nils-herrmann/clean_scirepeval huggingface dataset:
Low-Profile Millimeter-Wave SIW Cavity-Backed Dual-Band Circularly Polarized Antenna

A single-fed low-profile millimeter-wave substrate-integrated waveguide (SIW) cavity-backed slot antenna for dual-band circular polarization (CP) applications is proposed. First, by employing two annular exponential slots carved on the surface of a circular SIW cavity, dual-band CP radiation is achieved. Two different modes, that is, the cavity mode and the surface mode, separately modulated by the cavity field distribution and the surface current, are investigated. They are utilized to achieve the dual-band CP radiation. Next, a two-order matching circuit using inductor windows is introduced to achieve dual-band matching at the low-profile antenna design with the help of the differential evolution method. Finally, for the dual-band CP application at 37.5 and 47.8 GHz, an antenna prototype is fabricated and its performance is evaluated. Simulation and measurement results agree very well.
Gradient Flows for Optimizing Triangular Mesh-based Surfaces: Applications to 3D Reconstruction Problems Dealing with Visibility

This article tackles the problem of using variational methods for evolving 3D deformable surfaces. We give an overview of gradient descent flows when the shape is represented by a triangular mesh-based surface, and we detail the gradients of two generic energy functionals which embody a number of energies used in mesh processing and computer vision. In particular, we show how to rigorously account for visibility in the surface optimization process. We present different applications including 3D reconstruction from multiple views for which the visibility is fundamental. The gradient correctly takes into account the visibility changes that occur when a surface moves; this forces the contours generated by the reconstructed surface to match with the apparent contours in the input images.
A Comparative Evaluation of Term Recognition Algorithms

Automatic Term recognition (ATR) is a fundamental processing step preceding more complex tasks such as semantic search and ontology learning. From a large number of methodologies available in the literature only a few are able to handle both single and multi-word terms. In this paper we present a comparison of five such algorithms and propose a combined approach using a voting mechanism. We evaluated the six approaches using two different corpora and show how the voting algorithm performs best on one corpus (a collection of texts from Wikipedia) and less well using the Genia corpus (a standard life science corpus). This indicates that choice and design of corpus has a major impact on the evaluation of term recognition algorithms. Our experiments also showed that single-word terms can be equally important and occupy a fairly large proportion in certain domains. As a result, algorithms that ignore single-word terms may cause problems to tasks built on top of ATR. Effective ATR systems also need to take into account both the unstructured text and the structured aspects and this means information extraction techniques need to be integrated into the term recognition process.
The turing test track of the 2012 Mario AI Championship: Entries and evaluation

The Turing Test Track of the Mario AI Championship focused on developing human-like controllers for a clone of the popular game Super Mario Bros. Competitors participated by submitting AI agents that imitate human playing style. This paper presents the rules of the competition, the software used, the voting interface, the scoring procedure, the submitted controllers and the recent results of the competition for the year 2012. We also discuss what can be learnt from this competition in terms of believability in platform games. The discussion is supported by a statistical analysis of behavioural similarities and differences among the agents, and between agents and humans. The paper is co-authored by the organizers of the competition (the first three authors) and the competitors.
A Modified CFOA and Its Applications to Simulated Inductors, Capacitance Multipliers, and Analog Filters

In this paper, using a minimum number of passive components, i.e., new grounded and floating inductance simulators, grounded capacitance multipliers, and frequency-dependent negative resistors (FDNRs) based on one/two modified current-feedback operational amplifiers (MCFOAs), are proposed. The type of the simulators depends on the passive element selection used in the structure of the circuit without requiring critical active and passive component-matching conditions and/or cancellation constraints. In order to show the flexibility of the proposed MCFOA, a single-input three-output (SITO) voltage-mode (VM) filter, two three-input single-output (TISO) VM filters, and an SITO current-mode (CM) filter employing a single MCFOA are reported. The layout of the proposed MCFOA is also given. A number of simulations using the SPICE program and some experimental tests are performed to exhibit the performance of the introduced structures.
The attention system of the human brain.

