nils-herrmann/clean_scirepeval huggingface dataset:
Robust Learning with Missing Data

This paper introduces a new method, called the robust Bayesian estimator (RBE), to learn conditional probability distributions from incomplete data sets. The intuition behind the RBE is that, when no information about the pattern of missing data is available, an incomplete database constrains the set of all possible estimates and this paper provides a characterization of these constraints. An experimental comparison with two popular methods to estimate conditional probability distributions from incomplete data—Gibbs sampling and the EM algorithm—shows a gain in robustness. An application of the RBE to quantify a naive Bayesian classifier from an incomplete data set illustrates its practical relevance.
Deep Abstract Q-Networks

Weexamine the problem of learning and planning on high-dimensional domains with long horizons and sparse rewards. Recent approaches have shown great successes in many Atari 2600 domains. However, domains with long horizons and sparse rewards, such as Montezuma’s Revenge and Venture, remain challenging for existing methods.  using abstraction [5, 13] have shown to be useful in tackling long-horizon problems. We combine recent techniques of deep reinforcement learning with existing model-based approaches using an expert-provided state abstraction. We construct toy domains that elucidate the problem of long horizons, sparse rewards and high-dimensional inputs, and show that our algorithm significantly outperforms previous methods on these domains. Our abstraction-based approach outperforms Deep QNetworks [11] on Montezuma’s Revenge and Venture, and exhibits backtracking behavior that is absent from previous methods.
Compressive Sensing Based Multi-User Detection for Uplink Grant-Free Non-Orthogonal Multiple Access

Non-orthogonal multiple access (NOMA) has become one of the promising key technologies for future 5G wireless communications to improve spectral efficiency and support massive connectivity. However, in the uplink grant-free NOMA system, the current near-optimal multi-user detection (MUD) based on message passing algorithm (MPA) assumes that the user activity information is exactly known at the receiver, which is impractical yet challenging due to anyone of massive users can randomly enter or leave the system. In this paper, inspired by the observation of user sparsity, we jointly use compressive sensing (CS) and MPA to propose a CS-MPA detector to realize both user activity and data detection for uplink grant-free NOMA. Specifically, the MUD problem is firstly formulated under CS framework by exploiting user sparsity, and then user activity can be detected by sparse signal recovery algorithms in CS. Then, MPA can be performed to reliably detect active users' data. It is shown that the proposed CS-MPA detector with affordable complexity not only outperforms the conventional MPA detector without user activity information, but also achieves very close performance to the genie- knowledge MPA detector with exact knowledge of user activity, especially when the signal-to-noise ratio (SNR) is high.
Neural Sentence Ordering

Sentence ordering is a general and critical task for natural language generation applications. Previous works have focused on improving its performance in an external, downstream task, such as multi-document summarization. Given its importance, we propose to study it as an isolated task. We collect a large corpus of academic texts, and derive a data driven approach to learn pairwise ordering of sentences, and validate the efficacy with extensive experiments. Source codes1 and dataset2 of this paper will be made publicly available.
An Autocomplete Input Box for Semantic Annotation on the Web

A large share of websites today allow users to contribute and manage user-generated content. This content is often in textual form and involves names, terms, and keywords that can be ambiguous and difficult to interpret for other users. Semantic annotation can be used to tackle such issues, but this technique has been adopted by only a few websites. This may be attributed to a lack of a standard web input component that allows users to simply and efficiently annotate text. In this paper, we introduce an autocomplete-enabled annotation box that supports users in associating their text with DBpedia resources as they type. This web component can replace existing input fields and does not require particular user skills. Furthermore, it can be used by semantic web developers as a user interface for advanced semantic search and data processing back-ends. Finally, we validate the approach with a preliminary user study.
Schema-agnostic vs Schema-based Configurations for Blocking Methods on Homogeneous Data

Entity Resolution constitutes a core task for data integration that, due to its quadratic complexity, typically scales to large datasets through blocking methods. These can be configured in two ways. The schema-based configuration relies on schema information in order to select signatures of high distinctiveness and low noise, while the schema-agnostic one treats every token from all attribute values as a signature. The latter approach has significant potential, as it requires no fine-tuning by human experts and it applies to heterogeneous data. Yet, there is no systematic study on its relative performance with respect to the schema-based configuration. This work covers this gap by comparing analytically the two configurations in terms of effectiveness, time efficiency and scalability. We apply them to 9 established blocking methods and to 11 benchmarks of structured data. We provide valuable insights into the internal functionality of the blocking methods with the help of a novel taxonomy. Our studies reveal that the schema-agnostic configuration offers unsupervised and robust definition of blocking keys under versatile settings, trading a higher computational cost for a consistently higher recall than the schema-based one. It also enables the use of state-of-the-art blocking methods without schema knowledge.
Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees

