nils-herrmann/clean_scirepeval huggingface dataset:
Autonomous Automobile Trajectory Tracking for Off-Road Driving: Controller Design, Experimental Validation and Racing

This paper presents a nonlinear control law for an automobile to autonomously track a trajectory, provided in real-time, on rapidly varying, off-road terrain. Existing methods can suffer from a lack of global stability, a lack of tracking accuracy, or a dependence on smooth road surfaces, any one of which could lead to the loss of the vehicle in autonomous off-road driving. This work treats automobile trajectory tracking in a new manner, by considering the orientation of the front wheels - not the vehicle's body - with respect to the desired trajectory, enabling collocated control of the system. A steering control law is designed using the kinematic equations of motion, for which global asymptotic stability is proven. This control law is then augmented to handle the dynamics of pneumatic tires and of the servo-actuated steering wheel. To control vehicle speed, the brake and throttle are actuated by a switching proportional integral (PI) controller. The complete control system consumes a negligible fraction of a computer's resources. It was implemented on a Volkswagen Touareg, "Stanley", the Stanford Racing Team's entry in the DARPA Grand Challenge 2005, a 132 mi autonomous off-road race. Experimental results from Stanley demonstrate the ability of the controller to track trajectories between obstacles, over steep and wavy terrain, through deep mud puddles, and along cliff edges, with a typical root mean square (RMS) crosstrack error of under 0.1 m. In the DARPA National Qualification Event 2005, Stanley was the only vehicle out of 40 competitors to not hit an obstacle or miss a gate, and in the DARPA Grand Challenge 2005 Stanley had the fastest course completion time.
Prenatal tobacco smoke exposure increases hospitalizations for bronchiolitis in infants.

Tobacco smoke exposure (TSE) is a worldwide health problem and it is considered a risk factor for pregnant women's and children's health, particularly for respiratory morbidity during the first year of life. Few significant birth cohort studies on the effect of prenatal TSE via passive and active maternal smoking on the development of severe bronchiolitis in early childhood have been carried out worldwide.



From November 2009 to December 2012, newborns born at ≥ 33 weeks of gestational age (wGA) were recruited in a longitudinal multi-center cohort study in Italy to investigate the effects of prenatal and postnatal TSE, among other risk factors, on bronchiolitis hospitalization and/or death during the first year of life.



Two thousand two hundred ten newborns enrolled at birth were followed-up during their first year of life. Of these, 120 (5.4%) were hospitalized for bronchiolitis. No enrolled infants died during the study period. Prenatal passive TSE and maternal active smoking of more than 15 cigarettes/daily are associated to a significant increase of the risk of offspring children hospitalization for bronchiolitis, with an adjHR of 3.5 (CI 1.5-8.1) and of 1.7 (CI 1.1-2.6) respectively.



These results confirm the detrimental effects of passive TSE and active heavy smoke during pregnancy for infants' respiratory health, since the exposure significantly increases the risk of hospitalization for bronchiolitis in the first year of life.
Metabolites and chloroplast retrograde signaling.

Intracellular signaling from chloroplast to nucleus followed by a subsequent response in the chloroplast is called retrograde signaling. It not only coordinates the expression of nuclear and chloroplast genes, which is essential for chloroplast biogenesis, but also maintains chloroplast function at optimal levels in response to fluxes in metabolites and changes in environmental conditions. In recent years several putative retrograde signals have been identified and signaling pathways have been proposed. Here we review retrograde signals derived from tetrapyrroles, carotenoids, nucleotides and isoprene precursors in response to abiotic stresses, including oxidative stress. We discuss the responses that these signals elicit and show that they not only modify chloroplast function but also influence other aspects of plant development and adaptation.
Fish intelligence, sentience and ethics

Fish are one of the most highly utilised vertebrate taxa by humans; they are harvested from wild stocks as part of global fishing industries, grown under intensive aquaculture conditions, are the most common pet and are widely used for scientific research. But fish are seldom afforded the same level of compassion or welfare as warm-blooded vertebrates. Part of the problem is the large gap between people’s perception of fish intelligence and the scientific reality. This is an important issue because public perception guides government policy. The perception of an animal’s intelligence often drives our decision whether or not to include them in our moral circle. From a welfare perspective, most researchers would suggest that if an animal is sentient, then it can most likely suffer and should therefore be offered some form of formal protection. There has been a debate about fish welfare for decades which centres on the question of whether they are sentient or conscious. The implications for affording the same level of protection to fish as other vertebrates are great, not least because of fishing-related industries. Here, I review the current state of knowledge of fish cognition starting with their sensory perception and moving on to cognition. The review reveals that fish perception and cognitive abilities often match or exceed other vertebrates. A review of the evidence for pain perception strongly suggests that fish experience pain in a manner similar to the rest of the vertebrates. Although scientists cannot provide a definitive answer on the level of consciousness for any non-human vertebrate, the extensive evidence of fish behavioural and cognitive sophistication and pain perception suggests that best practice would be to lend fish the same level of protection as any other vertebrate.
Learning Vision-based Cohesive Flight in Drone Swarms

