates the output of the isolated bipolar converter to its reference value. The dynamic performance of the converter is demonstrated with load variations on both the positive and negative buses. The battery discharge limiting capability is also verified.
SOME INTERESTING DIFFERENCES BETWEEN VERBS AND NOUNS

Verbs are different from;nouns in ways that go beyond chair syntactic privileges . Verbs are harder to remember, more broadly defined, more prone to be altered in meaning when conflict of meaning occurs, less stable in translation between languages, and slower to be acquired by children than nouns . In this paper I argue that the differences stem in part from a basic cognitive distinction that, in perceptual domains, is correlated with the noun-verb distinctioq :. the distinction between object-reference concepts and relational concepts .' Object-reference concepts are typically lexicalized as concrete or proper nouns such as dog, collie, or Lassie . Relational concepts from the same concrete level a' ' typically lexicalized as predicates, usually verbs (e.g ., push, float, or move) or prepositions
Assuring Resilience, Security and Privacy for Flexible Networked Systems and Organisations (Dagstuhl Seminar 15151)

Dagstuhl Seminar 15151 entitled “Assuring Resilience, Security and Privacy for Flexible Networked Systems and Organisations” brought together researchers from different disciplines in order to establish a research agenda for making future services in our increasingly connected world more resilient and secure, as well as addressing privacy. The participants came from a range of disciplines covering the techno-legal domain, resilience and systems security, and sociotechnical concerns. The use case domains that were discussed during the Seminar covered the Internet of Things (IoT) as well as Cloud-based applications in which flexible service composition is a crucial element. From a starting point covering the “big picture”, the legal viewpoint, the technical viewpoint, and the organisational viewpoint, we derived initial research questions in small groups, and the questions and issues arising were then consolidated and refined. The groups discussed the issues in depth and have produced the report and the research agenda contained here. Seminar April 7–10, 2015 – http://www.dagstuhl.de/15151 1998
FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining

This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.
Common mode noise reduction in totem-pole bridgeless PFC converter

Since the loss of input rectifying diode bridge in the conventional PFC converter reduces the efficiency, the bridgeless PFC converter has been proposed. The totem-pole bridgeless PFC converter is the most representative one. However the common mode noise current occurring around the input voltage zero-crossing brings severe EMI problem restricts its practical application. In order to improve noise performance, the noise mechanism should be investigated as the first step. In this paper, common mode noise current of totem-pole bridgeless PFC circuit has been analyzed. Furthermore, noise reduction based on the LC resonance is proposed. A 100V ac input, 395V dc output, 100kHz switching frequency experimental circuit has been built. By applying the noise reduction, the common mode noise can be suppressed efficiently.
Discriminative Regularization for Generative Models

We explore the question of whether the representations learned by classifiers can be used to enhance the quality of generative models. Our conjecture is that labels correspond to characteristics of natural data which are most salient to humans: identity in faces, objects in images, and utterances in speech. We propose to take advantage of this by using the representations from discriminative classifiers to augment the objective function corresponding to a generative model. In particular we enhance the objective function of the variational autoencoder, a popular generative model, with a discriminative regularization term. We show that enhancing the objective function in this way leads to samples that are clearer and have higher visual quality than the samples from the standard variational autoencoders.
Dynamic Modelling of Differential-Drive Mobile Robots using Lagrange and Newton-Euler Methodologies: A Unified Framework

In recent years, there has been a considerable interest in the area of mobile robotics and educational technologies [1-7]. For control engineers and researchers, there is a wealth of literature dealing with wheeled mobile robots (WMR) control and their applications. However, while the subject of kinematic modeling of WMR is well documented and easily understood by students, the subject of dynamic modeling of WMR has not been addressed adequately in the literature. The dynamics of WMR are highly nonlinear and involve non-holonomic constraints which makes difficult their modeling and analysis especially for new engineering students starting their research in this field. Therefore, a detailed and accurate dynamic model describing the WMR motion need to be developed to offer students a general framework for simulation analysis and model based control system design.
Mining Vessel Tracking Data for Maritime Domain Applications

The growing number of remote sensing systems and ship reporting technologies (e.g. Automatic Identification System, Long Range Identification and Tracking, radar tracking, Earth Observation) are generating an overwhelming amount of spatio-temporal and geographically distributed data related to vessels and their movements. Research on reliable data mining techniques has proven essential to the discovery of knowledge from such increasingly available information on ship traffic at sea. Data driven knowledge discovery has very recently demonstrated its value in fields that go beyond the original maritime safety and security remits of such data. They include, but are not limited to, fisheries management, maritime spatial planning, gridding ship emissions, mapping activities at sea, risk assessment of offshore platforms, and trade indicators. The extraction of useful information from maritime Big Data is thus a key element in providing operational authorities, policy-makers and scientists with supporting tools to understand what is happening at sea and improve maritime knowledge. This work will provide a survey of the recent JRC research activities relevant to automatic anomaly detection and knowledge discovery in the maritime domain. Data mining, data analytics and predictive analysis examples are introduced using real data. In addition, this paper presents approaches to detect anomalies in reporting messages and unexpected behaviours at sea.