nils-herrmann/clean_scirepeval huggingface dataset:
Multi-fingered robotic hand based on hybrid mechanism of tendon-driven and jamming transition

This study presents a novel four-fingered robotic hand to attain a soft contact and high stability under disturbances while holding an object. Each finger is constructed using a tendon-driven skeleton, granular materials corresponding to finger pulp, and a deformable rubber skin. This structure provides soft contact with an object, as well as high adaptation to its shape. Even if the object is deformable and fragile, a grasping posture can be formed without deforming the object. If the air around the granular materials in the rubber skin and jamming transition is vacuumed, the grasping posture can be fixed and the object can be grasped firmly and stably. A high grasping stability under disturbances can be attained. Additionally, the fingertips can work as a small jamming gripper to grasp an object smaller than a fingertip. An experimental investigation indicated that the proposed structure provides a high grasping force with a jamming transition with high adaptability to the object's shape.
The aerodynamic effects of wing rotation and a revised quasi-steady model of flapping flight.

We used a dynamically scaled model insect to measure the rotational forces produced by a flapping insect wing. A steadily translating wing was rotated at a range of constant angular velocities, and the resulting aerodynamic forces were measured using a sensor attached to the base of the wing. These instantaneous forces were compared with quasi-steady estimates based on translational force coefficients. Because translational and rotational velocities were constant, the wing inertia was negligible, and any difference between measured forces and estimates based on translational force coefficients could be attributed to the aerodynamic effects of wing rotation. By factoring out the geometry and kinematics of the wings from the rotational forces, we determined rotational force coefficients for a range of angular velocities and different axes of rotation. The measured coefficients were compared with a mathematical model developed for two-dimensional motions in inviscid fluids, which we adapted to the three-dimensional case using blade element theory. As predicted by theory, the rotational coefficient varied linearly with the position of the rotational axis for all angular velocities measured. The coefficient also, however, varied with angular velocity, in contrast to theoretical predictions. Using the measured rotational coefficients, we modified a standard quasi-steady model of insect flight to include rotational forces, translational forces and the added mass inertia. The revised model predicts the time course of force generation for several different patterns of flapping kinematics more accurately than a model based solely on translational force coefficients. By subtracting the improved quasi-steady estimates from the measured forces, we isolated the aerodynamic forces due to wake capture.
Stride Length Estimation with Deep Learning

Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase. We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length. The model is trained on a publicly available and clinically relevant benchmark dataset consisting of 1220 strides from 101 geriatric patients. Evaluation is done in a 10-fold cross validation and for three different stride definitions. Even though best results are achieved with strides defined from mid-stance to mid-stance with average accuracy and precision of 0.01 ± 5.37 cm, performance does not strongly depend on stride definition. The achieved precision outperforms state-of-the-art methods evaluated on this benchmark dataset by 3.0 cm (36%). Due to the independence of stride definition, the proposed method is not subject to the methodological constrains that limit applicability of state-of-the-art double integration methods. Furthermore, precision on the benchmark dataset could be improved. With more precise mobile stride length estimation, new insights to the progression of neurological disease or early indications might be gained. Due to the independence of stride definition, previously uncharted diseases in terms of mobile gait analysis can now be investigated by re-training and applying the proposed method.
Microstrip Yagi-Uda Antenna for 2 . 45 GHz RFID Handheld Reader

This paper proposes a compact microstrip Yagi-Uda antenna for 2.45 GHz radio frequency identification (RFID) handheld reader applications. The proposed antenna is etched on a piece of FR4 substrate with an overall size of 65 mm × 55 mm ×1.6 mm and consists of a microstrip balun, a dipole, and a director. The ground plane is designed to act as a reflector that contributes to enhancing the antenna gain. The measured 10-dB return loss bandwidth and peak gain achieved by the proposed antenna are 380 MHz and 7.5 dBi, respectively. In addition, a parametric study is conducted to facilitate the design and optimization processes for engineers.
Topics in PDE-based image processing