This paper presents a data-driven approach to learning visionbased collective behavior from a simple flocking algorithm. We simulate a swarm of quadrotor drones and formulate the controller as a regression problem in which we generate 3D velocity commands directly from raw camera images. The dataset is created by simultaneously acquiring omnidirectional images and computing the corresponding control command from the flocking algorithm. We show that a convolutional neural network trained on the visual inputs of the drone can learn not only robust collision avoidance but also coherence of the flock in a sample-efficient manner. The neural controller effectively learns to localize other agents in the visual input, which we show by visualizing the regions with the most influence on the motion of an agent. This weakly supervised saliency map can be computed efficiently and may be used as a prior for subsequent detection and relative localization of other agents. We remove the dependence on sharing positions among flock members by taking only local visual information into account for control. Our work can therefore be seen as the first step towards a fully decentralized, visionbased flock without the need for communication or visual markers to aid detection of other agents.
A Feature Study for Masking-Based Reverberant Speech Separation

Monaural speech separation in reverberant conditions is very challenging. In masking-based separation, features extracted from speech mixtures are employed to predict a time-frequency mask. Robust feature extraction is crucial for the performance of supervised speech separation in adverse acoustic environments. Using objective speech intelligibility as the metric, we investigate a wide variety of monaural features in low signalto-noise ratios and moderate to high reverberation. Deep neural networks are employed as the learning machine in our feature investigation. We find considerable performance gain using a contextual window in reverberant speech processing, likely due to temporal structure of reverberation. In addition, we systematically evaluate feature combinations. In unmatched noise and reverberation conditions, the resulting feature set from this study substantially outperforms previously employed sets for speech separation in anechoic conditions.
Learning Disentangled Multimodal Representations for the Fashion Domain

In many visual domains (like fashion, furniture, etc.) the search for products on online platforms requires matching textual queries to image content. For example, the user provides a search query in natural language (e.g.,pink floral top) and the results obtained are of a different modality (e.g., the set of images of pink floral tops). Recent work on multimodal representation learning enables such cross-modal matching by learning a common representation space for text and image. While such representations ensure that the n-dimensional representation of pink floral top is very close to representation of corresponding images, they do not ensure that the first k1 (< n) dimensions correspond to color, the next k2 (< n) correspond to style and so on. In other words, they learn entangled representations where each dimension does not correspond to a specific attribute. We propose two simple variants which can learn disentangled common representations for the fashion domain wherein each dimension would correspond to a specific attribute (color, style, silhoutte, etc.). Our proposed variants can be integrated with any existing multimodal representation learning method. We use a large fashion dataset of over 700K fashion items crawled from multiple fashion e-commerce portals to evaluate the learned representations on four different applications from the fashion domain, namely, cross-modal image retrieval, visual search, image tagging, and query expansion. Our experimental results show that the proposed variants lead to better performance for each of these applications while learning disentangled representations.
Ethernet/IP-industrial protocol

DeviceNet and ControlNet are two well known industrial networks based on the CIP protocol (CIP = Control and Information Protocol). Both networks have been developed by Rockwell Automation, but are now owned and maintained by the two manufacturers organizations ODVA (Open DeviceNet Vendors Association) and ControlNet International. ODVA and ControlNet International have introduced the newest member of this family-Ethernet/IP ("IP" stands for "Industrial Protocol"). The paper describes the techniques and mechanisms that are used to implement a fully consistent set of services and data objects on a TCP/UDP/IP based Ethernet network.
Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios

In real video surveillance scenarios, visual pedestrian attributes, such as gender, backpack, clothes types, are very important for pedestrian retrieval and person reidentification. Existing methods for attributes recognition have two drawbacks: (a) handcrafted features (e.g. color histograms, local binary patterns) cannot cope well with the difficulty of real video surveillance scenarios; (b) the relationship among pedestrian attributes is ignored. To address the two drawbacks, we propose two deep learning based models to recognize pedestrian attributes. On the one hand, each attribute is treated as an independent component and the deep learning based single attribute recognition model (DeepSAR) is proposed to recognize each attribute one by one. On the other hand, to exploit the relationship among attributes, the deep learning framework which recognizes multiple attributes jointly (DeepMAR) is proposed. In the DeepMAR, one attribute can contribute to the representation of other attributes. For example, the gender of woman can contribute to the representation oflong hair and wearing skirt. Experiments on recent popular pedestrian attribute datasets illustrate that our proposed models achieve the state-of-the-art results.
Effects of Rotor Bar Shape for Squirrel Cage Induction Motor in Transient State Using Moving Band Technique

