nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive-Critic-Based Robust Trajectory Tracking of Uncertain Dynamics and Its Application to a Spring–Mass–Damper System

In this paper, the robust trajectory tracking design of uncertain nonlinear systems is investigated by virtue of a self-learning optimal control formulation. The primary novelty lies in that an effective learning based robust tracking control strategy is developed for nonlinear systems under a general uncertain environment. The augmented system construction is performed by combining the tracking error with the reference trajectory. Then, an improved adaptive critic technique, which does not depend on the initial stabilizing controller, is employed to solve the Hamilton–Jacobi–Bellman (HJB) equation with respect to the nominal augmented system. Using the obtained control law, the closed-loop form of the augmented system is built with stability proof. Moreover, the robust trajectory tracking performance is guaranteed via Lyapunov approach in theory and then through simulation demonstration, where an application to a practical spring–mass–damper system is included.
Building Rapport with Extraverted and Introverted Agents

Psychology research reports that people tend to seek companionship with those who have a similar level of extraversion, and markers in dialogue show the speaker’s extraversion. Work in human-computer interaction seeks to understand creating and maintaining rapport between humans and ECAs. This study examines if humans will report greater rapport when interacting with an agent with an extraversion/introversion profile similar to their own. ECAs representing an extrovert and an introvert were created by manipulating three dialogue features. Using an informal, task-oriented setting, participants interacted with one of the agents in an immersive environment. Results suggest that subjects did not report the greatest rapport when interacting with the agent most similar to their level of extraversion.  People often seek companionship with those who have a personality similar to their own [11]. There is evidence that personality types are borne out in dialogue choices . Humans are uniquely physically capable of speech, and tend to find spoken communication as the most efficient and comfortable way to interact, including with technology . They respond to computer personalities in the same way as they would to human personalities . Recent research has sought to understand the nature of creating and maintaining rapport—a sense of emotional connection—when communicating with embodied conversational agents (ECAs) [2, 8, 14]. Successful ECAs could serve in a number of useful applications, from education to care giving. As human relationships are fundamentally social and emotional, these qualities must be incorporated into ECAs if human-agent relationships are to feel natural to users. Research has been focused on the development and maintenance of rapport felt by humans when interacting with an ECA [11] and in developing ECA personalities . However, questions remain as to which agent personality is the best match for developing rapport in human-ECA interactions. In this study, two agents representing an extravert and an introvert were created by manipulating three dialogue features. Using a task-oriented but informal set-
Ensemble-Based Network Edge Processing

Estimating energy costs for an industrial process can be computationally intensive and time consuming, especially as it can involve data collection from different (distributed) monitoring sensors. Industrial processes have an implicit complexity involving the use of multiple appliances (devices/ sub-systems) attached to operation schedules, electrical capacity and optimisation setpoints which need to be determined for achieving operational cost objectives. Addressing the complexity associated with an industrial workflow (i.e. range and type of tasks) leads to increased requirements on the computing infrastructure. Such requirements can include achieving execution performance targets per processing unit within a particular size of infrastructure i.e. processing & data storage nodes to complete a computational analysis task within a specific deadline. The use of ensemblebased edge processing is identifed to meet these Quality of Service targets, whereby edge nodes can be used to distribute the computational load across a distributed infrastructure. Rather than relying on a single edge node, we propose the combined use of an ensemble of such nodes to overcome processing, data privacy/ security and reliability constraints. We propose an ensemble-based network processing model to facilitate distributed execution of energy simulations tasks within an industrial process. A scenario based on energy profiling within a fisheries plant is used to illustrate the use of an edge ensemble. The suggested approach is however general in scope and can be used in other similar application domains.
CRISPR Screens Uncover Genes that Regulate Target Cell Sensitivity to the Morphogen Sonic Hedgehog

To uncover regulatory mechanisms in Hedgehog (Hh) signaling, we conducted genome-wide screens to identify positive and negative pathway components and validated top hits using multiple signaling and differentiation assays in two different cell types. Most positive regulators identified in our screens, including Rab34, Pdcl, and Tubd1, were involved in ciliary functions, confirming the central role for primary cilia in Hh signaling. Negative regulators identified included Megf8, Mgrn1, and an unannotated gene encoding a tetraspan protein we named Atthog. The function of these negative regulators converged on Smoothened (SMO), an oncoprotein that transduces the Hh signal across the membrane. In the absence of Atthog, SMO was stabilized at the cell surface and concentrated in the ciliary membrane, boosting cell sensitivity to the ligand Sonic Hedgehog (SHH) and consequently altering SHH-guided neural cell-fate decisions. Thus, we uncovered genes that modify the interpretation of morphogen signals by regulating protein-trafficking events in target cells.
Ontology Repositories

