nils-herrmann/clean_scirepeval huggingface dataset:
Nonlinear and cooperative control of multiple hovercraft with input constraints

In this paper, we introduce an approach for distributed nonlinear control of multiple hovercraft-type underactuated vehicles with bounded and unidirectional inputs. First, a bounded nonlinear controller is given for stabilization and tracking of a single vehicle, using a cascade backstepping method. Then, this controller is combined with a distributed gradient-based control for multi-vehicle formation stabilization using formation potential functions previously constructed. The vehicles are used in the Caltech Multi-Vehicle Wireless Testbed (MVWT). We provide simulation and experimental results for stabilization and tracking of a single vehicle, and a simulation of stabilization of a six-vehicle formation, demonstrating that in all cases the control bounds and the control objective are satisfied.
The neural basis of empathy.

Empathy--the ability to share the feelings of others--is fundamental to our emotional and social lives. Previous human imaging studies focusing on empathy for others' pain have consistently shown activations in regions also involved in the direct pain experience, particularly anterior insula and anterior and midcingulate cortex. These findings suggest that empathy is, in part, based on shared representations for firsthand and vicarious experiences of affective states. Empathic responses are not static but can be modulated by person characteristics, such as degree of alexithymia. It has also been shown that contextual appraisal, including perceived fairness or group membership of others, may modulate empathic neuronal activations. Empathy often involves coactivations in further networks associated with social cognition, depending on the specific situation and information available in the environment. Empathy-related insular and cingulate activity may reflect domain-general computations representing and predicting feeling states in self and others, likely guiding adaptive homeostatic responses and goal-directed behavior in dynamic social contexts.
Attention-based Adaptive Model to Unify Warm and Cold Starts Recommendation

Nowadays, recommender systems provide essential web services on the Internet. There are mainly two categories of traditional recommendation algorithms: Content-Based (CB) and Collaborative Filtering (CF). CF methods make recommendations mainly according to the historical feedback information. They usually perform better when there is sufficient feedback information but less successful on new users and items, which is called the "cold-start'' problem. However, CB methods help in this scenario because of using content information. To take both advantages of CF and CB, how to combine them is a challenging issue. To the best of our knowledge, little previous work has been done to solve the problem in one unified recommendation model. In this work, we study how to integrate CF and CB, which utilizes both types of information in model-level but not in result-level and makes recommendations adaptively. A novel attention-based model named Attentional Content&Collaborate Model (ACCM) is proposed. Attention mechanism helps adaptively adjust for each user-item pair from which source information the recommendation is made. Especially, a "cold sampling'' learning strategy is designed to handle the cold-start problem. Experimental results on two benchmark datasets show that the ACCM performs better on both warm and cold tests compared to the state-of-the-art algorithms.
Embedding-based Query Expansion for Weighted Sequential Dependence Retrieval Model

Although information retrieval models based on Markov Random Fields (MRF), such as Sequential Dependence Model and Weighted Sequential Dependence Model (WSDM), have been shown to outperform bag-of-words probabilistic and language modeling retrieval models by taking into account term dependencies, it is not known how to effectively account for term dependencies in query expansion methods based on pseudo-relevance feedback (PRF) for retrieval models of this type. In this paper, we propose Semantic Weighted Dependence Model (SWDM), a PRF based query expansion method for WSDM, which utilizes distributed low-dimensional word representations (i.e., word embeddings). Our method finds the closest unigrams to each query term in the embedding space and top retrieved documents and directly incorporates them into the retrieval function of WSDM. Experiments on TREC datasets indicate statistically significant improvement of SWDM over state-of-the-art MRF retrieval models, PRF methods for MRF retrieval models and embedding based query expansion methods for bag-of-words retrieval models.
Harmonic Distortion Analysis for Switched-Capacitor Electromechanical Sigma–Delta Modulators

As the conversions between the sensing element and interface in the feedback loop and forward path are nonlinear, harmonic distortions appear in the output spectrum, which will decrease the signal-to-noise and distortion ratio. Nonlinear distortions are critical for a high-resolution electromechanical sigma-delta (ΣΔ) modulator. However, there exists no detailed analysis approach to derive harmonic distortion results in the output signal for electromechanical ΣΔ modulators. In this paper, we employ a nonlinear op-amp dc gain model to derive the nonlinear displacement to voltage conversion in the forward path, and the nonlinear electrostatic feedback force on the proof mass is also computed. Based on a linear approximation of the modulator in the back end, the harmonic distortion model in the output spectrum of the proposed fifth-order electromechanical ΣΔ modulator is derived as a function of system parameters. The proposed nonlinear distortion models are verified by simulation results and experimental results.
Relationships between personality traits and attitudes toward the sense of smell

