nils-herrmann/clean_scirepeval huggingface dataset:
INTERNAL COMPACT PRINTED LOOP ANTENNA WITH MATCHING ELEMENT FOR LAPTOP APPLICATIONS

An internal compact printed loop antenna with matching element is presented for laptop applications. Bending to the printed loop structure is employed to reduce the size of antenna. A matching element is used at backside of the substrate to enhance the bandwidth of the antenna. The matching element is of rectangular shape with inverted U shape slot in it. The proposed antenna with wideband characteristics covers GSM900, GSM1900, UMTS, LTE2300, WLAN, WiMAX 3.3/3.5/3.7 bands and is suitable for laptop applications. The antenna structure is simulated using IE3D software. Keywordsbended; multiband antenna; printed loop antenna; tablet; wideband antenna.
Earthquake damage detection in the Imperial County Services Building I : The data and time – frequency analysis

The former Imperial County Services Building was a six-story reinforced concrete structure in the El Centro, California, severely damaged by the 1979 Imperial Valley earthquake. It represents a rare case of an instrumented building that has been damaged, and thus can serve as a full-scale benchmark to evaluate and further develop structural health monitoring methods. This paper presents an analysis of inter-story drifts, and of changes in the first NS and EW system frequencies (estimated from the ridge of the Gabor transform of the relative roof displacement response) as indicators of the occurrence of damage. The detected initial decreases of system frequency, of about 28% for NS and 24% for EW motions, are not believed to be due to severe damage. The subsequent decreases, of about 44% for NS and 21% for EW motions, are attributed to damage. Near the end of shaking, increases of about 35% for the NS and 36% for EW motions were detected. (These percentage changes were computed with respect to the previous value for particular interval, rather than a fixed reference). During the most severe shaking, the inter-story drifts exceeded 0.5% for NS and 1.5% for EW motions. r 2006
Discovery of Evolving Semantics through Dynamic Word Embedding Learning

During the course of human language evolution, the semantic meanings of words keep evolving with time. e understanding of evolving semantics enables us to capture the true meaning of the words in dierent usage contexts, and thus is critical for various applications, such as machine translation. While it is naturally promising to study word semantics in a time-aware manner, traditional methods to learn word vector representation do not adequately capture the change over time. To this end, in this paper, we aim at learning time-aware vector representation of words through dynamic word embedding modeling. Specically, we rst propose a method that captures time-specic semantics and across-time alignment simultaneously in a way that is robust to data sparsity. en, we solve the resulting optimization problem using a scalable coordinate descent method. Finally, we perform the empirical study on New York Times data to learn the temporal embeddings and develop multiple evaluations that illustrate the semantic evolution of words, discovered from news media. Moreover, our qualitative and quantitative tests indicate that the our method not only reliably captures the semantic evolution over time, but also consistently outperforms state-of-the-art temporal embedding approaches on both semantic accuracy and alignment quality.
Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking

In this paper, we study, compare and combine two state-of-the-art approaches to automatic feature engineering: Convolution Tree Kernels (CTKs) and Convolutional Neural Networks (CNNs) for learning to rank answer sentences in a Question Answering (QA) setting. When dealing with QA, the key aspect is to encode relational information between the constituents of question and answer in learning algorithms. For this purpose, we propose novel CNNs using relational information and combined them with relational CTKs. The results show that (i) both approaches achieve the state of the art on a question answering task, where CTKs produce higher accuracy and (ii) combining such methods leads to unprecedented high results.
XWand: UI for intelligent spaces

The XWand is a novel wireless sensor package that enables styles of natural interaction with intelligent environments. For example, a user may point the wand at a device and control it using simple gestures. The XWand system leverages the intelligence of the environment to best determine the user's intention. We detail the hardware device, signal processing algorithms to recover position and orientation, gesture recognition techniques, a multimodal (wand and speech) computational architecture and a preliminary user study examining pointing performance under conditions of tracking availability and audio feedback.
Stock market prediction system with modular neural networks

This paper discusses a buying and selling timing prediction system for stocks on the Tokyo Stock Exchange and analysis of intemal representation. It is based on modular neural networks[l][2]. We developed a number of learning algorithms and prediction methods for the TOPIX(Toky0 Stock Exchange Prices Indexes) prediction system. The prediction system achieved accurate predictions and the simulation on stocks tradmg showed an excellent profit. The prediction system was developed by Fujitsu and Nikko Securities.
DramaBank: Annotating Agency in Narrative Discourse