Efficient methods for storing and querying are critical for scaling high-order m-gram language models to large corpora. We propose a language model based on compressed suffix trees, a representation that is highly compact and can be easily held in memory, while supporting queries needed in computing language model probabilities on-the-fly. We present several optimisations which improve query runtimes up to 2500×, despite only incurring a modest increase in construction time and memory usage. For large corpora and high Markov orders, our method is highly competitive with the state-of-the-art KenLM package. It imposes much lower memory requirements, often by orders of magnitude, and has runtimes that are either similar (for training) or comparable (for querying).
PGX.D: a fast distributed graph processing engine

Graph analysis is a powerful method in data analysis. Although several frameworks have been proposed for processing large graph instances in distributed environments, their performance is much lower than using efficient single-machine implementations provided with enough memory. In this paper, we present a fast distributed graph processing system, namely PGX.D. We show that PGX.D outperforms other distributed graph systems like GraphLab significantly (3x -- 90x). Furthermore, PGX.D on 4 to 16 machines is also faster than an implementation optimized for single-machine execution. Using a fast cooperative context-switching mechanism, we implement PGX.D as a low-overhead, bandwidth-efficient communication framework that supports remote data-pulling patterns. Moreover, PGX.D achieves large traffic reduction and good workload balance by applying selective ghost nodes, edge partitioning, and edge chunking transparently to the user. Our analysis confirms that each of these features is indeed crucial for overall performance of certain kinds of graph algorithms. Finally, we advocate the use of balanced beefy clusters where the sustained random DRAM-access bandwidth in aggregate is matched with the bandwidth of the underlying interconnection fabric.
Integrating Sample-Based Planning and Model-Based Reinforcement Learning

Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, despite their exponentially large state spaces. U nfortunately, these algorithms all require access to a plann er that computes a near optimal policy, and while many traditional MDP algorithms make this guarantee, their computation time grows with the number of states. We show how to replace these over-matched planners with a class of sample-based planners—whose computation time is independent of the number of states—without sacrificing the sampleefficiency guarantees of the overall learning algorithms. T o do so, we define sufficient criteria for a sample-based planne r to be used in such a learning system and analyze two popular sample-based approaches from the literature. We also in troduce our own sample-based planner, which combines the strategies from these algorithms and still meets the criter ia fo integration into our learning system. In doing so, we define the first complete RL solution for compactly represented (ex ponentially sized) state spaces with efficiently learnable dynamics that is both sample efficient and whose computation time does not grow rapidly with the number of states.
Graphene-based nano-antennas for electromagnetic nanocommunications in the terahertz band

Nanotechnology is enabling the development of devices in a scale ranging from one to a few hundred nanometers. Coordination and information sharing among these nano-devices will lead towards the development of future nanonetworks, boosting the range of applications of nanotechnology in the biomédical, environmental and military fields. Despite the major progress in nano-device design and fabrication, it is still not clear how these atomically precise machines will communicate. Recently, the advancements in graphene-based electronics have opened the door to electromagnetic communications in the nano-scale. In this paper, a new quantum mechanical framework is used to analyze the properties of Carbon Nanotubes (CNTs) as nano-dipole antennas. For this, first the transmission line properties of CNTs are obtained using the tight-binding model as functions of the CNT length, diameter, and edge geometry. Then, relevant antenna parameters such as the fundamental resonant frequency and the input impedance are calculated and compared to those of a nano-patch antenna based on a Graphene Nanoribbon (GNR) with similar dimensions. The results show that for a maximum antenna size in the order of several hundred nanometers (the expected maximum size for a nano-device), both a nano-dipole and a nano-patch antenna will be able to radiate electromagnetic waves in the terahertz band (0.1–10.0 THz).
SEGMENTATION OF LIDAR DATA USING THE TENSOR VOTING FRAMEWORK

We present an investigation on the use of Tensor Voting for categorizing LIDAR data into outliers, line elements (e.g. high-voltage power lines), surface patches (e.g. roofs) and volumetric elements (e.g. vegetation). The Reconstruction of man-made objects is a main task of photogrammetry. With the increasing quality and availability of LIDAR sensors, range data is becoming more and more important. With LIDAR sensors it is possible to quickly aquire huge amounts of data. But in contrast to classical systems, where the measurement points are chosen by an operator, the data points do not explicitly correspond to meaningful points of the object, i.e. edges, corners, junctions. To extract these features it is necessary to segment the data into homogeneous regions wich can be processed afterwards. Our approach consists of a two step segmentation. The first one uses the Tensor Voting algorithm. It encodes every data point as a particle which sends out a vector field. This can be used to categorize the pointness, edgeness and surfaceness of the data points. After the categorization of the given LIDAR data points also the regions between the data points are rated. Meaningful regions like edges and junctions, given by the inherent structure of the data, are extracted. In a second step the so labeled points are merged due to a similarity constraint. This similarity constraint is based on a minimum description length principle, encoding and comparing different geometrical models. The output of this segmentation consists of non overlapping geometric objects in three dimensional space. The aproach is evaluated with some examples of Lidar data.
Double-Deck Buck-Boost Converter With Soft Switching Operation

