nils-herrmann/clean_scirepeval huggingface dataset:
Planning and Control for Collision-Free Cooperative Aerial Transportation

This paper presents planning and control synthesis for multiple aerial manipulators to transport a common object. Each aerial manipulator that consists of a hexacopter and a two-degree-of-freedom robotic arm is controlled by an augmented adaptive sliding mode controller based on a closed-chain robot dynamics. We propose a motion planning algorithm by exploiting rapidly exploring random tree star (RRT*) and dynamic movement primitives (DMPs). The desired path for each aerial manipulator is obtained by using RRT* with Bezier curve, which is designed to handle environmental obstacles, such as buildings or equipments. During aerial transportation, to avoid unknown obstacle, DMPs modify the trajectory based on the virtual leader–follower structure. By the combination of RRT* and DMPs, the cooperative aerial manipulators can carry a common object to keep reducing the interaction force between multiple robots while avoiding an obstacle in the unstructured environment. To validate the proposed planning and control synthesis, two experiments with multiple custom-made aerial manipulators are presented, which involve user-guided trajectory and RRT*-planned trajectory tracking in unstructured environments.Note to Practitioners—This paper presents a viable approach to autonomous aerial transportation using multiple aerial manipulators equipped with a multidegree-of-freedom robotic arm. Existing approaches for cooperative manipulation based on force decomposition or impedance-based control often require a heavy or expensive force/torque sensor. However, this paper suggests a method without using a heavy or expensive force/torque sensor based on closed-chain dynamics in joint space and rapidly exploring random tree star (RRT*) that generates the desired trajectory of aerial manipulators. Unlike conventional RRT*, in this paper, our method can also avoid an unknown moving obstacle during aerial transportation by exploiting RRT* and dynamic movement primitives. The proposed planning and control synthesis is tested to demonstrate performance in a lab environment with two custom-made aerial manipulators and a common object.
An Interleaved Boost Converter With Zero-Voltage Transition

This paper proposes a novel soft-switching interleaved boost converter composed of two shunted elementary boost conversion units and an auxiliary inductor. This converter is able to turn on both the active power switches at zero voltage to reduce their switching losses and evidently raise the conversion efficiency. Since the two parallel-operated elementary boost units are identical, operation analysis and design for the converter module becomes quite simple. A laboratory test circuit is built, and the circuit operation shows satisfactory agreement with the theoretical analysis. The experimental results show that this converter module performs very well with the output efficiency as high as 95%.
The neuropsychopharmacology of fronto-executive function: monoaminergic modulation.

We review the modulatory effects of the catecholamine neurotransmitters noradrenaline and dopamine on prefrontal cortical function. The effects of pharmacologic manipulations of these systems, sometimes in comparison with the indoleamine serotonin (5-HT), on performance on a variety of tasks that tap working memory, attentional-set formation and shifting, reversal learning, and response inhibition are compared in rodents, nonhuman primates, and humans using, in a behavioral context, several techniques ranging from microiontophoresis and single-cell electrophysiological recording to pharmacologic functional magnetic resonance imaging. Dissociable effects of drugs and neurotoxins affecting these monoamine systems suggest new ways of conceptualizing state-dependent fronto-executive functions, with implications for understanding the molecular genetic basis of mental illness and its treatment.
A Hybrid Symmetrical Voltage Multiplier

Voltage multiplier circuits are widely used in many high-voltage/low-current applications. A conventional symmetrical voltage multiplier (SVM) has much better performance, when compared with its half-wave counterpart. However, it requires a high-voltage transformer (HVT) with center-tapped secondary to perform its push-pull kind of operation. The design of an HVT with center-tapped secondary is relatively complex. This paper proposes a hybrid SVM (HSVM) for dc high-voltage applications. The multiplier is formed by cascading a diode-bridge rectifier and an SVM with diode-bridge rectifier as the first stage of multiplier. The proposed topology saves two high-voltage capacitors and requires only one secondary winding of HVT. Besides, it has lesser voltage drop and faster transient response at start-up, when compared with conventional SVM. The feasibility of the proposed HSVM is validated both by simulation and experimental results of a laboratory scaled-down prototype.
VETIS TOOL FOR EDITING AND TRANSFORMING SBVR BUSINESS VOCABULARIES AND BUSINESS RULES INTO UML & OCL MODELS