The concept of attention as central to human performance extends back to the start of experimental psychology (James 1890), yet even a few years ago, it would not have been possible to outline in even a preliminary form a functional anatomy of the human attentional system. New developments in neuroscience (Hillyard & Picton 1987, Raichle 1983, Wurtz et al 1980) have opened the study of higher cognition to physiological analysis, and have revealed a system of anatomical areas that appear to be basic to the selection of information for focal (conscious) processing. The importance of attention is its unique role in connecting the mental level of description of processes used in cognitive science with the anatomical level common i neuroscience. Sperry (1988, p. 609) describes the central role that mental concepts play in understanding brain function as follows:
A Game-Theoretic Model for Medium Access Control

In this paper, a game-theoretic model for contention based medium access control (contention control) is proposed. We define a general game-theoretic model, called random access game, to capture the distributed nature of contention control and the interaction among wireless nodes with contention-based medium access. We study the design of random access games, characterize their equilibria, study their dynamics, and propose distributed algorithms to achieve the equilibria. This provides a unique perspective to understand existing MAC protocols and a general framework to guide the design of new ones to improve the system performance. As examples, a series of utility functions is proposed for games achieving the maximum throughput in a network of homogeneous nodes. The convergence of different variants (e.g., asynchronous and stochastic algorithms) of different dynamic algorithms such as gradient play are obtained. An equilibrium selection algorithm is also proposed to guarantee that the dynamic algorithms can actually achieve the desired operating point. Simulation results show that game model based protocols can achieve superior performance over the standard IEEE 802.11 DCF, and comparable performance as existing protocols with the best performance in literature.
Leveraging Context-Free Grammar for Efficient Inverted Index Compression

Large-scale search engines need to answer thousands of queries per second over billions of documents, which is typically done by querying a large inverted index. Many highly optimized integer encoding techniques are applied to compress the inverted index and reduce the query processing time. In this paper, we propose a new grammar-based inverted index compression scheme, which can improve the performance of both index compression and query processing.
 Our approach identifies patterns (common subsequences of docIDs) among different posting lists and generates a context-free grammar to succinctly represent the inverted index. To further optimize the compression performance, we carefully redesign the index structure. Experiments show a reduction up to 8.8% in space usage while decompression is up to 14% faster.
 We also design an efficient list intersection algorithm which utilizes the proposed grammar-based inverted index. We show that our scheme can be combined with common docID reassignment methods and encoding techniques, and yields about 14% to 27% higher throughput for AND queries by utilizing multiple threads.
Genetic Algorithm Rule-Based Intrusion Detection System ( GAIDS )

This study examines the detection of attacks or network intrusion by users referred to as hackers (whose aim is to gain illegal entry as well as access to a network system and resources. Network and data security has become a pertinent issue with the advent of the Internet; though the Internet comes with a lot of merits on its own. Traditional used methods for data security includes the use of passwords, cryptography to mention few. The approach considered here is Intrusion Detection System, which is a software, driver or device used to prevent an unauthorized or illegal access to data in a networked system. Most of the existing IDS are implemented via rule-based systems where new attacks are not detectable. This study thus, presents a genetic algorithm based approach (with its driver implementation), which employs a set of classification rule derived from network audit data and the support-confidence framework, utilized as fitness function to judge the quality of each rule. The software implementation is aimed at improving system security in networked settings allowing for confidentiality, integrity and availability of system resources.
Sketch Retrieval via Dense Stroke Features

Chao Ma1 chaoma@sjtu.edu.cn Xiaokang Yang1 xkyang@sjtu.edu.cn Chongyang Zhang1 sunny_zhang@sjtu.edu.cn Xiang Ruan2 gen@omm.ncl.omron.co.jp Ming-Hsuan Yang3 mhyang@ucmerced.edu 1 Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai, China 2 Omron Coporation Kyoto, Japan 3 Electrical Engineering and Computer Science University of California, Merced California, USA
Comparison Of Laser Scanning And Photogrammetry And Their Use For Digital Recording Of Cultural Monument Case Study: Byzantine Land Walls-Istanbul

3D modeling of cultural monuments is very crucial issue for preparing restoration projects. However, it has challenges such as data acquisition, preparation and processing. 3D modeling of objects can be time consuming and may include some difficulties due to the complexity of the structures. 3D terrestrial laser (TLS) scanning technique is one of the reliable and advantageous methods for 3D reconstruction of monuments. This technique is commonly acknowledged due to its accuracy, speed and flexibility. But the suitability and capability of this technique depends on proper usage, and good survey planning. Magnificent developments in highresolution digital sensor technologies leaded to manufacturing of new camera systems. Parallel to these innovations, development of computer systems and image processing techniques made enable to obtain multiple image-based 3D object models. In the presented study, TLS method has been compared to conventional photogrammetric and image-based dense matching methods. Automatic dense point creation has been realized by our developed algorithm and PIXEL-PHOTO software which generates 3D point clouds from stereo images. The reliability and encountered problems during point cloud measurement process have been discussed. The study area has been chosen as historical Byzantine Land Walls of Istanbul, which constitute a remarkable area defining the ancient city’s historical peninsula.
Unconstrained Arabic & Urdu Text Recognition using Deep CNN-RNN Hybrid Networks