Olfactory perception appears to be linked to personality traits. This study aimed to determine whether personality traits influence human attitudes toward sense of smell. Two-hundred participants' attitudes toward their senses of smell and their personality traits were measured using two self-administered questionnaires: the Importance of Olfaction Questionnaire and the Eysenck Personality Questionnaire-Revised. Demographics and olfactory function were also assessed using a self-administered questionnaire. Gender-induced differences were present in attitudes toward sense of smell. Women participants were more dependent than men participants on olfactory cues for daily decision-making. In addition, as participants evaluated their own olfactory functions more positively, they relied more on olfactory information in everyday life. To determine a relationship between personality traits and attitudes toward sense of smell, Spearman partial correlation analyses were conducted, with controlling the factors that might influence attitudes with respect to sense of smell (i.e., gender and self-awareness of olfactory function) as covariates. Participants who scored high on the lie-scale (i.e., socially desirable and faking good), tended to use olfactory cues for daily decision-making related both to social communication and product purchase. In conclusion, our findings demonstrate a significant association between personality traits and attitudes toward sense of smell.
Deep Complex Networks

At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and representations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capacity and could also facilitate noise-robust memory retrieval mechanisms. Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models. In this work, we provide the key atomic components for complex-valued deep neural networks and apply them to convolutional feed-forward networks and convolutional LSTMs. More precisely, we rely on complex convolutions and present algorithms for complex batch-normalization, complex weight initialization strategies for complex-valued neural nets and we use them in experiments with end-to-end training schemes. We demonstrate that such complex-valued models are competitive with their realvalued counterparts. We test deep complex models on several computer vision tasks, on music transcription using the MusicNet dataset and on Speech Spectrum Prediction using the TIMIT dataset. We achieve state-of-the-art performance on these audio-related tasks.
Metformin against Cancer Stem Cells through the Modulation of Energy Metabolism: Special Considerations on Ovarian Cancer

Ovarian cancer is the most lethal gynecologic malignancy among women worldwide and is presumed to result from the presence of ovarian cancer stem cells. To overcome the limitation of current anticancer agents, another anticancer strategy is necessary to effectively target cancer stem cells in ovarian cancer. In many types of malignancies, including ovarian cancer, metformin, one of the most popular antidiabetic drugs, has been demonstrated to exhibit chemopreventive and anticancer efficacy with respect to incidence and overall survival rates. Thus, the metabolic reprogramming of cancer and cancer stem cells driven by genetic alterations during carcinogenesis and cancer progression could be therapeutically targeted. In this review, the potential efficacy and anticancer mechanisms of metformin against ovarian cancer stem cells will be discussed.
Viewpoint: a comparison of cause-of-injury coding in U.S. military and civilian hospitals.

Complete and accurate coding of injury causes is essential to the understanding of injury etiology and to the development and evaluation of injury-prevention strategies. While civilian hospitals use ICD-9-CM external cause-of-injury codes, military hospitals use codes derived from the NATO Standardization Agreement (STANAG) 2050.



The STANAG uses two separate variables to code injury cause. The Trauma code uses a single digit with 10 possible values to identify the general class of injury as battle injury, intentionally inflicted nonbattle injury, or unintentional injury. The Injury code is used to identify cause or activity at the time of the injury. For a subset of the Injury codes, the last digit is modified to indicate place of occurrence. This simple system contains fewer than 300 basic codes, including many that are specific to battle- and sports-related injuries not coded well by either the ICD-9-CM or the draft ICD-10-CM. However, while falls, poisonings, and injuries due to machinery and tools are common causes of injury hospitalizations in the military, few STANAG codes correspond to these events. Intentional injuries in general and sexual assaults in particular are also not well represented in the STANAG. Because the STANAG does not map directly to the ICD-9-CM system, quantitative comparisons between military and civilian data are difficult.