The growing use and application of ontologies in the last years has led to an increased interest of researchers and practitioners in the development of ontologies, either from scratch or by reusing existing ones. Reusing existing ontologies instead of creating new ones from scratch has many benefits: It lowers the time and cost of development, avoids duplicate efforts, ensures interoperability, etc. In fact, ontology reuse is one of the key enablers for the realization of the Semantic Web. However, currently, ontologies are mostly developed from scratch, due to several reasons. First, ontologies are usually tailored to work for specific applications, restricting its potential reusability. Second, developers usually follow a monolithic approach when developing ontologies, usually covering different domains, hampering the reusability of relevant parts for other applications. Third, ontologies are rather difficult to find due to the lack of standards for documenting them and appropriate tools supporting intelligent ontology discovery and selection by end users. In this chapter, we define a generic ontology repository framework that enables the implementation of fully-fledged ontology repositories providing the technological support to the aforementioned issues. We distinguish between the ontology repository itself and the software to manage the repository, and describe their main aspects and services. Finally, we present two exemplary systems based on this framework.
Technology for sub-50nm DRAM and NAND flash manufacturing

This paper discusses whether memory technologies can continue advances beyond sub-50nm node especially for DRAM and NAND flash memories. First, the barriers to shrink technology are addressed for DRAM and NAND flash memories, depending on their inherent operation principles. Then, details of technology solutions are introduced and its manufacturability is examined. Beyond 30nm node, It is expected that 3-dimensional transistor scheme is needed for both logic and memory array in addition to the development of new materials and structural technologies
Autoerotic deaths in the literature from 1954 to 2004: a review.

Autoerotic death by hanging or ligature is a method of autoeroticism well known by forensic pathologists. In order to analyze autoerotic deaths of nonclassic hanging or ligature type, this paper reviews all published cases of autoerotic deaths from 1954 to 2004, with the exclusion of homicide cases or cases in which the autoerotic activity was not solitary. These articles were obtained through a systematic Medline database search. A total of 408 cases of such deaths has been reported in 57 articles. For each case, the following characteristics are presented here: sex, age, race, method of autoerotic activity, cause of death, and location where the body was found. Autoerotic death practioners were predominantly Caucasian males. Victims were aged from 9 to 77 years and were mainly found in various indoor locations. Most cases were asphyxia by hanging, ligature, plastic bags, chemical substances, or a mixture of these. Still, atypical methods of autoerotic activity leading to death accounted for about 10.3% of cases in the literature and are classified here into five broad categories: electrocution (3.7%), overdressing/body wrapping (1.5%), foreign body insertion (1.2%), atypical asphyxia method (2.9%), and miscellaneous (1.0%). All these atypical methods are further discussed individually.
Identification of Invariant Sensorimotor Structures as a Prerequisite for the Discovery of Objects

Perceiving the surrounding environment in terms of objects is useful for any general purpose intelligent agent. In this paper, we investigate a fundamental mechanism making object perception possible, namely the identification of spatio-temporally invariant structures in the sensorimotor experience of an agent. We take inspiration from the Sensorimotor Contingencies Theory to define a computational model of this mechanism through a sensorimotor, unsupervised and predictive approach. Our model is based on processing the unsupervised interaction of an artificial agent with its environment. We show how spatio-temporally invariant structures in the environment induce regularities in the sensorimotor experience of an agent, and how this agent, while building a predictive model of its sensorimotor experience, can capture them as densely connected subgraphs in a graph of sensory states connected by motor commands. Our approach is focused on elementary mechanisms, and is illustrated with a set of simple experiments in which an agent interacts with an environment. We show how the agent can build an internal model of moving but spatio-temporally invariant structures by performing a Spectral Clustering of the graph modeling its overall sensorimotor experiences. We systematically examine properties of the model, shedding light more globally on the specificities of the paradigm with respect to methods based on the supervised processing of collections of static images. ∗nicolas.lehir@softbankrorobtics.com †Olivier.Sigaud@upmc.fr ‡alaflaquiere@softbankrobotics.com 1 ar X iv :1 81 0. 05 05 7v 1 [ cs .A I] 1 1 O ct 2 01 8
Model Predictive Control of a Mobile Robot Using Linearization

This paper presents an optimal control scheme for a wheeled mobile robot (WMR) with nonholonomic constraints. It is well known that a WMR with nonholonomic constraints can not be feedback stabilized through continuously differentiable, time-invariant control laws. By using model predictive control (MPC), a discontinuous control law is naturally obtained. One of the main advantages of MPC is the ability to handle constraints (due to state or input limitations) in a straightforward way. Quadratic programming (QP) is used to solve a linear MPC by successive linearization of an error model of the WMR.
Human Activity Learning using Object Affordances from RGB-D Videos