The OMG SBVR standard is the most mature abstract representation for Business semantics; however, the complexity of SBVR metamodel prevents its broad and rapid usage in business communities. There are a few SBVR implementations yet, but the popularity of SBVR is growing as different interest-groups are finding the variety of ways for applying the SBVR standard. The goal of this paper is to present the VeTIS tool capable for editing SBVR Business Vocabularies and Business Rules and transforming them into UML class models supplemented with OCL constraints. It is integrated into MagicDraw UML CASE tool and organically combines with simple development process, which pursues defining requirements via use cases and modelling business processes via activities.
A Plastic Optical Fiber Microbend Sensor Used as a Low-Cost Anti-Squeeze Detector

This paper presents a low-cost fiber optic anti-squeeze sensor, suitable for use in high-volume cost driven applications. The sensor, used as a car window anti-squeeze detector, complies with EC 2000/4/EC and U.S. Federal Motor Vehicle Safety Standard No. 118. The proposed design relies on microbend, loss induced in plastic optical fiber (POF). The sensor assembly employs only low-cost optoelectronic components readily in use by automotive and consumer markets. The lifetime tests have been performed on prototype sensors. The results show that the proposed design can easily reach and exceed 100 000 operational cycles even though it relies on flexible and all-polymer design. The proposed sensor design can be easily extended to other nonautomotive applications, such as protection devices in automatic door openers.
Laplacian Reconstruction and Refinement for Semantic Segmentation

CNN architectures have terrific recognition performance but rely on spatial pooling which makes it difficult to adapt them to tasks that require dense pixel-accurate labeling. This paper makes two contributions: (1) We demonstrate that while the apparent spatial resolution of convolutional feature maps is low, the high-dimensional feature representation contains significant sub-pixel localization information. (2) We describe a multi-resolution reconstruction architecture, akin to a Laplacian pyramid, that uses skip connections from higher resolution feature maps to successively refine segment boundaries reconstructed from lower resolution maps. This approach yields state-of-the-art semantic segmentation results on PASCAL without resorting to more complex CRF or detection driven architectures.
How to handle ARP in a software-defined network

The Address Resolution Protocol (ARP) enables communication between IP-speaking nodes in a local network by reconstructing the hardware (MAC) address associated with the IP address of an interface. This is not needed in a Software-Defined Network (SDN), because each device can forward packets without the need to learn this association. We tackle the interoperability problem arising between standard network devices (end systems, routers), that rely on ARP, and SDN datapaths, that do not handle ARP packets natively. In particular, we propose a general approach to handle ARP in a SDN, that is applicable in several network scenarios, is transparent for existing devices, and can coexist with any packet forwarding logic implemented in the controller. Our approach reduces ARP traffic by confining it to the edge of SDNs and requires a minimal set of flow entries in the datapaths. We argument about its applicability and confirm it with experiments performed on SDN datapaths from a range of different vendors.
Understanding uncertainty and reducing vulnerability : lessons from resilience thinking

Vulnerability is registered not by exposure to hazards alone; it also resides in the resilience of the system experiencing the hazard. Resilience (the capacity of a system to absorb recurrent disturbances, such as natural disasters, so as to retain essential structures, processes and feedbacks) is important for the discussion of vulnerability for three reasons: (1) it helps evaluate hazards holistically in coupled human–environment systems, (2) it puts the emphasis on the ability of a system to deal with a hazard, absorbing the disturbance or adapting to it, and (3) it is forward-looking and helps explore policy options for dealing with uncertainty and future change. Building resilience into human–environment systems is an effective way to cope with change characterized by surprises and unknowable risks. There seem to be four clusters of factors relevant to building resilience: (1) learning to live with change and uncertainty, (2) nurturing various types of ecological, social and political diversity for increasing options and reducing risks, (3) increasing the range of knowledge for learning and problem-solving, and (4) creating opportunities for self-organization, including strengthening of local institutions and building cross-scale linkages and problem-solving networks.
Joint Embedding Models for Textual and Social Analysis

In online social networks, users openly interact, share content, and endorse each other. Although the data is interconnected, previous research has primarily focused on modeling the social network behavior separately from the textual content. Here we model the data in a holistic way, taking into account connections between social behavior and content. Specifically, we define multiple decision tasks over the relationships between users and the content generated by them. We show, on a real world dataset, that a learning a joint embedding (over user characteristics and language) and using joint prediction (based on intraand inter-task constraints) produces consistent gains over (1) learning specialized embeddings, and (2) predicting locally w.r.t. a single task, with or without constraints.
Current sensor in PCB technology

A novel DC/AC current sensor works on the fluxgate principle. The core in the form of a 7/10-mm ring made of electrodeposited permalloy is sandwiched in the middle of a printed circuit board (PCB), whereas the sensor excitation winding is also integrated in the copper layers of the PCB. To lower the sensor power consumption, the excitation winding was tuned by a parallel capacitor. The peak-peak/rms ratio of 5.2 was achieved for the excitation frequency of 25 kHz. The open-loop sensor has 100-mV/A sensitivity; the characteristics have a linearity error of 10% and hysteresis below 0.1% in the 1-A range.
Using phrases and document metadata to improve topic modeling of clinical reports