The ICD-10-CM, which will be implemented in the United States sometime after 2001, expands considerably on its predecessor, ICD-9-CM, and provides more specificity and detail than the STANAG. With slight modification, it might become a suitable replacement for the STANAG.
Generation of cartoon-style bas-reliefs from photographs

This paper describes a new algorithm that generates a cartoon-style bas-relief surface from photographs of general scenes. Most previous methods for bas-relief generation have focused on accurate restoration of input 3D models on a background plane. The generation of bas-reliefs with artistic effects has rarely been studied. Considering that non-photorealistic rendering (NPR) techniques are currently very popular and 3D printing technology is developing rapidly, extending NPR techniques to the generation of a bas-relief surface with artistic effects is natural and valuable. Furthermore, cartoon is a basic non-realistic and artistic style familiar to general users. From this motivation, our method focuses on generating a cartoon-style bas-relief surface. We use the lens blur function of Google Camera, which is a smartphone application, to obtain a photograph and its depth map as inputs. Using coherent line drawing and histogram-based quantization methods, we construct a depth map that contains the salient features of given input scenes in abstract form. Displacement mapping from the depth map onto a thin plane generates a cartoon-style bas-relief. Experimental results show that our method generates bas-relief surfaces that contain the characteristics of cartoons, such as coherent border lines and quantized layers.
MineSweeper: An In-depth Look into Drive-by Cryptocurrency Mining and Its Defense

A wave of alternative coins that can be effectively mined without specialized hardware, and a surge in cryptocurrencies' market value has led to the development of cryptocurrency mining ( cryptomining ) services, such as Coinhive, which can be easily integrated into websites to monetize the computational power of their visitors. While legitimate website operators are exploring these services as an alternative to advertisements, they have also drawn the attention of cybercriminals: drive-by mining (also known as cryptojacking ) is a new web-based attack, in which an infected website secretly executes JavaScript code and/or a WebAssembly module in the user's browser to mine cryptocurrencies without her consent. In this paper, we perform a comprehensive analysis on Alexa's Top 1 Million websites to shed light on the prevalence and profitability of this attack. We study the websites affected by drive-by mining to understand the techniques being used to evade detection, and the latest web technologies being exploited to efficiently mine cryptocurrency. As a result of our study, which covers 28 Coinhive-like services that are widely being used by drive-by mining websites, we identified 20 active cryptomining campaigns. Motivated by our findings, we investigate possible countermeasures against this type of attack. We discuss how current blacklisting approaches and heuristics based on CPU usage are insufficient, and present MineSweeper, a novel detection technique that is based on the intrinsic characteristics of cryptomining code, and, thus, is resilient to obfuscation. Our approach could be integrated into browsers to warn users about silent cryptomining when visiting websites that do not ask for their consent.
Cognitive enhancing effects of modafinil in healthy volunteers

Modafinil, a novel wake-promoting agent, has been shown to have a similar clinical profile to that of conventional stimulants such as methylphenidate. We were therefore interested in assessing whether modafinil, with its unique pharmacological mode of action, might offer similar potential as a cognitive enhancer, without the side effects commonly experienced with amphetamine-like drugs.  The main aim of this study was to evaluate the cognitive enhancing potential of this novel agent using a comprehensive battery of neuropsychological tests.  Sixty healthy young adult male volunteers received either a single oral dose of placebo, or 100 mg or 200 mg modafinil prior to performing a variety of tasks designed to test memory and attention. A randomised double-blind, between-subjects design was used.  Modafinil significantly enhanced performance on tests of digit span, visual pattern recognition memory, spatial planning and stop-signal reaction time. These performance improvements were complemented by a slowing in latency on three tests: delayed matching to sample, a decision-making task and the spatial planning task. Subjects reported feeling more alert, attentive and energetic on drug. The effects were not clearly dose dependent, except for those seen with the stop-signal paradigm. In contrast to previous findings with methylphenidate, there were no significant effects of drug on spatial memory span, spatial working memory, rapid visual information processing or attentional set-shifting. Additionally, no effects on paired associates learning were identified.  These data indicate that modafinil selectively improves neuropsychological task performance. This improvement may be attributable to an enhanced ability to inhibit pre-potent responses. This effect appears to reduce impulsive responding, suggesting that modafinil may be of benefit in the treatment of attention deficit hyperactivity disorder.
Max-Margin Multiple-Instance Dictionary Learning

Dictionary learning has became an increasingly important task in machine learning, as it is fundamental to the representation problem. A number of emerging techniques specifically include a codebook learning step, in which a critical knowledge abstraction process is carried out. Existing approaches in dictionary (codebook) learning are either generative (unsupervised e.g. k-means) or discriminative (supervised e.g. extremely randomized forests). In this paper, we propose a multiple instance learning (MIL) strategy (along the line of weakly supervised learning) for dictionary learning. Each code is represented by a classifier, such as a linear SVM, which naturally performs metric fusion for multi-channel features. We design a formulation to simultaneously learn mixtures of codes by maximizing classification margins in MIL. State-of-the-art results are observed in image classification benchmarks based on the learned codebooks, which observe both compactness and effectiveness.
Body Space in Social Interactions: A Comparison of Reaching and Comfort Distance in Immersive Virtual Reality

Do peripersonal space for acting on objects and interpersonal space for interacting with con-specifics share common mechanisms and reflect the social valence of stimuli? To answer this question, we investigated whether these spaces refer to a similar or different physical distance.



