nils-herrmann/clean_scirepeval huggingface dataset:
Where Is the Soho of Rome? Measures and Algorithms for Finding Similar Neighborhoods in Cities

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
Stability of Networked Control Systems With Uncertain Time-Varying Delays

In this technical note, a new approach for the stability analysis and controller synthesis of networked control systems (NCSs) with uncertain, time-varying, network delays is presented. Based on the Jordan form of the continuous-time plant, a discrete-time representation of the NCS is derived. Using this model for delays that can be both smaller and larger than the sampling interval, sufficient LMI conditions for stability and feedback stabilization are proposed. The results are illustrated by a typical motion control example.
Investigating gated recurrent networks for speech synthesis

Recently, recurrent neural networks (RNNs) as powerful sequence models have re-emerged as a potential acoustic model for statistical parametric speech synthesis (SPSS). The long short-term memory (LSTM) architecture is particularly attractive because it addresses the vanishing gradient problem in standard RNNs, making them easier to train. Although recent studies have demonstrated that LSTMs can achieve significantly better performance on SPSS than deep feedforward neural networks, little is known about why. Here we attempt to answer two questions: a) why do LSTMs work well as a sequence model for SPSS; b) which component (e.g., input gate, output gate, forget gate) is most important. We present a visual analysis alongside a series of experiments, resulting in a proposal for a simplified architecture. The simplified architecture has significantly fewer parameters than an LSTM, thus reducing generation complexity considerably without degrading quality.
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet

Recent results suggest that state-of-the-art saliency models perform far from optimal in predicting fixations. This lack in performance has been attributed to an inability to model the influence of high-level image features such as objects. Recent seminal advances in applying deep neural networks to tasks like object recognition suggests that they are able to capture this kind of structure. However, the enormous amount of training data necessary to train these networks makes them difficult to apply directly to saliency prediction. We present a novel way of reusing existing neural networks that have been pretrained on the task of object recognition in models of fixation prediction. Using the well-known network of Krizhevsky et al., 2012, we come up with a new saliency model that significantly outperforms all state-of-the-art models on the MIT Saliency Benchmark. We show that the structure of this network allows new insights in the psychophysics of fixation selection and potentially their neural implementation. To train our network, we build on recent work on the modeling of saliency as point processes. By understanding how humans choose eye fixations, we can hope to understand and explain human behaviour in a number of vision-related tasks. For this reason human eye movements have been studied for more than 80 years (e.g., Buswell, 1935). During the last 20 years, many models have been developed trying to explain fixations in terms of so called “saliency maps”. Recently, it has been suggested to model saliency maps probabilistically using point processes (Barthelmé et al., 2013) and to evaluate them using loglikelihood (Kümmerer et al., 2014). This evaluation revealed that state-of-theart models of saliency explain only one third of the explainable information in the spatial fixation structure (Kümmerer et al., 2014). Most of the existing models use low-level cues like edge-detectors and color filters (Itti et al., 1998) or local image statistics (Zhang et al., 2008; Bruce and 1 ar X iv :1 41 1. 10 45 v1 [ cs .C V ] 4 N ov 2 01 4
Design and Locomotion Control of a Biomimetic Underwater Vehicle With Fin Propulsion

As a novel biologically inspired underwater vehicle, a robotic manta ray (RoMan-II) has been developed for potential marine applications. Manta ray can perform diversified locomotion patterns in water by manipulating two wide tins. These motion patterns have been implemented on the developed fish robot, including swimming by flapping fins, turning by modulating phase relations of fins, and online transition of different motion patterns. The movements are achieved by using a model of artificial central pattern generators (CPGs) constructed with coupled nonlinear oscillators. This paper focuses on the analytical formulation of coupling terms in the CPG model and the implementation issues of the CPG-based control on the fish robot. The control method demonstrated on the manta ray robot is expected to be a frame- work that can tackle locomotion control problems in other types of multifin-actuated fish robots or more general robots with rhythmic movement patterns.
Error and discrepancy in radiology: inevitable or avoidable?

Errors and discrepancies in radiology practice are uncomfortably common, with an estimated day-to-day rate of 3-5% of studies reported, and much higher rates reported in many targeted studies. Nonetheless, the meaning of the terms "error" and "discrepancy" and the relationship to medical negligence are frequently misunderstood. This review outlines the incidence of such events, the ways they can be categorized to aid understanding, and potential contributing factors, both human- and system-based. Possible strategies to minimise error are considered, along with the means of dealing with perceived underperformance when it is identified. The inevitability of imperfection is explained, while the importance of striving to minimise such imperfection is emphasised.