We demonstrate the effectiveness of an end-to-end trainable hybrid CNN-RNN architecture in recognizing Urdu text from printed documents, typically known as Urdu OCR, and from Arabic text embedded in videos and natural scenes. When dealing with low-resource languages like Arabic and Urdu, a major adversary in developing a robust recognizer is the lack of large quantity of annotated data. We overcome this problem by synthesizing millions of images from a large vocabulary of words and phrases scraped from Wikipedia’s Arabic and Urdu versions, using a wide variety of fonts downloaded from various online resources. Building robust recognizers for Arabic and Urdu text has always been a challenging task. Though a lot of research has been done in the field of text recognition, the focus of the vision community has been primarily on English. While, Arabic script has started to receive some spotlight as far as text recognition is concerned, works on other languages which use the Nabatean family of scripts, like Urdu and Persian, are very limited. Moreover, the quality of the works presented in this field generally lack a standardized structure making it hard to reproduce and verify the claims or results. This is quite surprising considering the fact that Arabic is the fifth most spoken language in the world after Chinese, English, Spanish and Hindi catering to 4.7% of the world’s population, while Urdu has over a 100 million speakers and is spoken widely in Pakistan, where it is the national language, and India where it is recognized as one of the 22 official languages. In this thesis, we introduce the problems related with text recognition of low-resource languages, namely Arabic and Urdu, in various scenarios. We propose a language independent Hybrid CNN-RNN architecture which can be trained in an end-to-end fashion and prove it’s dominance over simple RNN based methods. Moreover, we dive deeper into the working of its convolutional layers and verify the robustness of convolutional-features through layer visualizations. We also propose a method to synthesize artificial text images to do away with the need of annotating large amounts of training data. We outperform previous state-of-the-art methods on existing benchmarks by quite some margin and release two new benchmark datasets for Arabic Scene Text and Urdu Printed Text Recognition to instill interest among fellow researchers of the field.
Minimising semantic drift with Mutual Exclusion Bootstrapping

Iterative bootstrapping techniques are commonly used to extract lexical semantic resources from raw text. Their major weakness is that, without costly human intervention, the extracted terms (often rapidly) drift from the meaning of the original seed terms. In this paper we propose Mutual Exclusion bootstrapping (MEB) in which multiple semantic classes compete for each extracted term. This significantly reduces the problem of semantic drift by providing boundaries for the semantic classes. We demonstrate the superiority of MEB to standard bootstrapping in extracting named entities from the Google Web 1T 5-grams. Finally, we demonstrate that MEB is a multi-way cut problem over semantic classes, terms and contexts.
Ecology-driven stereotypes override race stereotypes.

Why do race stereotypes take the forms they do? Life history theory posits that features of the ecology shape individuals' behavior. Harsh and unpredictable ("desperate") ecologies induce fast strategy behaviors such as impulsivity, whereas resource-sufficient and predictable ("hopeful") ecologies induce slow strategy behaviors such as future focus. We suggest that individuals possess a lay understanding of ecology's influence on behavior, resulting in ecology-driven stereotypes. Importantly, because race is confounded with ecology in the United States, we propose that Americans' stereotypes about racial groups actually reflect stereotypes about these groups' presumed home ecologies. Study 1 demonstrates that individuals hold ecology stereotypes, stereotyping people from desperate ecologies as possessing faster life history strategies than people from hopeful ecologies. Studies 2-4 rule out alternative explanations for those findings. Study 5, which independently manipulates race and ecology information, demonstrates that when provided with information about a person's race (but not ecology), individuals' inferences about blacks track stereotypes of people from desperate ecologies, and individuals' inferences about whites track stereotypes of people from hopeful ecologies. However, when provided with information about both the race and ecology of others, individuals' inferences reflect the targets' ecology rather than their race: black and white targets from desperate ecologies are stereotyped as equally fast life history strategists, whereas black and white targets from hopeful ecologies are stereotyped as equally slow life history strategists. These findings suggest that the content of several predominant race stereotypes may not reflect race, per se, but rather inferences about how one's ecology influences behavior.
Dimensioning Large Call Centers