This paper presents a novel two-stage buck-boost converter with a soft switching operation. The proposed converter is constructed of two identical buck-boost converters working in parallel. The converter units are connected to each other by an inductor as a bridge. This inductor plays an important role in the soft switching operation of the converter by maintaining the voltage applied to switches at zero at switching intervals. The utilized method is called the zero-voltage switching. It is shown that the structure of the proposed converter is significantly efficient in the reduction of switching losses, leading to the improvement of the converter efficiency. Moreover, because of the parallel operation of two identical converters, the output voltage and the input current contain fewer ripples than those of a single converter with the same specifications. Also, utilizing only one inductor as an extra element to achieve this goal makes the proposed converter more economical and reliable with a simpler structure. The detailed analysis of the circuit operation is provided in eight modes. The proposed method is implemented in a laboratory test circuit within the range of 100-220 W output power validating the accuracy of the proposed converter.
Defocus Magnification

A blurry background due to shallow depth of field is often desired for photographs such as portraits, but, unfortunately, small point-and-shoot cameras do not permit enough defocus because of the small diameter of their lenses. We present an image-processing technique that increases the defocus in an image to simulate the shallow depth of field of a lens with a larger aperture. Our technique estimates the spatially-varying amount of blur over the image, and then uses a simple image-based technique to increase defocus. We first estimate the size of the blur kernel at edges and then propagate this defocus measure over the image. Using our defocus map, we magnify the existing blurriness, which means that we blur blurry regions and keep sharp regions sharp. In contrast to more difficult problems such as depth from defocus, we do not require precise depth estimation and do not need to disambiguate textureless regions.
Control of a nonholomic mobile robot: Backstepping kinematics into dynamics

A dynamical extension that makes possible the integration of a kinematic controller and a torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping, and asymptotic stability is guaranteed by Lyapunov theory. Moreover, this control algorithm can be applied to the three basic nonholonomic navigation problems: tracking a reference trajectory, path following, and stabilization about a desired posture. The result is a general structure for controlling a mobile robot that can accommodate different control techniques, ranging from a conventional computed-torque controller, when all dynamics are known, to robust-adaptive controllers if this is not the case. A robust-adaptive controller based on neural networks (NNs) is proposed in this work. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the vehicle. On-line NN weight tuning algorithms that do not require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.
CHATTERBOTS, TINYMUDS, and the Turing Test: Entering the Loebner Prize Competition

The Turing Test was proposed by Alan Turing in 1950; he called it the Imitation Game. In 1991 Hu Loebner prize competition, offering a f h Loebner started the 100,000 prize to the author of the first computer program to pass an unrestricted Turing test. Annual competitions are held each year with smaller prizes for the best program on a restricted Turing test. This paper describes the development of one such Turing System, including the technical design of the program and its performance on the first three Loebner Prize competitions. We also discuss the program’s four year development effort, which has depended heavily on constant interaction with people on the Internet via Tinymuds (multiuser network communication servers). Finally, we discuss the design of the Loebner competition itself, and address its usefulness in furthering the development of Artificial Intelligence.
Initial investigations for modeling interior Utilities within 3 D Geo Context : Transforming IFC-interior utility to CityGML / UtilityNetworkADE

3D City models have so far neglected utility networks in built environments, both interior and exterior. Many urban applications, e.g. emergency response or maintenance operations, are looking for such an integration of interior and exterior utility. Interior utility is usually created and maintained using Building Information Model (BIM) systems, while exterior utility is stored, managed and analyzed using GIS. Researchers have suggested that the best approach for BIM/GIS integration is harmonized semantics, which allow formal mapping between the BIM and real world GIS. This paper provides preliminary ideas and directions for how to acquire information from BIM/Industry Foundation Class (IFC) and map it to CityGML utility network Application Domain Extension (ADE). The investigation points out that, in most cases, there is a direct one-to-one mapping between IFC schema and UtilityNetworkADE schema, and only in one case there is one-to-many mapping; related to logical connectivity since there is no exact concept to represent the case in UtilityNetworkADE. Many examples are shown of partial IFC files and their possible translation in order to be represented in UtilityNetworkADE classes. DRAFT VERSION of the paper to be published in Kolbe, T. H.; König, G.; Nagel, C. (Eds.) 2011: Advances in 3D Geo-Information Sciences, ISBN 978-3-642-12669-7 Series Editors: Cartwright, W., Gartner, G., Meng, L., Peterson, M.P., ISSN: 1863-2246 5th International 3D GeoInfo Conference, November 3-4, 2010, Berlin, Germany 1 2 I. Hijazi, M. Ehlers, S. Zlatanova, T. Becker, L.Berlo
A Perceptual Colour Segmentation Algorithm