• Discrepancies between radiology reports and subsequent patient outcomes are not inevitably errors. • Radiologist reporting performance cannot be perfect, and some errors are inevitable. • Error or discrepancy in radiology reporting does not equate negligence. • Radiologist errors occur for many reasons, both human- and system-derived. • Strategies exist to minimise error causes and to learn from errors made.
LB_Keogh Supports Exact Indexing of Shapes under Rotation Invariance with Arbitrary Representations and Distance Measures

The matching of two-dimensional shapes is an important problem with applications in domains as diverse as biometrics, industry, medicine and anthropology. The distance measure used must be invariant to many distortions, including scale, offset, noise, partial occlusion, etc. Most of these distortions are relatively easy to handle, either in the representation of the data or in the similarity measure used. However rotation invariance seems to be uniquely difficult. Current approaches typically try to achieve rotation invariance in the representation of the data, at the expense of discrimination ability, or in the distance measure, at the expense of efficiency. In this work we show that we can take the slow but accurate approaches and dramatically speed them up. On real world problems our technique can take current approaches and make them four orders of magnitude faster, without false dismissals. Moreover, our technique can be used with any of the dozens of existing shape representations and with all the most popular distance measures including Euclidean distance, Dynamic Time Warping and Longest Common Subsequence.
Curvature and torsion estimators based on parametric curve fitting

Many applications of geometry processing and computer vision rely on geometric properties of curves, particularly their curvature. Several methods have already been proposed to estimate the curvature of a planar curve, most of them for curves in digital spaces. This work proposes a new scheme for estimating curvature and torsion of planar and spatial curves, based on weighted least–squares fitting and local arc–length approximation. The method is simple enough to admit a convergence analysis that take into acount the effect of noise in the samples. The implementation of the method is compared to other curvature estimation methods showing a good performance. Applications to prediction in geometry compression are presented both as a practical application and as a validation of this new scheme.
Ontology-based semantic similarity: A new feature-based approach

Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language processing, knowledge acquisition and information retrieval. Semantic similarity measures exploit knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approach’s results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works.
ACT-R: A Theory of Higher Level Cognition and Its Relation to Visual Attention

The ACT-R system is a general system for modeling a wide range of higher level cognitive processes. Recently, it has been embellished with a theory of how its higher level processes interact with a visual interface. This includes a theory of how visual attention can move across the screen, encoding information into a form that can be processed by ACT-R. This system is applied to modeling several classic phenomena in the literature that depend on the speed and selectivity with which visual attention can move across a visual display. ACT-R is capable of interacting with the same computer screens that subjects do and, as such, is well suited to provide a model for tasks involving human-computer interaction. In this article, we discuss a demonstration of ACT-R's application to menu selection and show that the ACT-R theory makes unique predictions, without estimating any parameters, about the time to search a menu. These predictions are confirmed. John R. Anderson is a cognitive scientist with an interest in cognitive architectures and intelligent tutoring systems; he is a Professor of Psychology and Computer Science at Carnegie Mellon University. Michael Matessa is a graduate student studying cognitive psychology at Carnegie Mellon University; his interests include cognitive architectures and modeling the acquisition of information from the environment. Christian Lebiere is a computer scientist with an interest in intelligent architectures; he is a Research Programmer in the Department of Psycholo and a graduate student in the School of Computer Science at Carnegie Me1 By on University. 440 ANDERSON, MATESSA, LEBIERE
Performance Analysis of Mixed-ADC Massive MIMO Systems Over Rician Fading Channels

