nils-herrmann/clean_scirepeval huggingface dataset:
Radar Scan Matching SLAM Using the Fourier-Mellin Transform

This paper is concerned with the Simultaneous Localization And Mapping (SLAM) problem using data obtained from a microwave radar sensor. The radar scanner is based on Frequency Modulated Continuous Wave (FMCW) technology. In order to meet the needs of radar image analysis complexity, a trajectoryoriented EKF-SLAM technique using data from a 360◦ field of view radar sensor has been developed. This process makes no landmark assumptions and avoids the data association problem. The method of egomotion estimation makes use of the Fourier-Mellin Transform for registering radar images in a sequence, from which the rotation and translation of the sensor motion can be estimated. In the context of the scan-matching SLAM, the use of the Fourier-Mellin Transform is original and provides an accurate and efficient way of computing the rigid transformation between consecutive scans. Experimental results on real-world data are presented.
Predicting the functional states of human iPSC-derived neurons with single-cell RNA-seq and electrophysiology

Human neural progenitors derived from pluripotent stem cells develop into electrophysiologically active neurons at heterogeneous rates, which can confound disease-relevant discoveries in neurology and psychiatry. By combining patch clamping, morphological and transcriptome analysis on single-human neurons in vitro, we defined a continuum of poor to highly functional electrophysiological states of differentiated neurons. The strong correlations between action potentials, synaptic activity, dendritic complexity and gene expression highlight the importance of methods for isolating functionally comparable neurons for in vitro investigations of brain disorders. Although whole-cell electrophysiology is the gold standard for functional evaluation, it often lacks the scalability required for disease modeling studies. Here, we demonstrate a multimodal machine-learning strategy to identify new molecular features that predict the physiological states of single neurons, independently of the time spent in vitro. As further proof of concept, we selected one of the potential neurophysiological biomarkers identified in this study—GDAP1L1—to isolate highly functional live human neurons in vitro.
Cancer Stem Cell Immunology: Key to Understanding Tumorigenesis and Tumor Immune Escape?

Cancer stem cell (CSC) biology and tumor immunology have shaped our understanding of tumorigenesis. However, we still do not fully understand why tumors can be contained but not eliminated by the immune system and whether rare CSCs are required for tumor propagation. Long latency or recurrence periods have been described for most tumors. Conceptually, this requires a subset of malignant cells which is capable of initiating tumors, but is neither eliminated by immune cells nor able to grow straight into overt tumors. These criteria would be fulfilled by CSCs. Stem cells are pluripotent, immune-privileged, and long-living, but depend on specialized niches. Thus, latent tumors may be maintained by a niche-constrained reservoir of long-living CSCs that are exempt from immunosurveillance while niche-independent and more immunogenic daughter cells are constantly eliminated. The small subpopulation of CSCs is often held responsible for tumor initiation, metastasis, and recurrence. Experimentally, this hypothesis was supported by the observation that only this subset can propagate tumors in non-obese diabetic/scid mice, which lack T and B cells. Yet, the concept was challenged when an unexpectedly large proportion of melanoma cells were found to be capable of seeding complex tumors in mice which further lack NK cells. Moreover, the link between stem cell-like properties and tumorigenicity was not sustained in these highly immunodeficient animals. In humans, however, tumor-propagating cells must also escape from immune-mediated destruction. The ability to persist and to initiate neoplastic growth in the presence of immunosurveillance - which would be lost in a maximally immunodeficient animal model - could hence be a decisive criterion for CSCs. Consequently, integrating scientific insight from stem cell biology and tumor immunology to build a new concept of "CSC immunology" may help to reconcile the outlined contradictions and to improve our understanding of tumorigenesis.
Evaluation of peer-to-peer energy sharing mechanisms based on a multiagent simulation framework ☆ , ☆ ☆