We describe the Story Intention Graph, a set of discourse relations designed to represent aspects of narrative. Compared to prior models, ours is a novel synthesis of the notions of goal, plan, intention, outcome, affect and time that is amenable to corpus annotation. We describe a collection project, DramaBank, which includes encodings of texts ranging from small fables to epic poetry and contemporary nonfiction.
Energy-traffic tradeoff cooperative offloading for mobile cloud computing

This paper presents a quantitative study on the energy-traffic tradeoff problem from the perspective of entire Wireless Local Area Network (WLAN). We propose a novel Energy-Efficient Cooperative Offloading Model (E2COM) for energy-traffic tradeoff, which can ensure the fairness of energy consumption of mobile devices and reduce the computation repetition and eliminate the Internet data traffic redundancy through cooperative execution and sharing computation results. We design an Online Task Scheduling Algorithm (OTS) based on a pricing mechanism and Lyapunov optimization to address the problem without predicting future information on task arrivals, transmission rates and so on. OTS can achieve a desirable tradeoff between the energy consumption and Internet data traffic by appropriately setting the tradeoff coefficient. Simulation results demonstrate that E2COM is more efficient than no offloading and cloud offloading for a variety of typical mobile devices, applications and link qualities in WLAN.
Reinforcement Learning Testbed for Power-Consumption Optimization

Common approaches to control a data-center cooling system rely on approximated system/environment models that are built upon the knowledge of mechanical cooling and electrical and thermal management. These models are difficult to design and often lead to suboptimal or unstable performance. In this paper, we show how deep reinforcement learning techniques can be used to control the cooling system of a simulated data center. In contrast to common control algorithms, those based on reinforcement learning techniques can optimize a system’s performance automatically without the need of explicit model knowledge. Instead, only a reward signal needs to be designed. We evaluated the proposed algorithm on the open source simulation platform EnergyPlus. The experimental results indicate that we can achieve 22% improvement compared to a model-based control algorithm built into the EnergyPlus. To encourage the reproduction of our work as well as future research, we have also publicly released an open-source EnergyPlus wrapper interface 1 directly compatible with existing reinforcement learning frameworks.
FiberMesh: designing freeform surfaces with 3D curves

This paper presents a system for designing freeform surfaces with a collection of 3D curves. The user first creates a rough 3D model by using a sketching interface. Unlike previous sketching systems, the user-drawn strokes stay on the model surface and serve as handles for controlling the geometry. The user can add, remove, and deform these control curves easily, as if working with a 2D line drawing. The curves can have arbitrary topology; they need not be connected to each other. For a given set of curves, the system automatically constructs a smooth surface embedding by applying functional optimization. Our system provides real-time algorithms for both control curve deformation and the subsequent surface optimization. We show that one can create sophisticated models using this system, which have not yet been seen in previous sketching or functional optimization systems.
Service specific anomaly detection for network intrusion detection

The constant increase of attacks against networks and their resources (as recently shown by the CodeRed worm) causes a necessity to protect these valuable assets. Firewalls are now a common installation to repel intrusion attempts in the first place. Intrusion detection systems (IDS), which try to detect malicious activities instead of preventing them, offer additional protection when the first defense perimeter has been penetrated. ID systems attempt to pin down attacks by comparing collected data to predefined signatures known to be malicious (signature based) or to a model of legal behavior (anomaly based).Anomaly based systems have the advantage of being able to detect previously unknown attacks but they suffer from the difficulty to build a solid model of acceptable behavior and the high number of alarms caused by unusual but authorized activities. We present an approach that utilizes application specific knowledge of the network services that should be protected. This information helps to extend current, simple network traffic models to form an application model that allows to detect malicious content hidden in single network packets. We describe the features of our proposed model and present experimental data that underlines the efficiency of our systems.
Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification

Categorization of documents is challenging, as the number o f discriminating words can be very large. We present a nearest neighbor classification scheme for text categoriz ation in which the importance of discriminating words is learned using mutual information and weight adjustment tec hniques. The nearest neighbors for a particular document are then computed based on the matching words and their weigh ts. We evaluate our scheme on both synthetic and real world documents. Our experiments with synthetic data sets s how that this scheme is robust under different emulated conditions. Empirical results on real world documents demo nstrate that this scheme outperforms state of the art classification algorithms such as C4.5, RIPPER, Rainbow, an d PEBLS.
Character animation from 2D pictures and 3D motion data