Human activities comprise several sub-activities performed in a sequence and involve interactions with various objects. This makes reasoning about the object affordances a central task for activity recognition. In this work, we consider the problem of jointly labeling the object affordances and human activities from RGB-D videos. We frame the problem as a Markov Random Field where the nodes represent objects and sub-activities, and the edges represent the relationships between object affordances, their relations with sub-activities, and their evolution over time. We formulate the learning problem using a structural SVM approach, where labeling over various alternate temporal segmentations are considered as latent variables. We tested our method on a dataset comprising 120 activity videos collected from four subjects, and obtained an end-to-end precision of 81.8% and recall of 80.0% for labeling the activities.
Mathematical models to characterize early epidemic growth: A review.

There is a long tradition of using mathematical models to generate insights into the transmission dynamics of infectious diseases and assess the potential impact of different intervention strategies. The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing reliable models that capture the baseline transmission characteristics of specific pathogens and social contexts. More refined models are needed however, in particular to account for variation in the early growth dynamics of real epidemics and to gain a better understanding of the mechanisms at play. Here, we review recent progress on modeling and characterizing early epidemic growth patterns from infectious disease outbreak data, and survey the types of mathematical formulations that are most useful for capturing a diversity of early epidemic growth profiles, ranging from sub-exponential to exponential growth dynamics. Specifically, we review mathematical models that incorporate spatial details or realistic population mixing structures, including meta-population models, individual-based network models, and simple SIR-type models that incorporate the effects of reactive behavior changes or inhomogeneous mixing. In this process, we also analyze simulation data stemming from detailed large-scale agent-based models previously designed and calibrated to study how realistic social networks and disease transmission characteristics shape early epidemic growth patterns, general transmission dynamics, and control of international disease emergencies such as the 2009 A/H1N1 influenza pandemic and the 2014-2015 Ebola epidemic in West Africa.
Smodels: A System for Answer Set Programming

The Smodels system implements the stable model semantics for normal logic programs. It handles a subclass of programs which contain no function symbols and are domain-restricted but supports extensions including built-in functions as well as cardinality and weight constraints. On top of this core engine more involved systems can be built. As an example, we have implemented total and partial stable model computation for disjunctive logic programs. An interesting application method is based on answer set programming, i.e., encoding an application problem as a set of rules so that its solutions are captured by the stable models of the rules. Smodels has been applied to a number of areas including planning, model checking, reachability analysis, product configuration, dynamic constraint satisfaction, and feature interaction.
Ubiquitous Cell-Free Massive MIMO Communications

Since the first cellular networks were trialled in the 1970s, we have witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic growth has been managed by a combination of wider bandwidths, refined radio interfaces, and network densification, namely increasing the number of antennas per site . Due its cost-efficiency, the latter has contributed the most. Massive MIMO (multiple-input multiple-output) is a key 5G technology that uses massive antenna arrays to provide a very high beamforming gain and spatially multiplexing of users, and hence, increases the spectral and energy efficiency (see  and references herein). It constitutes a centralized solution to densify a network, and its performance is limited by the inter-cell interference inherent in its cell-centric design. Conversely, ubiquitous cell-free Massive MIMO [3] refers to a distributed Massive MIMO system implementing coherent user-centric transmission to overcome the inter-cell interference limitation in cellular networks and provide additional macro-diversity. These features, combined with the system scalability inherent in the Massive MIMO design, distinguishes ubiquitous cell-free Massive MIMO from prior coordinated distributed wireless systems. In this article, we investigate the enormous potential of this promising technology while addressing practical deployment issues to deal with the increased back/front-hauling overhead deriving from the signal co-processing.
BETWEEN THE HEDGES: A COMPUTATIONAL ANALYSIS OF SENTIMENT AND LINGUISTIC HEDGING IN FINANCIAL DOCUMENTS by CAITLIN CASSIDY

LINGUISTIC HEDGING IN FINANCIAL DOCUMENTS by CAITLIN CASSIDY (Under the Direction of Frederick W. Maier) ABSTRACT Each year, publicly incorporated companies are required to file a Form 10-K with the United States Securities and Exchange Commission. These documents contain an enormous amount of natural language data and may offer insight into financial performance prediction. This thesis attempts to analyze two dimensions of language held within this data: sentiment and linguistic hedging. An experiment was conducted with 325 human annotators to manually score a subset of the sentiment words contained in a corpus of 106 10-K filings, and an inference engine identified instances of hedges having governance over these words in a dependency tree. Finally, this work proposes an algorithm for the automatic classification of sentences in the financial domain as speculative or non-speculative using the previously defined hedge cues.
Persistence and adherence to overactive bladder medications in Japan: A large nationwide real-world analysis.