This paper presents a dynamics analysis algorithm and the effect of rotor bar shape in the transient characteristics of the squirrel cage induction motor, in which the aluminum, copper, and silicon-copper are used for the conductor bars. The electromagnetic field, stator, and rotor circuit equations are coupled to analyze the motor performance in time domain using finite element method. The generated torque is calculated using the relationship of input and output power. Also, motion equation is used to find the next position of the rotor. Moving band technique is used to move the rotor of the motor in irregular intervals. In order to know the starting characteristics, various rotor slot shapes are analyzed and the results are compared with experiments to evaluate its torque performance by using the proposed method.
DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation

We propose DeepMiner, a framework to discover interpretable representations in deep neural networks and to build explanations for medical predictions. By probing convolutional neural networks (CNNs) trained to classify cancer in mammograms, we show that many individual units in the final convolutional layer of a CNN respond strongly to diseased tissue concepts specified by the BI-RADS lexicon. After expert annotation of the interpretable units, our proposed method is able to generate explanations for CNN mammogram classification that are correlated with ground truth radiology reports on the DDSM dataset. We show that DeepMiner not only enables better understanding of the nuances of CNN classification decisions, but also possibly discovers new visual knowledge relevant to medical diagnosis.
Predicting suicide attempts with the SAD PERSONS scale: a longitudinal analysis.

The SAD PERSONS scale is a widely used risk assessment tool for suicidal behavior despite a paucity of supporting data. The objective of this study was to examine the ability of the scale in predicting suicide attempts.



Participants consisted of consecutive referrals (N=4,019) over 2 years (January 1, 2009 to December 31, 2010) to psychiatric services in the emergency departments of the 2 largest tertiary care hospitals in the province of Manitoba, Canada. SAD PERSONS and Modified SAD PERSONS (MSPS) scale scores were recorded for individuals at their index and all subsequent presentations. The 2 main outcome measures in the study included current suicide attempts (at index presentation) and future suicide attempts (within the next 6 months). The ability of the scales to predict suicide attempts was evaluated with logistic regression, sensitivity and specificity analyses, and receiver operating characteristic curves.



566 people presented with suicide attempts (14.1% of the sample). Both SAD PERSONS and MSPS showed poor predictive ability for future suicide attempts. Compared to low risk scores, high risk baseline scores had low sensitivity (19.6% and 40.0%, respectively) and low positive predictive value (5.3% and 7.4%, respectively). SAD PERSONS did not predict suicide attempts better than chance (area under the curve =0.572; 95% confidence interval [CI], 0.51-0.64; P value nonsignificant). Stepwise regression identified 5 original scale items that accounted for the greatest proportion of future suicide attempt variance. High risk scores using this model had high sensitivity (93.5%) and were associated with a 5-fold higher likelihood of future suicide attempt presentation (odds ratio =5.58; 95% CI, 2.24-13.86; P<.001).



In their current form, SAD PERSONS and MSPS do not accurately predict future suicide attempts.
Modeling and design solutions to overcome warpage challenge for fan-out wafer level packaging (FO-WLP) technology

Fan-out wafer-level-packaging (FO-WLP) technology gets more and more significant attention with its advantages of small form factor, higher I/O density, cost effective and high performance for wide range application. However, wafer warpage is still one critical issue which is needed to be addressed for successful subsequent processes for FO-WLP packaging. In this study, methodology to reduce wafer warpage of 12" wafer at different processes was proposed in terms of geometry design, material selection, and process optimization through finite element analysis (FEA) and experiment. Wafer process dependent modeling results were validated by experimental measurement data. Solutions for reducing wafer warpage were recommended. Key parameters were identified based on FEA modeling results: thickness ratio of die to total mold thickness, molding compound and support wafer materials, dielectric material and RDL design.
Linearization of the Bradford protein assay increases its sensitivity: theoretical and experimental studies.

Determination of microgram quantities of protein in the Bradford Coomassie brilliant blue assay is accomplished by measurement of absorbance at 590 nm. However, as intrinsic nonlinearity compromises the sensitivity and accuracy of this method. It is shown that under standard assay conditions, the ratio of the absorbances, 590 nm over 450 nm, is strictly linear with protein concentration. This simple procedure increases the accuracy and improves the sensitivity of the assay about 10-fold, permitting quantitation down to 50 ng of bovine serum albumin. Furthermore, protein assay in presence of up to 35-fold weight excess of sodium dodecyl sulfate (detergent) over bovine serum albumin (protein) can be performed. A linear equation that perfectly fits the experimental data is provided on the basis of mass action and Beer's law.
Trajectory Factory: Tracklet Cleaving and Re-Connection by Deep Siamese Bi-GRU for Multiple Object Tracking

