nils-herrmann/clean_scirepeval huggingface dataset:
Design and Analysis of Compact Printed Triple Band-Notched UWB Antenna

A compact printed microstrip-fed monopole ultrawideband antenna with triple notched bands is presented and analyzed in detail. A straight, open-ended quarter-wavelength slot is etched in the radiating patch to create the first notched band in 3.3-3.7 GHz for the WiMAX system. In addition, three semicircular half-wavelength slots are cut in the radiating patch to generate the second and third notched bands in 5.15-5.825 GHz for WLAN and 7.25-7.75 GHz for downlink of X-band satellite communication systems. Surface current distributions and transmission line models are used to analyze the effect of these slots. The antenna is successfully fabricated and measured, showing broad band matched impedance and good omnidirectional radiation pattern. The designed antenna has a compact size of 25 × 29 mm2.
Modelling and Control of a Large Quadrotor Robot

Typical quadrotor aerial robots used in research weigh less than 3 kg and carry payloads measured in hundreds of grams. Several obstacles in design and control must be overcome to cater for expected industry demands that push the boundaries of existing quadrotor performance. The X-4 Flyer, a 4 kg quadrotor with a 1 kg payload, is intended to be prototypical of useful commercial quadrotors. The custom-built craft uses tuned plant dynamics with an onboard embedded attitude controller to stabilise flight. Independent linear SISO controllers were designed to regulate flyer attitude. The performance of the system is demonstrated in indoor and outdoor flight.
Multimodal representation learning with neural networks

Representation learning methods have received a lot of attention by researchers and practitioners because of their successful application to complex problems in areas such as computer vision, speech recognition and text processing . Many of these promising results are due to the development of methods to automatically learn the representation of complex objects directly from large amounts of sample data . These efforts have concentrated on data involving one type of information (images, text, speech, etc.), despite data being naturally multimodal. Multimodality refers to the fact that the same real-world concept can be described by different views or data types. Addressing multimodal automatic analysis faces three main challenges: feature learning and extraction, modeling of relationships between data modalities and scalability to large multimodal collections [3, 4]. This research considers the problem of leveraging multiple sources of information or data modalities in neural networks. It defines a novel model called gated multimodal unit (GMU), designed as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities. The GMU learns to decide how modalities influence the activation of the unit using multiplicative gates. The GMU can be used as a building block for different kinds of neural networks and can be seen as a form of intermediate fusion. The model was evaluated on four supervised learning tasks in conjunction with fully-connected and convolutional neural networks. We compare the GMU with other early and late fusion methods, outperforming classification scores in the evaluated datasets. Strategies to understand how the model gives importance to each input were also explored. By measuring correlation between gate activations and predictions, we were able to associate modalities with classes. It was found that some classes were more correlated with some particular modality. Interesting findings in genre prediction show, for instance, that the model associates the visual information with animation movies while textual information is more associated with drama or romance movies. During the development of this project, three new benchmark datasets were built and publicly released. The BCDR-F03 dataset which contains 736 mammography images and serves as benchmark for mass lesion classification. The MM-IMDb dataset containing around 27000 movie plots, poster along with 50 metadata annotations and that motivates new research in multimodal analysis. And the Goodreads dataset, a collection of 1000 books that encourages the research on success prediction based on the book content. This research also facilitates reproducibility of the present work by releasing source code implementation of the proposed methods.
Measuring the Intrinsic Dimension of Objective Landscapes

Many recently trained neural networks employ large numbers of parameters to achieve good performance. One may intuitively use the number of parameters required as a rough gauge of the difficulty of a problem. But how accurate are such notions? How many parameters are really needed? In this paper we attempt to answer this question by training networks not in their native parameter space, but instead in a smaller, randomly oriented subspace. We slowly increase the dimension of this subspace, note at which dimension solutions first appear, and define this to be the intrinsic dimension of the objective landscape. The approach is simple to implement, computationally tractable, and produces several suggestive conclusions. Many problems have smaller intrinsic dimensions than one might suspect, and the intrinsic dimension for a given dataset varies little across a family of models with vastly different sizes. This latter result has the profound implication that once a parameter space is large enough to solve a problem, extra parameters serve directly to increase the dimensionality of the solution manifold. Intrinsic dimension allows some quantitative comparison of problem difficulty across supervised, reinforcement, and other types of learning where we conclude, for example, that solving the inverted pendulum problem is 100 times easier than classifying digits from MNIST, and playing Atari Pong from pixels is about as hard as classifying CIFAR-10. In addition to providing new cartography of the objective landscapes wandered by parameterized models, the method is a simple technique for constructively obtaining an upper bound on the minimum description length of a solution. A byproduct of this construction is a simple approach for compressing networks, in some cases by more than 100 times.
Current-Mode CMOS Image Sensor