The content of this dissertation lies at the intersection of analysis and applications of PDE to image processing and computer vision applications. In the first part of this thesis, we propose e fficient and accurate algorithms for computing certain area preserving geometric motions of curves in the plane, such as area preserving motion by curvature. These schemes are based on a new class of di ffusion generated motion algorithms using signed distance functions. In particular, they alternate two very simple and fast operations, namely convolution with the Gaussian kernel and construction of the distance function, to generate the desired geometric flow in an unconditionally stable manner. We present applications of these area preserving flows to large scale simulations of coarsening, and inverse problems. In the second part of this dissertation, we study the discrete version of a family of illposed, nonlinear di ffusion equations of order 2 n. The fourth order ( n = 2) version of these equations constitutes our main motivation, as it appears prominently in image processing and computer vision literature. It was proposed by You and Kaveh as a model for denoising images while maintaining sharp object boundaries (edges). The second order equation (n = 1) corresponds to another famous model from image processing, namely Perona and Malik’s anisotropic di ffusion, and was studied in earlier papers. The equations studied in this paper are high order analogues of the Perona-Malik equation, and like the second order model, their continuum versions violate parabolicity and hence lack well-posedness theory. We follow a recent technique from Kohn and Otto, and prove a weak upper bound
The Architecture of an English-Text-to-Sign-Languages Translation System

We present an overview of the overall architecture of the language-processing component of an English-Text to Sign-Languages translation system. Initially relevant aspects of sign languages and the in uence these have on the design decisions in the translation system are described. Focusing on the analysis stage, we then present the syntactic, semantic and discourse oriented natural language processing (NLP) techniques, that are implemented to generate the DRS-based semantic representation from English text. We also give an account on the pronoun resolution algorithm, which is tightly coupled with the DRS creation.
Colorization as a Proxy Task for Visual Understanding

We investigate and improve self-supervision as a drop-in replacement for ImageNet pretraining, focusing on automatic colorization as the proxy task. Self-supervised training has been shown to be more promising for utilizing unlabeled data than other, traditional unsupervised learning methods. We build on this success and evaluate the ability of our self-supervised network in several contexts. On VOC segmentation and classification tasks, we present results that are state-of-the-art among methods not using ImageNet labels for pretraining representations. Moreover, we present the first in-depth analysis of self-supervision via colorization, concluding that formulation of the loss, training details and network architecture play important roles in its effectiveness. This investigation is further expanded by revisiting the ImageNet pretraining paradigm, asking questions such as: How much training data is needed? How many labels are needed? How much do features change when fine-tuned? We relate these questions back to self-supervision by showing that colorization provides a similarly powerful supervisory signal as various flavors of ImageNet pretraining.
Characterizing diabetes, diet, exercise, and obesity comments on Twitter

Social media provide a platform for users to express their opinions and share information. Understanding public health opinions on social media, such as Twitter, offers a unique approach to characterizing common health issues such as diabetes, diet, exercise, and obesity (DDEO); however, collecting and analyzing a large scale conversational public health data set is a challenging research task. The goal of this research is to analyze the characteristics of the general public’s opinions in regard to diabetes, diet, exercise and obesity (DDEO) as expressed on Twitter. A multi-component semantic and linguistic framework was developed to collect Twitter data, discover topics of interest about DDEO, and analyze the topics. From the extracted 4.5 million tweets, 8% of tweets discussed diabetes, 23.7% diet, 16.6% exercise, and 51.7% obesity. The strongest correlation among the topics was determined between exercise and obesity (p < .0002). Other notable correlations were: diabetes and obesity (p < .0005), and diet and obesity (p < .001). DDEO terms were also identified as subtopics of each of the DDEO topics. The frequent subtopics discussed along with “Diabetes”, excluding the DDEO terms themselves, were blood pressure, heart attack, yoga, and Alzheimer. The non-DDEO subtopics for “Diet” included vegetarian, pregnancy, celebrities, weight loss, religious, and mental health, while subtopics for “Exercise” included computer games, brain, fitness, and daily plan. Non-DDEO subtopics for “Obesity” included Alzheimer, cancer, and children. With 2.67 billion social media users in 2016, publicly available data such as Twitter posts can be utilized to support clinical providers, public health experts, and social scientists in better understanding common public opinions in regard to diabetes, diet, exercise, and obesity.
Dialogue as skill: training a health professions workforce that can talk about race and racism.

Efforts in the field of multicultural education for the health professions have focused on increasing trainees' knowledge base and awareness of other cultures, and on teaching technical communication skills in cross-cultural encounters. Yet to be adequately addressed in training are profound issues of racial bias and the often awkward challenge of cross-racial dialogue, both of which likely play some part in well-documented racial disparities in health care encounters. We seek to establish the need for the skill of dialoguing explicitly with patients, colleagues, and others about race and racism and its implications for patient well-being, for clinical practice, and for the ongoing personal and professional development of health care professionals. We present evidence establishing the need to go beyond training in interview skills that efficiently "extract" relevant cultural and clinical information from patients. This evidence includes concepts from social psychology that include implicit bias, explicit bias, and aversive racism. Aiming to connect the dots of diverse literatures, we believe health professions educators and institutional leaders can play a pivotal role in reducing racial disparities in health care encounters by actively promoting, nurturing, and participating in this dialogue, modeling its value as an indispensable skill and institutional priority.
A New Energy-Efficient Topology for Wireless Body Area Networks