To evaluate persistence and adherence to mirabegron and antimuscarinics in Japan using data from two administrative databases.



The present retrospective study evaluated insurance claims for employees and dependents aged ≤75 years, and pharmacy claims for outpatients. From October 2012 to September 2014, new users of mirabegron or five individual antimuscarinics indicated for overactive bladder in Japan (fesoterodine, imidafenacin, propiverine, solifenacin and tolterodine) were identified and followed for 1 year. Persistence with mirabegron and antimuscarinics were evaluated using Kaplan-Meier methods. Any associations between baseline characteristics (age, sex and previous medication use) and persistence were explored. Adherence was assessed using the medication possession ratio.



In total, 3970 and 16 648 patients were included from the insurance and pharmacy claims databases, respectively. Mirabegron treatment was associated with longer median persistence compared with antimuscarinics (insurance claims: 44 [95% confidence intervals 37-56] vs 21 [14-28] to 30 [30-33] days, pharmacy claims: 105 [96-113] vs 62 [56-77] to 84 [77-86] days). The results were consistent when patients were stratified by age, sex and previous medication. Persistence rate at 1 year was higher for mirabegron (insurance claims: 14.0% [11.5-16.8%] vs 5.4% [4.1-7.0%] to 9.1% [5.3-14.2%], pharmacy claims: 25.9% [24.6-27.3%] vs 16.3% [14.0-18.6%] to 21.3% [20.2-22.4%]). Compared with each antimuscarinic, a higher proportion of mirabegron-treated patients had medication possession ratios ≥0.8.



This large nationwide Japanese study shows that persistence and adherence are greater with mirabegron compared with five antimuscarinics.
Automated Semantic Knowledge Acquisition From Sensor Data

The gathering of real-world data is facilitated by many pervasive data sources such as sensor devices and smartphones. The abundance of the sensory data raises the need to make the data easily available and understandable for the potential users and applications. Using semantic enhancements is one approach to structure and organize the data and to make it processable and interoperable by machines. In particular, ontologies are used to represent information and their relations in machine interpretable forms. In this context, a significant amount of work has been done to create real-world data description ontologies and data description models; however, little effort has been done in creating and constructing meaningful topical ontologies from a vast amount of sensory data by automated processes. Topical ontologies represent the knowledge from a certain domain providing a basic understanding of the concepts that serve as building blocks for further processing. There is a lack of solution that construct the structure and relations of ontologies based on real-world data. To address this challenge, we introduce a knowledge acquisition method that processes real-world data to automatically create and evolve topical ontologies based on rules that are automatically extracted from external sources. We use an extended k- means clustering method and apply a statistic model to extract and link relevant concepts from the raw sensor data and represent them in the form of a topical ontology. We use a rule-based system to label the concepts and make them understandable for the human user or semantic analysis and reasoning tools and software. The evaluation of our work shows that the construction of a topological ontology from raw sensor data is achievable with only small construction errors.
Yellow papules: the iceberg of systemic elastorrhexis

Systemic elastorrhexis is a multisystem genetic disorder characterised by dystrophic mineralization of soft connective tissues in a number of organs, including the skin, the eyes and the arterial blood vessels. Although the eye and skin findings have for years attracted the attention of ophthalmologists and dermatologists, the systemic nature of the disorder has not received sufficient attention among internists and many patients with this disorder have undoubtedly been unrecognized. We reported a case of systemic elastorrhexis redressing the diagnosis of vascular leucoencephalopathy of an unknown aetiology for many years.
Error-Resilient Machine Learning in Near Threshold Voltage via Classifier Ensemble

In this paper, we present the design of error-resilient machine learning architectures by employing a distributed machine learning framework referred to as classifier ensemble (CE). CE combines several simple classifiers to obtain a strong one. In contrast, centralized machine learning employs a single complex block. We compare the random forest (RF) and the support vector machine (SVM), which are representative techniques from the CE and centralized frameworks, respectively. Employing the dataset from UCI machine learning repository and architecturallevel error models in a commercial 45 nm CMOS process, it is demonstrated that RF-based architectures are significantly more robust than SVM architectures in presence of timing errors due to process variations in near-threshold voltage (NTV) regions (0.3 V 0.7 V). In particular, the RF architecture exhibits a detection accuracy (Pdet) that varies by 3.2% while maintaining a median Pdet ≥ 0.9 at a gate level delay variation of 28.9% . In comparison, SVM exhibits a Pdet that varies by 16.8%. Additionally, we propose an error weighted voting technique that incorporates the timing error statistics of the NTV circuit fabric to further enhance robustness. Simulation results confirm that the error weighted voting achieves a Pdet that varies by only 1.4%, which is 12× lower compared to SVM.