Multi-Object Tracking (MOT) is a challenging task in the complex scene such as surveillance and autonomous driving. In this paper, we propose a novel tracklet processing method to cleave and re-connect tracklets on crowd or longterm occlusion by Siamese Bi-Gated Recurrent Unit (GRU). The tracklet generation utilizes object features extracted by CNN and RNN to create the high-confidence tracklet candidates in sparse scenario. Due to mis-tracking in the generation process, the tracklets from different objects are split into several sub-tracklets by a bidirectional GRU. After that, a Siamese GRU based tracklet re-connection method is applied to link the sub-tracklets which belong to the same object to form a whole trajectory. In addition, we extract the track-let images from existing MOT datasets and propose a novel dataset to train our networks. The proposed dataset contains more than 95160 pedestrian images. It has 793 different persons in it. On average, there are 120 images for each person with positions and sizes. Experimental results demonstrate the advantages of our model over the state-of-the-art methods on MOTI6.
Age moderates response to acceptance and commitment therapy vs. cognitive behavioral therapy for chronic pain.

The purpose of this study was to examine age differences in response to different forms of psychotherapy for chronic pain.



We performed a secondary analysis of 114 adults (ages 18-89 years) with a variety of chronic, nonmalignant pain conditions randomly assigned to 8 weeks of group-administered acceptance and commitment therapy (ACT) or cognitive behavioral therapy (CBT). Treatment response was defined as a drop of at least three points on the Brief Pain Inventory-interference subscale.



Older adults were more likely to respond to ACT, and younger adults to CBT, both immediately following treatment and at 6-month follow-up. There were no significant differences in credibility, expectations of positive outcome, attrition, or satisfaction, although there was a trend for the youngest adults (ages 18-45 years) to complete fewer sessions.



These data suggest that ACT may be an effective and acceptable treatment for chronic pain in older adults.
Multi-objective design of a close-coupled inductor for a three-phase interleaved 140kW DC-DC converter

A multi-objective design procedure is applied to the design of a close-coupled inductor for a three-phase interleaved 140kW DC-DC converter. For the multi-objective optimization, a genetic algorithm is used in combination with a detailed physical model of the inductive component. From the solution of the optimization, important conclusions about the advantages and disadvantages of using close-coupled inductors compared to separate inductors can be drawn.
Micromouse: An autonomous robot vehicle interdisciplinary attraction to education and research

The objective of this paper is to instill in students motivation and interest for what they are studying a little bit further of the theory they learn in classroom. Sometimes students prefer more interactive classes and want to know why the material given in class is helpful in their career. Do we have a solution for this? Autonomous Robotic Vehicle (ARV) projects can be the solution to this problem. This type of project are interdisciplinary and offer students varieties of challenges, while involving different areas of interest like programming, design, assembling and testing. One of the best ARV project is the Micromouse. The Micromouse is a small robot that solves mazes. The basic idea is that the student makes his own Micromouse from scratch using knowledge acquired from different classes and the research done. This project also helps the student to develop teamwork skills and creativity to complete the different challenges and objectives that appear when building a Micromouse. The student learns the importance of working with students from other engineering concentrations, which allows him to experience how a career in engineer will be.
An Ontology Based Natural Language Story Generation Approach: WELOTA

WELOTA is an automatic story generator which generates and narrates “Well Loved Tales” for kids between 5-7 years old. WELOTA implements a generic and a powerful approach to extract information from digital text files, represent that using a knowledge representation mechanism and later retrieve the knowledge to construct meaningful natural language sentences. Kid’s story domain is chosen to implement and prove this approach. Knowledge Extraction is handled through GATE NLP tool and WordNet 2.1. Extracted knowledge is then represented automatically in a domain specific ontology built in protégé. Later the knowledge base is inferred on a selected story title using SPARQL query language. Then the retrieved knowledge is converted into simple English sentences through SimpleNLG API and a set of linguistic rules. WELOTA also embeds narrative terms and phrases to the story content which are constructed using its tacit knowledge.
Audio description as an aural guide of children's visual attention: evidence from an eye-tracking study

Audio description (AD) has become a cultural revolution for the visually impaired; however, the range of AD beneficiaries can be much broader. We claim that AD is useful for guiding children's attention. The paper presents an eye-tracking study testing the usefulness of AD in selective attention to described elements of a video scene. Forty-four children watched 2 clips from an educational animation series while their eye movements were recorded. Average fixation duration, fixation count, and saccade amplitude served as primary dependent variables. The results confirmed that AD guides children's attention towards described objects resulting e. g., in more fixations on specific regions of interest. We also evaluated eye movement patterns in terms of switching between focal and ambient processing. We postulate that audio description could complement regular teaching tools for guiding and focusing children's attention, especially when new concepts are introduced.