This article presents a new method to animate photos of 2D characters using 3D motion capture data. Given a single image of a person or essentially human-like subject, our method transfers the motion of a 3D skeleton onto the subject's 2D shape in image space, generating the impression of a realistic movement. We present robust solutions to reconstruct a projective camera model and a 3D model pose which matches best to the given 2D image. Depending on the reconstructed view, a 2D shape template is selected which enables the proper handling of occlusions. After fitting the template to the character in the input image, it is deformed as-rigid-as-possible by taking the projected 3D motion data into account. Unlike previous work, our method thereby correctly handles projective shape distortion. It works for images from arbitrary views and requires only a small amount of user interaction. We present animations of a diverse set of human (and nonhuman) characters with different types of motions, such as walking, jumping, or dancing.
Analysis of a Wavelength-Scaled Array (WSA) Architecture

Wavelength-scaled array architectures use scaled elements to achieve ultrawideband performance with significantly fewer overall radiators than traditional ultrawideband arrays based on a single element type. Compared to a conventional ultrawideband array with 8:1 bandwidth, a wavelength-scaled array that uses elements of three different sizes creates an aperture with fewer than 16% of the original element count, i.e., 6.4-times fewer elements, and by extension a comparable reduction in electronics required to feed the array. In this paper, a study of an asymmetric wavelength-scaled array architecture is presented for finite arrays of offset-centered dual-polarized flared-notch radiators. The unique element transitions within the finite array structure are modeled via a non-matching grid Domain Decomposition-Finite Element Method that allows for rigorous impedance and radiation pattern prediction of full-sized wavelength-scaled arrays. This design study shows that the wavelength-scaled array has comparable performance to traditional ultrawideband arrays in terms of VSWR, radiation patterns, array mismatch efficiency, and cross-polarization levels.
An investigation of model-free planning

The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity. For an RL agent to address these challenges, it is essential that it can plan effectively. Prior work has typically utilized an explicit model of the environment, combined with a specific planning algorithm (such as tree search). More recently, a new family of methods have been proposed that learn how to plan, by providing the structure for planning via an inductive bias in the function approximator (such as a tree structured neural network), trained end-to-end by a modelfree RL algorithm. In this paper, we go even further, and demonstrate empirically that an entirely model-free approach, without special structure beyond standard neural network components such as convolutional networks and LSTMs, can learn to exhibit many of the characteristics typically associated with a model-based planner. We measure our agent’s effectiveness at planning in terms of its ability to generalize across a combinatorial and irreversible state space, its data efficiency, and its ability to utilize additional thinking time. We find that our agent has many of the characteristics that one might expect to find in a planning algorithm. Furthermore, it exceeds the state-ofthe-art in challenging combinatorial domains such as Sokoban and outperforms other model-free approaches that utilize strong inductive biases toward planning.
Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost

We are interested in large-scale image classification and especially in the setting where images corresponding to new or existing classes are continuously added to the training set. Our goal is to devise classifiers which can incorporate such images and classes on-the-fly at (near) zero cost. We cast this problem into one of learning a metric which is shared across all classes and explore k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers. We learn metrics on the ImageNet 2010 challenge data set, which contains more than 1.2M training images of 1K classes. Surprisingly, the NCM classifier compares favorably to the more flexible k-NN classifier, and has comparable performance to linear SVMs. We also study the generalization performance, among others by using the learned metric on the ImageNet-10K dataset, and we obtain competitive performance. Finally, we explore zero-shot classification, and show how the zero-shot model can be combined very effectively with small training datasets.
Sparse inverse covariance estimation with the graphical lasso.