Peer-to-peer (P2P) energy sharing involves novel technologies and business models at the demand-side of power systems, which is able to manage the increasing connection of distributed energy resources (DERs). In P2P energy sharing, prosumers directly trade energy with each other to achieve a win-win outcome. From the perspectives of power systems, P2P energy sharing has the potential to facilitate local energy balance and selfsufficiency. A systematic index system was developed to evaluate the performance of various P2P energy sharing mechanisms based on a multiagent-based simulation framework. The simulation framework is composed of three types of agents and three corresponding models. Two techniques, i.e. step length control and learning process involvement, and a last-defence mechanism were proposed to facilitate the convergence of simulation and deal with the divergence. The evaluation indexes include three economic indexes, i.e. value tapping, participation willing and equality, and three technical indexes, i.e. energy balance, power flatness and self-sufficiency. They are normalised and further synthesized to reflect the overall performance. The proposed methods were applied to simulate and evaluate three existing P2P energy sharing mechanisms, i.e. the supply and demand ratio (SDR), mid-market rate (MMR) and bill sharing (BS), for residential customers in current and future scenarios of Great Britain. Simulation results showed that both of the step length control and learning process involvement techniques improve the performance of P2P energy sharing mechanisms with moderate ramping/learning rates. The results also showed that P2P energy sharing has the potential to bring both economic and technical benefits for Great Britain. In terms of the overall performance, the SDR mechanism outperforms all the other mechanisms, and the MMR mechanism has good performance when with moderate PV penetration levels. The BS mechanism performs at the similar level as the conventional paradigm. The conclusion on the mechanism performance is not sensitive to season factors, day types and retail price schemes.
Manipulating measurement scales in medical statistical analysis and data mining: A review of methodologies

selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal-variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD).


ORDINAL-TO-INTERVAL SCALE CONVERSION EXAMPLE
a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests.



the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable.



by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables.
Exploring Contextual Engagement for Trauma Recovery

A wide range of research has used face data to estimate a person's engagement, in applications from advertising to student learning. An interesting and important question not addressed in prior work is if face-based models of engagement are generalizable and context-free, or do engagement models depend on context and task. This research shows that context-sensitive face-based engagement models are more accurate, at least in the space of web-based tools for trauma recovery. Estimating engagement is important as various psychological studies indicate that engagement is a key component to measure the effectiveness of treatment and can be predictive of behavioral outcomes in many applications. In this paper, we analyze user engagement in a trauma-recovery regime during two separate modules/tasks: relaxation and triggers. The dataset comprises of 8M+ frames from multiple videos collected from 110 subjects, with engagement data coming from 800+ subject self-reports. We build an engagement prediction model as sequence learning from facial Action Units (AUs) using Long Short Term Memory (LSTMs). Our experiments demonstrate that engagement prediction is contextual and depends significantly on the allocated task. Models trained to predict engagement on one task are only weak predictors for another and are much less accurate than context-specific models. Further, we show the interplay of subject mood and engagement using a very short version of Profile of Mood States (POMS) to extend our LSTM model.
Three-level neutral-point-clamped inverters in transformerless PV systems — State of the art

Nowadays, the three-level neutral point clamped (3L-NPC) inverter has become more attractive among multilevel inverters topologies, especially in transformerless grid connected photovoltaic systems. Due to the unbalance between the two DC load-side capacitor voltages, the development and improvement of such topology were important topics in recent studies. New structures were derived focusing on better distribution of losses, increasing efficiency, decreasing the switching devices losses and the conduction losses. For this reason, seven derived topologies are discussed in this paper and compared among the conventional inverter. In addition, a part of this study focuses on modifying modulation techniques in order to comply with the neutral point fluctuation problem. Several carrier-based PWM and space vector modulation techniques are considered, discussed and compared.
Neuropsychological assessment in illiterates: Visuospatial and memory abilities

A basic neuropsychological battery of visuospatial and memory abilities was administered to extreme educational groups (illiterates and professionals). Subjects were matched according to sex and age. The following visuospatial tasks were included: figure copy (cube, house, and Rey-Osterrieth complex figure), telling time, recognition of superimposed figures, recognition of a map, and drawing of the plan of the room. The following memory tasks were used: basic information, digit retention (forward and backward), memory curve, delayed verbal recall, sentence repetition, logical memory, delayed logical memory, immediate recall of the Rey-Osterrieth complex figure, immediate reproduction of a cube, visuospatial memory, and sequential memory. In visuospatial tasks all differences between the two groups were statistically significant. Five of the seven visuospatial tasks (all but telling time and recognition of superimposed figures) showed differences between age groups with a better performance found in the younger groups and four of the tasks (cube, house, Rey-Osterrith complex figure copying, and telling time) were significant between sexes with a better performance in men. In memory tasks, with the exception of the immediate memory of sentences, all tasks showed statistically significant differences between educational groups. Eight of the 13 memory tasks (digits forward and backward, delayed memory of words, immediate and delayed logical memory, Rey-Osterrieth immediate memory, cube immediate memory, and sequential memory) showed significant differences for age while 4 of the tasks (digits backward, memory curve, Rey-Osterrieth immediate memory, and cube immediate memory) were significant for sex. Results are analyzed with regard to current theories in cognitive psychology and anthropology. Emphasis is placed on the finding that cognitive skills usually examined by neuropsychological tests represent learned and highly trained abilities.
Healthcare utilization in older patients using personal emergency response systems: an analysis of electronic health records and medical alert data