The practical deployment of massive multiple-input multiple-output (MIMO) in the future fifth generation (5G) wireless communication systems is challenging due to its high-hardware cost and power consumption. One promising solution to address this challenge is to adopt the low-resolution analog-to-digital converter (ADC) architecture. However, the practical implementation of such architecture is challenging due to the required complex signal processing to compensate the coarse quantization caused by low-resolution ADCs. Therefore, few high-resolution ADCs are reserved in the recently proposed mixed-ADC architecture to enable low-complexity transceiver algorithms. In contrast to previous works over Rayleigh fading channels, we investigate the performance of mixed-ADC massive MIMO systems over the Rician fading channel, which is more general for the 5G scenarios like Internet of Things. Specially, novel closed-form approximate expressions for the uplink achievable rate are derived for both cases of perfect and imperfect channel state information (CSI). With the increasing Rician  $K$ -factor, the derived results show that the achievable rate will converge to a fixed value. We also obtain the power-scaling law that the transmit power of each user can be scaled down proportionally to the inverse of the number of base station (BS) antennas for both perfect and imperfect CSI. Moreover, we reveal the tradeoff between the achievable rate and the energy efficiency with respect to key system parameters, including the quantization bits, number of BS antennas, Rician  $K$ -factor, user transmit power, and CSI quality. Finally, numerical results are provided to show that the mixed-ADC architecture can achieve a better energy-rate tradeoff compared with the ideal infinite-resolution and low-resolution ADC architectures.
Simple Algorithmic Theory of Subjective Beauty , Novelty , Surprise , Interestingness , Attention , Curiosity , Creativity , Art , Science , Music , Jokes

In this summary of previous work, I argue that data becomes temporarily interesting by itself to some selfimproving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively more “beautiful.” Curiosity is the desire to create or discover more non-random, non-arbitrary, “truly novel,” regular data that allows for compression progress because its regularity was not yet known. This drive maximizes “interestingness,” the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and recent artificial systems.
Aircraft Cavity-Backed Nonprotruding Wideband Antenna

A novel circular aperture antenna is presented. The proposed nonprotruding antenna is composed of a cylindrical cavity embedding an inverted hat antenna whose profile is defined by three elliptical segments. This profile can be used to control and shape the return loss response over a wide frequency band (31% at -15 dB). The present design combines the benefits of a low-profile antenna and the broad bandwidth of the inverted hat monopole. A parametric analysis of the antenna is conducted. Omnidirectional monopole-like radiation pattern and vertical polarization are also verified with measurements and simulations. This antenna constitutes a low drag candidate for a distance measurement equipment (DME) aircraft navigation system.
Energetic Cost of Locomotion in Kangaroos

THE hopping of kangaroos is reminiscent of a bouncing ball or the action of a pogo stick. This suggests a significant storage and recovery of energy in elastic elements. One might surmise that the kangaroo's first hop would require a large amount of energy whereas subsequent hops could rely extensively on elastic rebound. If this were the case, then the kangaroo's unusual saltatory mode of locomotion should be an energetically inexpensive way to move.
Broadband Phase Shifter Using Loaded Transmission Line

This letter presents a novel loaded transmission line, which has a constant phase shift over a wide bandwidth, referenced to a uniform transmission line. Compared with the conventional coupled line phase shifter, the configuration shows good performance with simplicity in both design and fabrication. The optimal design parameters for different phase shift values up to 120° are also presented to allow for a quick design process. To verify the configuration, a 90° phase shifter using a T shaped open stub loaded transmission line is designed, fabricated and measured. Excellent performance is achieved with small insertion loss and phase deviation over a bandwidth of 82%.
Design and experimental evaluation of cooperative adaptive cruise control

Road throughput can be increased by driving at small inter-vehicle time gaps. The amplification of velocity disturbances in upstream direction, however, poses limitations to the minimum feasible time gap. String-stable behavior is thus considered an essential requirement for the design of automatic distance control systems, which are needed to allow for safe driving at time gaps well below 1 s. Theoretical analysis reveals that this requirement can be met using wireless inter-vehicle communication to provide real-time information of the preceding vehicle, in addition to the information obtained by common Adaptive Cruise Control (ACC) sensors. In order to validate these theoretical results and to demonstrate the technical feasibility, the resulting control system, known as Cooperative ACC (CACC), is implemented on a test fleet consisting of six passenger vehicles. Experiments clearly show that the practical results match the theoretical analysis, thereby indicating the possibilities for short-distance vehicle following.
Unroofing and marsupialization vs. rhomboid excision and Limberg flap in pilonidal disease: a prospective, randomized, clinical trial.

The aim of the study was to compare clinical outcomes and quality of life in patients undergoing surgery for pilonidal disease with unroofing and marsupialization (UM) or rhomboid excision and Limberg flap (RELP) procedures.