We develop a framework for asymptotic optimization of a queueing system. The motivation is the staffing problem of large call centers, which we have modeled as M/M/N queues with N , the number of agents, being large. Within our framework, we determine the asymptotically optimal staffing level N ∗ that trades off agents’ costs with service quality: the higher the latter, the more expensive is the former. As an alternative to this optimization, we also develop a constraint satisfaction approach where one chooses the least N ∗ that adheres to a given constraint on waiting cost. Either way, the analysis gives rise to three regimes of operation: quality-driven, where the focus is on service quality; efficiency-driven, which emphasizes agents’ costs; and a rationalized regime that balances, and in fact unifies, the other two. Numerical experiments reveal remarkable accuracy of our asymptotic approximations: over a wide range of parameters, from the very small to the extremely large, N ∗ is exactly optimal, or it is accurate to within a single agent. We demonstrate the utility of our approach by revisiting the square-root safety staffing principle, which is a long-existing rule of thumb for staffing the M/M/N queue. In its simplest form, our rule is as follows: if c is the hourly cost of an agent, and a is the hourly cost of customers’ delay, then N ∗ =R+ y∗ a/c √R, where R is the offered load, and y∗ · is a function that is easily computable.
How can human motion prediction increase transparency?

A major issue in the field of human-robot interaction for assistance to manipulation is transparency. This basic feature qualifies the capacity for a robot to follow human movements without any human-perceptible resistive forces. In this paper we address the issue of human motion prediction in order to increase the transparency of a robotic manipulator. Our aim is not to predict the motion itself, but to study how this prediction can be used to improve the robot transparency. For this purpose, we have designed a setup for performing basic planar manipulation tasks involving movements that are demanded to the subject and thus easily predictable. Moreover, we have developed a general controller which takes a predicted trajectory (recorded from offline free motion experiments) as an input and feeds the robot motors with a weighted sum of three controllers: torque feedforward, variable stiffness control and force feedback control. Subjects were then asked to perform the same task but with or without the robot assistance (which was not visible to the subject), and with several sets of gains for the controller tuning. First results seems to indicate that when a predictive controller with open loop torque feedforward is used, in conjunction with force- feedback control, the interaction forces are minimized. Therefore, the transparency is increased.
Cloud Computing in Healthcare and Biomedicine

High throughput platforms available in clinical settings or in research laboratories, such as magnetic resonance imaging, microarray, mass spectrometry and next-generation sequencing, are producing an increasing volume of clinical and omics data that poses new issues in terms of secure data storage, models for data integration and analysis, and high performance computing. Cloud Computing offers large scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, and it supports easy but powerful distributed computing models, for facing those issues. In fact, in the recent years it has been adopted for the deployment of several applications in healthcare and bioinformatics both in academia and in the industry. However, cloud computing presents several issues regarding the security and privacy of data, that are particularly important when analyzing patients data, such as in personalized medicine. This paper reviews main cloud-based healthcare and biomedicine applications; with a special focus on healthcare, biomedicine and bioinformatics solutions and underlines main issues and problems related to the use of such platforms for the storage and analysis of patients data.
Surface Aesthetics in Tip Rhinoplasty: A Step-by-Step Guide.

Tip rhinoplasty is a key component of aesthetic rhinoplasty. An understanding of the correlation between tip surface aesthetics and the underlying anatomic structures enables proper identification and correction of tip abnormalities. Surface aesthetics of the attractive nose are created by certain lines, shadows, and highlights with specific proportions and breakpoints. In this Featured Operative Technique, the authors describe a stepwise process for tip rhinoplasty that conceptualizes aesthetic subunits as geometric polygons to define the existing deformity, the operative plan, and the aesthetic goals. Tip rhinoplasty is described in detail, from initial markings through incisions and dissection. The autorim graft concept is explained, and lateral crural steal and footplate setback techniques are described for the attainment of symmetric domes with correct lateral crural resting angles. Methods in columellar reconstruction are described, including creating the columella (C') breakpoint and the infralobular caudal contour graft. The principal author (B.Ç.) has applied these techniques to 257 consecutive "polygon rhinoplasties" over the past 3 years.
Induction and molecular signature of pathogenic TH17 cells

Interleukin 17 (IL-17)-producing helper T cells (TH17 cells) are often present at the sites of tissue inflammation in autoimmune diseases, which has led to the conclusion that TH17 cells are main drivers of autoimmune tissue injury. However, not all TH17 cells are pathogenic; in fact, TH17 cells generated with transforming growth factor-β1 (TGF-β1) and IL-6 produce IL-17 but do not readily induce autoimmune disease without further exposure to IL-23. Here we found that the production of TGF-β3 by developing TH17 cells was dependent on IL-23, which together with IL-6 induced very pathogenic TH17 cells. Moreover, TGF-β3-induced TH17 cells were functionally and molecularly distinct from TGF-β1-induced TH17 cells and had a molecular signature that defined pathogenic effector TH17 cells in autoimmune disease.