Personal Emergency Response Systems (PERS) are traditionally used as fall alert systems for older adults, a population that contributes an overwhelming proportion of healthcare costs in the United States. Previous studies focused mainly on qualitative evaluations of PERS without a longitudinal quantitative evaluation of healthcare utilization in users. To address this gap and better understand the needs of older patients on PERS, we analyzed longitudinal healthcare utilization trends in patients using PERS through the home care management service of a large healthcare organization.



Retrospective, longitudinal analyses of healthcare and PERS utilization records of older patients over a 5-years period from 2011-2015. The primary outcome was to characterize the healthcare utilization of PERS patients. This outcome was assessed by 30-, 90-, and 180-day readmission rates, frequency of principal admitting diagnoses, and prevalence of conditions leading to potentially avoidable admissions based on Centers for Medicare and Medicaid Services classification criteria.



The overall 30-day readmission rate was 14.2%, 90-days readmission rate was 34.4%, and 180-days readmission rate was 42.2%. While 30-day readmission rates did not increase significantly (p = 0.16) over the study period, 90-days (p = 0.03) and 180-days (p = 0.04) readmission rates did increase significantly. The top 5 most frequent principal diagnoses for inpatient admissions included congestive heart failure (5.7%), chronic obstructive pulmonary disease (4.6%), dysrhythmias (4.3%), septicemia (4.1%), and pneumonia (4.1%). Additionally, 21% of all admissions were due to conditions leading to potentially avoidable admissions in either institutional or non-institutional settings (16% in institutional settings only).



Chronic medical conditions account for the majority of healthcare utilization in older patients using PERS. Results suggest that PERS data combined with electronic medical records data can provide useful insights that can be used to improve health outcomes in older patients.
The Nrf2/HO-1 Axis in Cancer Cell Growth and Chemoresistance

The transcription factor, nuclear factor erythroid 2 p45-related factor 2 (Nrf2), acts as a sensor of oxidative or electrophilic stresses and plays a pivotal role in redox homeostasis. Oxidative or electrophilic agents cause a conformational change in the Nrf2 inhibitory protein Keap1 inducing the nuclear translocation of the transcription factor which, through its binding to the antioxidant/electrophilic response element (ARE/EpRE), regulates the expression of antioxidant and detoxifying genes such as heme oxygenase 1 (HO-1). Nrf2 and HO-1 are frequently upregulated in different types of tumours and correlate with tumour progression, aggressiveness, resistance to therapy, and poor prognosis. This review focuses on the Nrf2/HO-1 stress response mechanism as a promising target for anticancer treatment which is able to overcome resistance to therapies.
Enabling Safe Autonomous Driving in Real-World City Traffic Using Multiple Criteria Decision Making

This paper addresses the topic of real-time decision making for autonomous city vehicles, i.e., the autonomous vehicles' ability to make appropriate driving decisions in city road traffic situations. The paper explains the overall controls system architecture, the decision making task decomposition, and focuses on how Multiple Criteria Decision Making (MCDM) is used in the process of selecting the most appropriate driving maneuver from the set of feasible ones. Experimental tests show that MCDM is suitable for this new application area.
A mobile health application for falls detection and biofeedback monitoring

A mobile health application solution with biofeedback based on body sensors is very useful to perform a data collection for patients remote monitoring. This system allows comfort, mobility, and efficiency in all the process of data collection providing more confidence and operability. Falls represent a high risk for debilitated elderly people. Falls can be detected by the accelerometer presented in most of the available mobile devices. To reverse this tendency, more accurate data for patients monitoring can be obtained from the body sensors attached to a human body (such as, electro cardiogram, electromyography, blood pressure, electro dermal activity, and temperature). Then, this paper proposes a mobile solution for falls detection and biofeedback monitoring. The proposed system collects sensed data from body that is forwarded to a smartphone or tablet through Bluetooth. Mobile devices are used to display information graphically to users. All the process of data acquisition is performed in real time. The proposed system is evaluated, demonstrated, and validated through a prototype and it is ready for use.
Dependency-based Gated Recursive Neural Network for Chinese Word Segmentation