One hundred forty consecutive patients with pilonidal sinus were randomly assigned to receive either UM or RELP procedures. A specifically designed questionnaire was administered at three months to assess time from the operation until the patient was able to walk, return to daily activities, or sit without pain, time to return to work or school, and time to healing. Postoperative pain was assessed with a visual analog scale and the McGill Pain Questionnaire. Patients' quality of life was evaluated with the Cardiff Wound Impact Schedule (CWIS). Questionnaires were administered by a clinician blinded to treatment.



Compared with RELP, patients receiving UM had significantly shorter duration of operation and hospital stay, shorter time periods to walk, return to daily activities, or sit without pain and to return to work or school, and fewer complications. Time to final healing was significantly shorter and quality of life scores on the CWIS were higher in patients receiving RELP than in those receiving UM. Patients with UM had lower levels of pain one week after surgery.



The unroofing and marsupialization procedure provides more clinical benefits in the treatment of pilonidal disease than rhomboid excision and Limberg flap and should be considered the procedure of choice. However, it may be associated with more inconvenience in wound care and longer healing time than rhomboid excision and Lindberg flap.
Template-based autonomous navigation and obstacle avoidance in urban environments

Autonomous navigation is a fundamental task in mobile robotics. In the last years, several approaches have been addressing the autonomous navigation in outdoor environments. Lately it has also been extended to robotic vehicles in urban environments. This paper presents a vehicle control system capable of learning behaviors based on examples from human driver and analyzing different levels of memory of the templates, which are an important capability to autonomous vehicle drive. Our approach is based on image processing, template matching classification, finite state machine, and template memory. The proposed system allows training an image segmentation algorithm and a neural network to work with levels of memory of the templates in order to identify navigable and non-navigable regions. As an output, it generates the steering control and speed for the Intelligent Robotic Car for Autonomous Navigation (CaRINA). Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques.
Active inference and epistemic value.

We offer a formal treatment of choice behavior based on the premise that agents minimize the expected free energy of future outcomes. Crucially, the negative free energy or quality of a policy can be decomposed into extrinsic and epistemic (or intrinsic) value. Minimizing expected free energy is therefore equivalent to maximizing extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximizing information gain or intrinsic value (or reducing uncertainty about the causes of valuable outcomes). The resulting scheme resolves the exploration-exploitation dilemma: Epistemic value is maximized until there is no further information gain, after which exploitation is assured through maximization of extrinsic value. This is formally consistent with the Infomax principle, generalizing formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk-sensitive (Kullback-Leibler) control. Furthermore, as with previous active inference formulations of discrete (Markovian) problems, ad hoc softmax parameters become the expected (Bayes-optimal) precision of beliefs about, or confidence in, policies. This article focuses on the basic theory, illustrating the ideas with simulations. A key aspect of these simulations is the similarity between precision updates and dopaminergic discharges observed in conditioning paradigms.
Antenna miniaturization and bandwidth enhancement using a reactive impedance substrate

The concept of a novel reactive impedance surface (RIS) as a substrate for planar antennas, that can miniaturize the size and significantly enhance both the bandwidth and the radiation characteristics of an antenna is introduced. Using the exact image formulation for the fields of elementary sources above impedance surfaces, it is shown that a purely reactive impedance plane with a specific surface reactance can minimize the interaction between the elementary source and its image in the RIS substrate. An RIS can be tuned anywhere between perfectly electric and magnetic conductor (PEC and PMC) surfaces offering a property to achieve the optimal bandwidth and miniaturization factor. It is demonstrated that RIS can provide performance superior to PMC when used as substrate for antennas. The RIS substrate is designed utilizing two-dimensional periodic printed metallic patches on a metal-backed high dielectric material. A simplified circuit model describing the physical phenomenon of the periodic surface is developed for simple analysis and design of the RIS substrate. Also a finite-difference time-domain (FDTD) full-wave analysis in conjunction with periodic boundary conditions and perfectly matched layer walls is applied to provide comprehensive study and analysis of complex antennas on such substrates. Examples of different planar antennas including dipole and patch antennas on RIS are considered, and their characteristics are compared with those obtained from the same antennas over PEC and PMC. The simulations compare very well with measured results obtained from a prototype /spl lambda//10 miniaturized patch antenna fabricated on an RIS substrate. This antenna shows measured relative bandwidth, gain, and radiation efficiency of BW=6.7, G=4.5 dBi, and e/sub r/=90, respectively, which constitutes the highest bandwidth, gain, and efficiency for such a small size thin planar antenna.