This thesis presents a current-mode CMOS image sensor using lateral bipolar phototransistors (LPTs). The objective of this design is to improve the photosensitivity of the image sensor, and to provide photocurrent amplification at the circuit level. Lateral bipolar phototransistors can be implemented using a standard CMOS technology with no process modification. Under illumination, photogenerated carriers contribute to the base current, and the output emitter current is amplified through the transistor action of the bipolar device. Our analysis and simulation results suggest that the LPT output characteristics are strongly dependent on process parameters including base and emitter doping concentrations, as well as the device geometry such as the base width. For high current gain, a minimized base width is desired. The 2D effect of current crowding has also been discussed. Photocurrent can be further increased using amplifying current mirrors in the pixel and column structures. A prototype image sensor has been designed and fabricated in a standard 0.18μm CMOS technology. This design includes a photodiode image array and a LPT image array, each 70× 48 in dimension. For both arrays, amplifying current mirrors are included in the pixel readout structure and at the column level. Test results show improvements in both photosensitivity and conversion efficiency. The LPT also exhibits a better spectral response in the red region of the spectrum, because of the nwell/p-substrate depletion region. On the other hand, dark current, fixed pattern noise (FPN), and power consumption also increase due to current amplification. This thesis has demonstrated that the use of lateral bipolar phototransistors and amplifying current mirrors can help to overcome low photosensitivity and other deterioration imposed by technology scaling. The current-mode readout scheme with LPT-based photodetectors can be used as a front end to additional image processing circuits.
A Fast Face Detection Method via Convolutional Neural Network

Current face or object detection methods via convolutional neural network (such as OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features based on an image pyramid. However, such a strategy increases the computational burden for face detection. In this paper, we propose a fast face detection method based on discriminative complete features (DCFs) extracted by an elaborately designed convolutional neural network, where face detection is directly performed on the complete feature maps. DCFs have shown the ability of scale invariance, which is beneficial for face detection with high speed and promising performance. Therefore, extracting multi-scale features on an image pyramid employed in the conventional methods is not required in the proposed method, which can greatly improve its efficiency for face detection. Experimental results on several popular face detection datasets show the efficiency and the effectiveness of the proposed method for face detection.
Cannabis sativa: The Plant of the Thousand and One Molecules

Cannabis sativa L. is an important herbaceous species originating from Central Asia, which has been used in folk medicine and as a source of textile fiber since the dawn of times. This fast-growing plant has recently seen a resurgence of interest because of its multi-purpose applications: it is indeed a treasure trove of phytochemicals and a rich source of both cellulosic and woody fibers. Equally highly interested in this plant are the pharmaceutical and construction sectors, since its metabolites show potent bioactivities on human health and its outer and inner stem tissues can be used to make bioplastics and concrete-like material, respectively. In this review, the rich spectrum of hemp phytochemicals is discussed by putting a special emphasis on molecules of industrial interest, including cannabinoids, terpenes and phenolic compounds, and their biosynthetic routes. Cannabinoids represent the most studied group of compounds, mainly due to their wide range of pharmaceutical effects in humans, including psychotropic activities. The therapeutic and commercial interests of some terpenes and phenolic compounds, and in particular stilbenoids and lignans, are also highlighted in view of the most recent literature data. Biotechnological avenues to enhance the production and bioactivity of hemp secondary metabolites are proposed by discussing the power of plant genetic engineering and tissue culture. In particular two systems are reviewed, i.e., cell suspension and hairy root cultures. Additionally, an entire section is devoted to hemp trichomes, in the light of their importance as phytochemical factories. Ultimately, prospects on the benefits linked to the use of the -omics technologies, such as metabolomics and transcriptomics to speed up the identification and the large-scale production of lead agents from bioengineered Cannabis cell culture, are presented.
Internet of things: from internet scale sensing to smart services