Recently, many neural network models have been applied to Chinese word segmentation. However, such models focus more on collecting local information while long distance dependencies are not well learned. To integrate local features with long distance dependencies, we propose a dependency-based gated recursive neural network. Local features are first collected by bi-directional long short term memory network, then combined and refined to long distance dependencies via gated recursive neural network. Experimental results show that our model is a competitive model for Chinese word segmentation.
Knowledge Representation with Ontology Tools & Methodology

As a backbone of the Semantic Web, Ontologies provide a shared understanding of a domain of text. Ontologies, with their appearance, usage, and classification address for concrete ontology language which is important for the Semantic Web. They can be used to support a great variety of tasks in different domains such as knowledge representation, natural language processing, information retrieval, information exchange, collaborative systems, databases, knowledge management, database integration, digital libraries, information retrieval, or multi agent systems. Thus a fast and efficient ontology development is a requirement for the success of many knowledge based systems and for the Semantic Web itself. This paper provides discussion on existing ontology tools and methodologies and the state of the art of the field.
Semantics-Based Obfuscation-Resilient Binary Code Similarity Comparison with Applications to Software and Algorithm Plagiarism Detection

Existing code similarity comparison methods, whether source or binary code based, are mostly not resilient to obfuscations. Identifying similar or identical code fragments among programs is very important in some applications. For example, one application is to detect illegal code reuse. In the code theft cases, emerging obfuscation techniques have made automated detection increasingly difficult. Another application is to identify cryptographic algorithms which are widely employed by modern malware to circumvent detection, hide network communications, and protect payloads among other purposes. Due to diverse coding styles and high programming flexibility, different implementation of the same algorithm may appear very distinct, causing automatic detection to be very hard, let alone code obfuscations are sometimes applied. In this paper, we propose a binary-oriented, obfuscation-resilient binary code similarity comparison method based on a new concept, longest common subsequence of semantically equivalent basic blocks , which combines rigorous program semantics with longest common subsequence based fuzzy matching. We model the semantics of a basic block by a set of symbolic formulas representing the input-output relations of the block. This way, the semantic equivalence (and similarity) of two blocks can be checked by a theorem prover. We then model the semantic similarity of two paths using the longest common subsequence with basic blocks as elements. This novel combination has resulted in strong resiliency to code obfuscation. We have developed a prototype. The experimental results show that our method can be applied to software plagiarism and algorithm detection, and is effective and practical to analyze real-world software.
I'll Be Back: On the Multiple Lives of Users of a Mobile Activity Tracking Application

Mobile health applications that track activities, such as exercise, sleep, and diet, are becoming widely used. While these activity tracking applications have the potential to improve our health, user engagement and retention are critical factors for their success. However, long-term user engagement patterns in real-world activity tracking applications are not yet well understood. Here we study user engagement patterns within a mobile physical activity tracking application consisting of 115 million logged activities taken by over a million users over 31 months. Specifically, we show that over 75% of users return and re-engage with the application after prolonged periods of inactivity, no matter the duration of the inactivity. We find a surprising result that the re-engagement usage patterns resemble those of the start of the initial engagement period, rather than being a simple continuation of the end of the initial engagement period. This evidence points to a conceptual model of multiple lives of user engagement, extending the prevalent single life view of user activity. We demonstrate that these multiple lives occur because the users have a variety of different primary intents or goals for using the app. These primary intents are associated with how long each life lasts and how likely the user is to re-engage for a new life. We find evidence for users being more likely to stop using the app once they achieved their primary intent or goal (e.g., weight loss). However, these users might return once their original intent resurfaces (e.g., wanting to lose newly gained weight). We discuss implications of the multiple life paradigm and propose a novel prediction task of predicting the number of lives of a user. Based on insights developed in this work, including a marker of improved primary intent performance, our prediction models achieve 71% ROC AUC. Overall, our research has implications for modeling user re-engagement in health activity tracking applications and has consequences for how notifications, recommendations as well as gamification can be used to increase engagement.