This paper presents a simple method for segmenting colour regions into categories like red, green, blue, and yellow. We are interested in studying how colour categories influence colour selection during scientific visualization. The ability to name individual colours is also important in other problem domains like real-time displays, user-interface design, and medical imaging systems. Our algorithm uses the Munsell and CIE LUV colour models to automatically segment a colour space like RGB or CIE XYZ into ten colour categories. Users are then asked to name a small number of representative colours from each category. This provides three important results: a measure of the perceptual overlap between neighbouring categories, a measure of a category’s strength, and a user-chosen name for each strong category. We evaluated our technique by segmenting known colour regions from the RGB, HSV, and CIE LUV colour models. The names we obtained were accurate, and the boundaries between different colour categories were well defined. We concluded our investigation by conducting an experiment to obtain user-chosen names and perceptual overlap for ten colour categories along the circumference of a colour wheel in CIE LUV.
Concepts and methods in fault-tolerant control

Faults in automated processes will often cause undesired reactions and shut-down of a controlled plant, and the consequences could be damage to technical parts of the plant, to personnel or the environment.Faulttolerant control combines diagnosis with control methods to handle faults in an intelligent way. The aim is to prevent that simple faults develop into serious failure and hence increase plant availability and reduce the risk of safety hazards. Fault-tolerant control merges several disciplines into a common framework to achieve these goals. The desired features are obtained through on-line fault diagnosis, automatic condition assessment and calculation of appropriate remedial actions to avoid certain consequences of a fault. The envelope of the possible remedial actions is very wide. Sometimes, simple re-tuning can suffice. In other cases, accommodation of the fault could be achieved by replacing a measurement from a faulty sensor by an estimate. In yet other situations, complex reconfiguration or online controller redesign is required. This paper gives an overview of recent tools to analyze and explore structure and other fundamental properties of an automated system such that any inherent redundancy in the controlled process can be fully utilized to maintain availability, even though faults may occur. .
Evolution toward 5G multi-tier cellular wireless networks: An interference management perspective

The evolving fifth generation (5G) cellular wireless networks are envisioned to overcome the fundamental challenges of existing cellular networks, for example, higher data rates, excellent end-to-end performance, and user-coverage in hot-spots and crowded areas with lower latency, energy consumption, and cost per information transfer. To address these challenges, 5G systems will adopt a multi-tier architecture consisting of macrocells, different types of licensed small cells, relays, and device-to-device (D2D) networks to serve users with different quality-of-service (QoS) requirements in a spectrum and energy-efficient manner. Starting with the visions and requirements of 5G multi-tier networks, this article outlines the challenges of interference management (e.g. power control, cell association) in these networks with shared spectrum access (i.e. when the different network tiers share the same licensed spectrum). It is argued that the existing interference management schemes will not be able to address the interference management problem in prioritized 5G multi-tier networks where users in different tiers have different priorities for channel access. In this context a survey and qualitative comparison of the existing cell association and power control schemes is provided to demonstrate their limitations for interference management in 5G networks. Open challenges are highlighted and guidelines are provided to modify the existing schemes in order to overcome these limitations and make them suitable for the emerging 5G systems.
Moving vehicle detection and tracking in unstructured environments

The detection and tracking of moving vehicles is a necessity for collision-free navigation. In natural unstructured environments, motion-based detection is challenging due to low signal to noise ratio. This paper describes our approach for a 14 km/h fast autonomous outdoor robot that is equipped with a Velodyne HDL-64E S2 for environment perception. We extend existing work that has proven reliable in urban environments. To overcome the unavailability of road network information for background separation, we introduce a foreground model that incorporates geometric as well as temporal cues. Local shape estimates successfully guide vehicle localization. Extensive evaluation shows that the system works reliably and efficiently in various outdoor scenarios without any prior knowledge about the road network. Experiments with our own sensor as well as on publicly available data from the DARPA Urban Challenge revealed more than 96% correctly identified vehicles.