We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.
Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain

Rough terrain autonomous navigation continues to pose a challenge to the robotics community. Robust navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are coupled. When traversing complex unstructured terrain, this coupling (in the form of a cost function) has a large impact on robot behavior and performance, necessitating a robust design. This paper explores the application of Learning from Demonstration to this task for the Crusher autonomous navigation platform. Using expert examples of desired navigation behavior, mappings from both online and offline perceptual data to planning costs are learned. Challenges in adapting existing techniques to complex online planning systems and imperfect demonstration are addressed, along with additional practical considerations. The benefits to autonomous performance of this approach are examined, as well as the decrease in necessary designer effort. Experimental results are presented from autonomous traverses through complex natural environments.
A model-based method for the computation of fingerprints' orientation field

As a global feature of fingerprints, the orientation field is very important for automatic fingerprint recognition. Many algorithms have been proposed for orientation field estimation, but their results are unsatisfactory, especially for poor quality fingerprint images. In this paper, a model-based method for the computation of orientation field is proposed. First a combination model is established for the representation of the orientation field by considering its smoothness except for several singular points, in which a polynomial model is used to describe the orientation field globally and a point-charge model is taken to improve the accuracy locally at each singular point. When the coarse field is computed by using the gradient-based algorithm, a further result can be gained by using the model for a weighted approximation. Due to the global approximation, this model-based orientation field estimation algorithm has a robust performance on different fingerprint images. A further experiment shows that the performance of a whole fingerprint recognition system can be improved by applying this algorithm instead of previous orientation estimation methods.
Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge.

Functional magnetic resonance imaging (fMRI) of the human brain was used to study whether the amygdala is activated in response to emotional stimuli, even in the absence of explicit knowledge that such stimuli were presented. Pictures of human faces bearing fearful or happy expressions were presented to 10 normal, healthy subjects by using a backward masking procedure that resulted in 8 of 10 subjects reporting that they had not seen these facial expressions. The backward masking procedure consisted of 33 msec presentations of fearful or happy facial expressions, their offset coincident with the onset of 167 msec presentations of neutral facial expressions. Although subjects reported seeing only neutral faces, blood oxygen level-dependent (BOLD) fMRI signal in the amygdala was significantly higher during viewing of masked fearful faces than during the viewing of masked happy faces. This difference was composed of significant signal increases in the amygdala to masked fearful faces as well as significant signal decreases to masked happy faces, consistent with the notion that the level of amygdala activation is affected differentially by the emotional valence of external stimuli. In addition, these facial expressions activated the sublenticular substantia innominata (SI), where signal increases were observed to both fearful and happy faces--suggesting a spatial dissociation of territories that respond to emotional valence versus salience or arousal value. This study, using fMRI in conjunction with masked stimulus presentations, represents an initial step toward determining the role of the amygdala in nonconscious processing.
Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights

Supervised neural networks generalize well if there is much less information in the weights than there is in the output vectors of the training cases. So during learning, it is important to keep the weights simple by penalizing the amount of information they contain. The amount of information in a weight can be controlled by adding Gaussian noise and the noise level can be adapted during learning to optimize the trade-o between the expected squared error of the network and the amount of information in the weights. We describe a method of computing the derivatives of the expected squared error and of the amount of information in the noisy weights in a network that contains a layer of non-linear hidden units. Provided the output units are linear, the exact derivatives can be computed e ciently without time-consuming Monte Carlo simulations. The idea of minimizing the amount of information that is required to communicate the weights of a neural network leads to a number of interesting schemes for encoding the weights.
Controlling privacy in recommender systems

Recommender systems involve an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about their preferences. In this paper, we explore a two-tiered notion of privacy where there is a small set of “public” users who are willing to share their preferences openly, and a large set of “private” users who require privacy guarantees. We show theoretically and demonstrate empirically that a moderate number of public users with no access to private user information already suffices for reasonable accuracy. Moreover, we introduce a new privacy concept for gleaning relational information from private users while maintaining a first order deniability. We demonstrate gains from controlled access to private user preferences.
An ontology based approach for activity recognition from video

Representation and recognition of human activities is an important problem for video surveillance and security applications. Considering the wide variety of settings in which surveillance systems are being deployed, it is necessary to create a common knowledge-base or ontology of human activities. Most current attempts at ontology design in computer vision for human activities have been empirical in nature. In this paper, we present a more systematic approach to address the problem of designing ontologies for visual activity recognition. We draw on general ontology design principles and adapt them to the specific domain of human activity ontologies. Then, we discuss qualitative evaluation principles and provide several examples from existing ontologies and how they can be improved upon. Finally, we demonstrate quantitatively in terms of recognition performance, the efficacy and validity of our approach for bank and airport tarmac surveillance domains.