Participants provided reachability-distance (for potential action) and comfort-distance (for social processing) judgments towards human and non-human virtual stimuli while standing still (passive) or walking toward stimuli (active).



Comfort-distance was larger than other conditions when participants were passive, but reachability and comfort distances were similar when participants were active. Both spaces were modulated by the social valence of stimuli (reduction with virtual females vs males, expansion with cylinder vs robot) and the gender of participants.



These findings reveal that peripersonal reaching and interpersonal comfort spaces share a common motor nature and are sensitive, at different degrees, to social modulation. Therefore, social processing seems embodied and grounded in the body acting in space.
The second green revolution? Production of plant-based biodegradable plastics.

Biodegradable plastics are those that can be completely degraded in landfills, composters or sewage treatment plants by the action of naturally occurring micro-organisms. Truly biodegradable plastics leave no toxic, visible or distinguishable residues following degradation. Their biodegradability contrasts sharply with most petroleum-based plastics, which are essentially indestructible in a biological context. Because of the ubiquitous use of petroleum-based plastics, their persistence in the environment and their fossil-fuel derivation, alternatives to these traditional plastics are being explored. Issues surrounding waste management of traditional and biodegradable polymers are discussed in the context of reducing environmental pressures and carbon footprints. The main thrust of the present review addresses the development of plant-based biodegradable polymers. Plants naturally produce numerous polymers, including rubber, starch, cellulose and storage proteins, all of which have been exploited for biodegradable plastic production. Bacterial bioreactors fed with renewable resources from plants--so-called 'white biotechnology'--have also been successful in producing biodegradable polymers. In addition to these methods of exploiting plant materials for biodegradable polymer production, the present review also addresses the advances in synthesizing novel polymers within transgenic plants, especially those in the polyhydroxyalkanoate class. Although there is a stigma associated with transgenic plants, especially food crops, plant-based biodegradable polymers, produced as value-added co-products, or, from marginal land (non-food), crops such as switchgrass (Panicum virgatum L.), have the potential to become viable alternatives to petroleum-based plastics and an environmentally benign and carbon-neutral source of polymers.
A half bridge LC resonant converter with reduced current ripple of the output capacitor

This paper proposes a half bridge LC resonant converter with reduced current ripple of the output capacitor. A current stress of the output capacitor can be reduced while maintains the benefits of half bridge LLC resonant converter. The operation principle is explained and voltage gain curve is analyzed through fundamental mode approximation (FMA) method. The 240W, 24V/10A hardware prototype is tested to verify the performance and reduced current stress of the proposed converter compared to LLC resonant converter.
Making the Future Safe for the Past: Adding Genericity to the Java Programming Language

We present GJ, a design that extends the Java programming language with generic types and methods. These are both explained and implemented by translation into the unextended language. The translation closely mimics the way generics are emulated by programmers: it erases all type parameters, maps type variables to their bounds, and inserts casts where needed. Some subtleties of the translation are caused by the handling of overriding.GJ increases expressiveness and safety: code utilizing generic libraries is no longer buried under a plethora of casts, and the corresponding casts inserted by the translation are guaranteed to not fail.GJ is designed to be fully backwards compatible with the current Java language, which simplifies the transition from non-generic to generic programming. In particular, one can retrofit existing library classes with generic interfaces without changing their code.An implementation of GJ has been written in GJ, and is freely available on the web.
Performance Analysis of Load Balancing Architectures in Cloud Computing

The Cloud computing is a rapidly emerging distributed system paradigm that offers a huge amount of IT resources as utility services at a reduced cost and flexible schemes. The key of such flexibility is an efficient load balancer that offers better management and utilization of virtualized underlying cloud infrastructures. However, most of the existing load balancers in cloud computing are based on either centralized or fully distributed architectures while the idea of harnessing multiple load balancers in a hierarchical structure to improve the sever load and job response time is still under studied. Therefore, this paper, aims at bridging this gap by providing a comparative study between the three load balancing architectures in cloud computing: centralized, decentralized and hierarchical load balancers. The experimental results suggest that the hierarchical architecture for load balancers best suits the public cloud environment and call for further research to test whether these results can be generalized for other types of clouds.