The internet of things (IoT) is the latest web evolution that incorporates billions of devices (such as cameras, sensors, RFIDs, smart phones, and wearables), that are owned by different organizations and people who are deploying and using them for their own purposes. Federations of such IoT devices (we refer to as IoT things) can deliver the information needed to solve internet-scale problems that have been too difficult to obtain and harness before. To realize this unprecedented IoT potential, we need to develop IoT solutions for discovering the IoT devices each application needs, collecting and integrating their data, and distilling the high value information each application needs. We also need to provide solutions that permit doing these tasks in real-time, on the move, in the cloud, and securely. In this paper we present an overview of a collection of IoT solutions (which we have developed in partnerships with other prominent IoT innovators and refer to them collectively as IoT platform) for addressing these technical challenges and help springboard IoT to its potential. We also describe a variety of IoT applications that have utilized the proposed IoT platform to provide smart IoT services in the areas of smart farming, smart grids, and smart manufacturing. Finally, we discuss future research and a vision of the next generation IoT infrastructure.
Learning solutions to partial differential equations using LS-SVM

This paper proposes an approach based on Least Squares Suppo rt Vect r Machines (LS-SVMs) for solving second order parti al differential equations (PDEs) with variable coe fficients. Contrary to most existing techniques, the proposed m thod provides a closed form approximate solution. The optimal representat ion of the solution is obtained in the primal-dual setting. T he model is built by incorporating the initial /boundary conditions as constraints of an optimization prob lem. The developed method is well suited for problems involving singular, variable and const a t coefficients as well as problems with irregular geometrical domai ns. Numerical results for linear and nonlinear PDEs demonstrat e he efficiency of the proposed method over existing methods.
Assessment of Jeepney ’ s Components , Systems and Separate Technical Units for the Development of Standards

This established database of manufacturers and thei r design specification, determined the condition and design of the vehicle based on the perception and preference of jeepney drivers and passengers, and compared the pa rts of the jeepney vehicle using Philippine National Standards and international sta ndards. The study revealed that most jeepney manufacturing firms have varied specificati ons with regard to the capacity, dimensions and weight of the vehicle and similar sp ecification on the parts and equipment of the jeepney vehicle. Most of the jeepney drivers an d passengers want to improve, change and standardize the parts of the jeepney vehicle. The p arts of jeepney vehicles have similar specifications compared to the 4 out of 5 mandatory PNS and 22 out 32 UNECE Regulations applicable for jeepney vehicle. It is concluded tha t t e jeepney vehicle can be standardized in terms of design, safety and environmental concerns.
A Realistic Experimental Comparison of the Suricata and Snort Intrusion-Detection Systems

The Suricata intrusion-detection system for computer-network monitoring has been advanced as an open-source improvement on the popular Snort system that has been available for over a decade. Suricata includes multi-threading to improve processing speed beyond Snort. Previous work comparing the two products has not used a real-world setting. We did this and evaluated the speed, memory requirements, and accuracy of the detection engines in three kinds of experiments: (1) on the full traffic of our school as observed on its " backbone" in real time, (2) on a supercomputer with packets recorded from the backbone, and (3) in response to malicious packets sent by a red-teaming product. We used the same set of rules for both products with a few small exceptions where capabilities were missing. We conclude that Suricata can handle larger volumes of traffic than Snort with similar accuracy, and that its performance scaled roughly linearly with the number of processors up to 48. We observed no significant speed or accuracy advantage of Suricata over Snort in its current state, but it is still being developed. Our methodology should be useful for comparing other intrusion-detection products.
A computational intelligence scheme for the prediction of the daily peak load

Forecasting of future electricity demand is very important for decision making in power system operation and planning. In recent years, due to privatization and deregulation of the power industry, accurate electricity forecasting has become an important research area for efficient electricity production. This paper presents a time series approach for mid-term load forecasting (MTLF) in order to predict the daily peak load for the next month. The proposed method employs a computational intelligence scheme based on the self-organizing map (SOM) and support vector machine (SVM). According to the similarity degree of the time series load data, SOM is used as a clustering tool to cluster the training data into two subsets, using the Kohonen rule. As a novel machine learning technique, the support vector regression (SVR) is used to fit the testing data based on the clustered subsets, for predicting the daily peak load. Our proposed SOM-SVR load forecasting model is evaluated in MATLAB on the electricity load dataset provided by the Eastern Slovakian Electricity Corporation, which was used in the 2001 European Network on Intelligent Technologies (EUNITE) load forecasting competition. Power load data obtained from (i) Tenaga Nasional Berhad (TNB) for peninsular Malaysia and (ii) PJM for the eastern interconnection grid of the United States of America is used to benchmark the performance of our proposed model. Experimental results obtained indicate that our proposed SOM-SVR technique gives significantly good prediction accuracy for MTLF compared to previously researched findings using the EUNITE, Malaysian and PJM electricity load
Fine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph Completion