Probabilistic topic models provide an unsupervised method for analyzing unstructured text, which have the potential to be integrated into clinical automatic summarization systems. Clinical documents are accompanied by metadata in a patient's medical history and frequently contains multiword concepts that can be valuable for accurately interpreting the included text. While existing methods have attempted to address these problems individually, we present a unified model for free-text clinical documents that integrates contextual patient- and document-level data, and discovers multi-word concepts. In the proposed model, phrases are represented by chained n-grams and a Dirichlet hyper-parameter is weighted by both document-level and patient-level context. This method and three other Latent Dirichlet allocation models were fit to a large collection of clinical reports. Examples of resulting topics demonstrate the results of the new model and the quality of the representations are evaluated using empirical log likelihood. The proposed model was able to create informative prior probabilities based on patient and document information, and captured phrases that represented various clinical concepts. The representation using the proposed model had a significantly higher empirical log likelihood than the compared methods. Integrating document metadata and capturing phrases in clinical text greatly improves the topic representation of clinical documents. The resulting clinically informative topics may effectively serve as the basis for an automatic summarization system for clinical reports.
Attention-over-Attention Neural Networks for Reading Comprehension

Cloze-style queries are representative problems in reading comprehension. Over the past few months, we have seen much progress that utilizing neural network approach to solve Cloze-style questions. In this paper, we present a novel model called attention-over-attention reader for the Cloze-style reading comprehension task. Our model aims to place another attention mechanism over the document-level attention, and induces “attended attention” for final predictions. Unlike the previous works, our neural network model requires less pre-defined hyper-parameters and uses an elegant architecture for modeling. Experimental results show that the proposed attentionover-attention model significantly outperforms various state-of-the-art systems by a large margin in public datasets, such as CNN and Children’s Book Test datasets.
Diagnostic Classifiers Revealing how Neural Networks Process Hierarchical Structure

We investigate how neural networks can be used for hierarchical, compositional semantics. To this end, we define the simple but nontrivial artificial task of processing nested arithmetic expressions and study whether different types of neural networks can learn to add and subtract. We find that recursive neural networks can implement a generalising solution, and we visualise the intermediate steps: projection, summation and squashing. We also show that gated recurrent neural networks, which process the expressions incrementally, perform surprisingly well on this task: they learn to predict the outcome of the arithmetic expressions with reasonable accuracy, although performance deteriorates with increasing length. To analyse what strategy the recurrent network applies, visualisation techniques are less insightful. Therefore, we develop an approach where we formulate and test hypotheses on what strategies these networks might be following. For each hypothesis, we derive predictions about features of the hidden state representations at each time step, and train ’diagnostic classifiers’ to test those predictions. Our results indicate the networks follow a strategy similar to our hypothesised ’incremental strategy’.
Seven element wideband planar log-periodic antenna for TVWS base station

This paper presents design and simulation of a wide band antenna for base station in the TV White Space (TVWS) spectrum band operating between 470 MHz-700 MHz. Concept of printed Log Periodic Dipole Array (LPDA), which provides wide bandwidth, has been used to realize wide band antenna. The antenna elements are printed on low cost FR4 substrate with εr = 4.4 and tan δ = 0.02. These elements are printed alternatively on both side of the substrate. Total volume of the antenna is 303 × 162.3 × 1.6 mm3. To reduce the size, scaling factor (τ) for this design is considered as 0.89 and the relative spacing (σ) is chosen as 0.054. The antenna is fed at the base of smallest element. The antenna shows an impedance bandwidth for VSWR ≤ 2 in the frequency range of 470 MHz–700 MHz. The gain of this antenna is between 5.3 dB to 6.5 dB in the entire band of operation. The radiation pattern shows end fire behavior with uniform radiation pattern in both E and H-planes with maximum front to back lobe ratio (F/B) of 30 dB.
Psychophysical magic: rendering the visible ‘invisible’

What are the neural correlates of conscious visual awareness? Tackling this question requires contrasting neural correlates of stimulus processing culminating in visual awareness with neural correlates of stimulus processing unaccompanied by awareness. To produce these two neural states, one must be able to erase an otherwise visible stimulus from awareness. This article describes and assesses visual phenomena involving dissociation of physical stimulation and conscious awareness: degraded stimulation, visual masking, visual crowding, bistable figures, binocular rivalry, motion-induced blindness, inattentional blindness, change blindness and attentional blink. No single approach stands above the others, but those producing changing visual awareness despite invariant physical stimulation are clearly preferable. Such phenomena can help lead us ultimately to a comprehensive account of the neural correlates of conscious awareness.
From Institutions to Code: Towards Automated Generation of Smart Contracts