Wireless body area networks consist of several devices placed on the human body, sensing vital signs and providing remote recognition of health disorders. Low power consumption is crucial in these networks. A new energy-efficient topology is provided in this paper, considering relay and sensor nodes' energy consumption and network maintenance costs. In this topology design, relay nodes, placed on the cloth, are used to help the sensor nodes forwarding data to the sink. Relay nodes' situation is determined such that the relay nodes' energy consumption merges the uniform distribution. Simulation results show that the proposed method increases the lifetime of the network with nearly uniform distribution of the relay nodes' energy consumption. Furthermore, this technique simultaneously reduces network maintenance costs and continuous replacements of the designer clothing. The proposed method also determines the way by which the network traffic is split and multipath routed to the sink.
Hypoplasia of the trapezius and history of ipsilateral transient neonatal brachial plexus palsy.

We present two children with hypoplasia of the left trapezius muscle and a history of ipsilateral transient neonatal brachial plexus palsy without documented trapezius weakness. Magnetic resonance imaging in these patients with unilateral left hypoplasia of the trapezius revealed decreased muscles in the left side of the neck and left supraclavicular region on coronal views, decreased muscle mass between the left splenius capitis muscle and the subcutaneous tissue at the level of the neck on axial views, and decreased size of the left paraspinal region on sagittal views. Three possibilities can explain the association of hypoplasia of the trapezius and obstetric brachial plexus palsy: increased vulnerability of the brachial plexus to stretch injury during delivery because of intrauterine trapezius weakness, a casual association of these two conditions, or an erroneous diagnosis of brachial plexus palsy in patients with trapezial weakness. Careful documentation of neck and shoulder movements can distinguish among shoulder weakness because of trapezius hypoplasia, brachial plexus palsy, or brachial plexus palsy with trapezius hypoplasia. Hence, we recommend precise documentation of neck movements in the initial description of patients with suspected neonatal brachial plexus palsy.
A Practical Approach to Tolerance Allocation

— This paper reviews the currently available tolerance allocation tools and identifies their shortcomings which are then used to create a better tool suitable for industrial applications. The obstacle in the application of existing tolerance allocation tools appears to be the lack of up-to-date cost data. In this paper, a tolerance allocation strategy that is simple and practical is presented. It is suitable for use in a Concurrent Engineering (CE) environment and incorporates some of the existing methods/tools viz. method of guided iteration, manufacturability rating technique-baseline and decision matrix. The proposed strategy is interactive and utilizes the expertise of all the members of the CE team. It uses production time as the basis for optimization, thus avoiding the need for cost-tolerance
A Graph Search Heuristic for Shortest Distance Paths

This paper presents a heuristic for guiding A* search for finding the shortest distance path between two vertices in a connected, undirected, and explicitly stored graph. The heuristic requires a small amount of data to be stored at each vertex. The heuristic has application to quickly detecting relationships between two vertices in a large information or knowledge network. We compare the performance of this heuristic with breadth-first search on graphs with various topological properties. The results show that one or more orders of magnitude improvement in the number of vertices expanded is possible for large graphs, including Poisson random graphs.
Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters

Dynamic Camera Clusters (DCCs) are multi-camera systems where one or more cameras are mounted on actuated mechanisms such as a gimbal. Existing methods for DCC calibration rely on joint angle measurements to resolve the time-varying transformation between the dynamic and static camera. This information is usually provided by motor encoders, however, joint angle measurements are not always readily available on off-the-shelf mechanisms. In this paper, we present an encoderless approach for DCC calibration which simultaneously estimates the kinematic parameters of the transformation chain as well as the unknown joint angles. We also demonstrate the integration of an encoderless gimbal mechanism with a state-of-the art VIO algorithm, and show the extensions required in order to perform simultaneous online estimation of the joint angles and vehicle localization state. The proposed calibration approach is validated both in simulation and on a physical DCC composed of a 2-DOF gimbal mounted on a UAV. Finally, we show the experimental results of the calibrated mechanism integrated into the OKVIS VIO package, and demonstrate successful online joint angle estimation while maintaining localization accuracy that is comparable to a standard static multi-camera configuration.
An Intermodal Dispatch Support System for Intermodal Transport Chains