Over the recent years, embedding methods have attracted increasing focus as a means for knowledge graph completion. Similarly, rule-based systems have been studied for this task in the past. What is missing so far is a common evaluation that includes more than one type of method. We close this gap by comparing representatives of both types of systems in a frequently used evaluation protocol. Leveraging the explanatory qualities of rule-based systems, we present a fine-grained evaluation that gives insight into characteristics of the most popular datasets and points out the different strengths and shortcomings of the examined approaches. Our results show that models such as TransE, RESCAL or HolE have problems in solving certain types of completion tasks that can be solved by a rulebased approach with high precision. At the same time, there are other completion tasks that are difficult for rule-based systems. Motivated by these insights, we combine both families of approaches via ensemble learning. The results support our assumption that the two methods complement each other in a beneficial way.
FinFET Circuit Design

Fin-type field-effect transistors (FinFETs) are promising substitutes for bulk CMOS at the nanoscale. FinFETs are double-gate devices. The two gates of a FinFET can either be shorted for higher perfomance or independently controlled for lower leakage or reduced transistor count. This gives rise to a rich design space. This chapter provides an introduction to various interesting FinFET logic design styles, novel circuit designs, and layout considerations.
Zero-shot semantic parser for spoken language understanding

Machine learning algorithms are now common in the state-ofthe-art spoken language understanding models. But to reach good performance they must be trained on a potentially large amount of data which are not available for a variety of tasks and languages of interest. In this work, we present a novel zero-shot learning method, based on word embeddings, allowing to derive a full semantic parser for spoken language understanding. No annotated in-context data are needed, the ontological description of the target domain and generic word embedding features (learned from freely available general domain data) suffice to derive the model. Two versions are studied with respect to how the model parameters and decoding step are handled, including an extension of the proposed approach in the context of conditional random fields. We show that this model, with very little supervision, can reach instantly performance comparable to those obtained by either state-of-the-art carefully handcrafted rule-based or trained statistical models for extraction of dialog acts on the Dialog State Tracking test datasets (DSTC2 and 3).
A novel immersed boundary velocity correction-lattice Boltzmann method and its application to simulate flow past a circular cylinder

A novel immersed boundary velocity correction–lattice Boltzmann method is presented and validated in this work by its application to simulate the two-dimensional flow over a circular cylinder. The present approach is inspired from the conventional immersed boundary method (IBM). In the conventional IBM, the effect of rigid body on the surrounding flow is modeled through a forcing term, which is in turn used to correct the surrounding velocity field. It was found that this process is actually an iterative procedure, trying to satisfy the non-slip boundary condition at the solid wall. In this work, a new concept of immersed boundary velocity correction approach is proposed, which directly corrects the velocity to enforce the physical boundary condition. The main advantage of the new method is that it is simple in concept and easy for implementation, and the convergence of numerical computation is faster and more stable than the conventional IBM. One challenging issue of conventional IBM is that some streamlines may pass through the solid body since there is no mechanism to enforce the non-slip condition at the boundary. As shown in the present numerical results, this unphysical phenomenon is avoided in our new method since the non-slip condition is enforced. The present results for the steady and unsteady flows compare very well with available data in the literature.
Shifted Jacobi tau method for solving the space fractional diffusion equation

In this paper, approximation techniques based on the shifted Jacobi together with spectral tau technique are presented to solve a class of initial-boundary value problems for the fractional diffusion equations with variable coefficients on a finite domain. The fractional derivatives are described in the Caputo sense. The technique is derived by expanding the required approximate solution as the elements of shifted Jacobi polynomials. Using the operational matrix of the fractional derivative, the problem can be reduced to a set of linear algebraic equations. Numerical examples are included to demonstrate the validity and applicability of the technique and a comparison is made with the existing results to show that the proposed method is easy to implement and produce accurate results.