Blockchain technology has emerged as a solution to consistency problems in peer to peer networks. By now, it has matured as a solution to a range of use cases in which it can effectively provide the notion of third party trust without the need for a trusted (physical) third party, which makes it an attractive coordination mechanism for distributed systems. To promote the wide adoption of this technology, we yet lack mechanisms that make the specification and interpretation of smart contracts accessible to a broader audience. In this work, we propose a modeling approach that supports the semi-automated translation of human-readable contract representations into computational equivalents in order to enable the codification of laws into verifiable and enforceable computational structures that reside within a public blockchain. We identify smart contract components that correspond to real world institutions, and propose a mapping that we operationalize using a domain-specific language in order to support the contract modeling process. We explore this capability based on selected examples and plot out directions for future research on smart contracts.
Large-area synthesis of high-quality and uniform graphene films on copper foils.

Graphene has been attracting great interest because of its distinctive band structure and physical properties. Today, graphene is limited to small sizes because it is produced mostly by exfoliating graphite. We grew large-area graphene films of the order of centimeters on copper substrates by chemical vapor deposition using methane. The films are predominantly single-layer graphene, with a small percentage (less than 5%) of the area having few layers, and are continuous across copper surface steps and grain boundaries. The low solubility of carbon in copper appears to help make this growth process self-limiting. We also developed graphene film transfer processes to arbitrary substrates, and dual-gated field-effect transistors fabricated on silicon/silicon dioxide substrates showed electron mobilities as high as 4050 square centimeters per volt per second at room temperature.
Extrinsic calibration of a fisheye multi-camera setup using overlapping fields of view

It is well known that the robustness of many computer vision algorithms can be improved by employing large field of view cameras, such as omnidirectional cameras. To avoid obstructions in the field of view, such cameras need to be mounted in an exposed position. Alternatively, a multicamera setup can be used. However, this requires the extrinsic calibration to be known. In the present work, we propose a method to calibrate a fisheye multi-camera rig, mounted on a mobile platform. The method only relies on feature correspondences from pairwise overlapping fields of view of adjacent cameras. In contrast to existing approaches, motion estimation or specific motion patterns are not required. To compensate for the large extent of multi-camera setups and corresponding viewpoint variations, as well as geometrical distortions caused by fisheye lenses, captured images are mapped into virtual camera views such that corresponding image regions coincide. To this end, the scene geometry is approximated by the ground plane in close proximity and by infinitely far away objects elsewhere. As a result, low complexity feature detectors and matchers can be employed. The approach is evaluated using a setup of four rigidly coupled and synchronized wide angle fisheye cameras that were attached to four sides of a mobile platform. The cameras have pairwise overlapping fields of view and baselines between 2.25 and 3 meters.
Automated mitosis detection based on eXclusive Independent Component Analysis

In this paper, we propose an approach for automated mitosis detection, which provides critical information during performing breast cancer prognosis. Essentially, the problem of mitotic detection involves irregular shape object classification. It is a very challenging task. In this paper, a novel algorithm, named eXclusive Independent Component Analysis (XICA) is proposed. The XICA is an extension of a generic ICA, but focusing the components of differences (called exclusive basis set) between two classes of training patterns rather than the major (independent) components. Based on the residuals obtained from the relative computing involving the exclusive basis set of the relative training patterns, the automated mitosis detection is performed. By computing the residual of the relative exclusive basis set, we are able to classify the given testing patterns. The proposed approach has been tested on a mitosis image set provided by a ICPR2012 contest. It contains 226 mitosis in 35 color images. It achieved accurate rate 100% in training patterns and 83.513% in testing patterns.
Powergrading: a Clustering Approach to Amplify Human Effort for Short Answer Grading

We introduce a new approach to the machine-assisted grading of short answer questions. We follow past work in automated grading by first training a similarity metric between student responses, but then go on to use this metric to group responses into clusters and subclusters. The resulting groupings allow teachers to grade multiple responses with a single action, provide rich feedback to groups of similar answers, and discover modalities of misunderstanding among students; we refer to this amplification of grader effort as “powergrading.” We develop the means to further reduce teacher effort by automatically performing actions when an answer key is available. We show results in terms of grading progress with a small “budget” of human actions, both from our method and an LDA-based approach, on a test corpus of 10 questions answered by 698 respondents.