A critical problem in an intermodal transport chain is the direct meet at the transhipment nodes. This requires information technology and modern communication facilities as well as close collaboration between all the concerned transport operators in the chain. The T ELETRUCK system – currently under development at the German Research Center for Artificial Intelligence (DFKI) – is a dispatch support system that tackles this problem. Intercompany planning, scheduling, and monitoring of intermodal transport chains will be supported by our system. It aims at providing smooth access to railway time tables and rail-based transport services and – much more important – at allowing for the planning of both, exclusively road-based and combined journeys and showing their cost-effectiveness. We describe our approach – based on intelligent agent technology – both the current state of implementation and our goal of the very next future.
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering

Vision and language understanding has emerged as a subject undergoing intense study in Artificial Intelligence. Among many tasks in this line of research, visual question answering (VQA) has been one of the most successful ones, where the goal is to learn a model that understands visual content at region-level details and finds their associations with pairs of questions and answers in the natural language form. Despite the rapid progress in the past few years, most existing work in VQA have focused primarily on images. In this paper, we focus on extending VQA to the video domain and contribute to the literature in three important ways. First, we propose three new tasks designed specifically for video VQA, which require spatio-temporal reasoning from videos to answer questions correctly. Next, we introduce a new large-scale dataset for video VQA named TGIF-QA that extends existing VQA work with our new tasks. Finally, we propose a dual-LSTM based approach with both spatial and temporal attention, and show its effectiveness over conventional VQA techniques through empirical evaluations.
Focused Depth-first Proof Number Search using Convolutional Neural Networks for the Game of Hex

Proof Number search (PNS) is an effective algorithm for searching theoretical values on games with non-uniform branching factors. Focused depth-first proof number search (FDFPN) with dynamic widening was proposed for Hex where the branching factor is nearly uniform. However, FDFPN is fragile to its heuristic move ordering function. The recent advances of Convolutional Neural Networks (CNNs) have led to considerable progress in game playing. We investigate how to incorporate the strength of CNNs into solving, with application to the game of Hex. We describe FDFPN-CNN, a new focused DFPN search that uses convolutional neural networks. FDFPN-CNN integrates two CNNs trained from games played by expert players. The value approximation CNN provides reliable information for defining the widening size by estimating the value of the node to expand, while the policy CNN selects promising children nodes to the search. On 8x8 Hex, experimental results show FDFPN-CNN performs notably better than FDFPN, suggesting a promising direction for better solving Hex positions where learning from strong players is possible.
Digital marketing and regulatory challenges of Machine-to-Machine (M2M) Communications

Machine-to-machine (M2M) solutions are rolling out worldwide and across all industries - possibly being a key enabler of applications and services covering a broad range of vertical markets (e.g., health-care, utilities, transport, education-research and development, logistics etc.). M2M - as a part of the Internet of Things (IoT) - is, at the present, one of the main drivers behind the growth in mobile subscribers, with all of the world's largest electronic communications operators now having several million M2M subscribers in their mobile networks. The M2M communication model is linked to the interaction between IoT endpoints, which represent - from the point of view of business process and end user applications - service end points that can be easily embedded in emerging service oriented enterprise systems and service delivery platforms. To capitalize on the projected expansion of the M2M market, both regulators and telecommunications operators (as well as service providers) will have to be agile and flexible. The challenge of embedding real world information into networks, services and applications through the convergence of domains like Technologies, Electronic Communications and Intelligence - enabled by context aware technologies, nanoelectronics, sensors and cloud computing - will bring about the development of novel services, innovative products, new interfaces and new applications.
Comparison of Yeasts as Hosts for Recombinant Protein Production

Recombinant protein production emerged in the early 1980s with the development of genetic engineering tools, which represented a compelling alternative to protein extraction from natural sources. Over the years, a high level of heterologous protein was made possible in a variety of hosts ranging from the bacteria Escherichia coli to mammalian cells. Recombinant protein importance is represented by its market size, which reached $1654 million in 2016 and is expected to reach $2850.5 million by 2022. Among the available hosts, yeasts have been used for producing a great variety of proteins applied to chemicals, fuels, food, and pharmaceuticals, being one of the most used hosts for recombinant production nowadays. Historically, Saccharomyces cerevisiae was the dominant yeast host for heterologous protein production. Lately, other yeasts such as Komagataella sp., Kluyveromyces lactis, and Yarrowia lipolytica have emerged as advantageous hosts. In this review, a comparative analysis is done listing the advantages and disadvantages of using each host regarding the availability of genetic tools, strategies for cultivation in bioreactors, and the main techniques utilized for protein purification. Finally, examples of each host will be discussed regarding the total amount of protein recovered and its bioactivity due to correct folding and glycosylation patterns.