nils-herrmann/clean_scirepeval huggingface dataset:
Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances

The success of the supervised classification of remotely sensed images acquired over large geographical areas or at short time intervals strongly depends on the representativity of the samples used to train the classification algorithm and to define the model. When training samples are collected from an image or a spatial region that is different from the one used for mapping, spectral shifts between the two distributions are likely to make the model fail. Such shifts are generally due to differences in acquisition and atmospheric conditions or to changes in the nature of the object observed. To design classification methods that are robust to data set shifts, recent remote sensing literature has considered solutions based on domain adaptation (DA) approaches. Inspired by machine-learning literature, several DA methods have been proposed to solve specific problems in remote sensing data classification. This article provides a critical review of the recent advances in DA approaches for remote sensing and presents an overview of DA methods divided into four categories: 1) invariant feature selection, 2) representation matching, 3) adaptation of classifiers, and 4) selective sampling. We provide an overview of recent methodologies, examples of applications of the considered techniques to real remote sensing images characterized by very high spatial and spectral resolution as well as possible guidelines for the selection of the method to use in real application scenarios.
Physiotherapy Exercises Recognition Based on RGB-D Human Skeleton Models

In the Western world, aging is a growing problem of the society and computer assisted treatments can facilitate the telemedicine for old people or it can help in rehabilitations of patients after sport accidents in far locations. Physical exercises play an important role in physiotherapy and RGB-D devices can be utilized to recognize them in order to make interactive computer healthcare applications in the future. A practical model definition is introduced in this paper to recognize different exercises with Asus Xtion camera. One of the contributions is the extendable recognition models to detect other human activities with noisy sensors, but avoiding heavy data collection. The experiments show satisfactory detection performance without any false positives which is unique in the field to the best of the author knowledge. The computational costs are negligible thus the developed models can be suitable for embedded systems.
Sling bag and backpack detection for human appearance semantic in vision system

In many intelligent surveillance systems there is a requirement to search for people of interest through archived semantic labels. Other than searching through typical appearance attributes such as clothing color and body height, information such as whether a person carries a bag or not is valuable to provide more relevant targeted search. We propose two novel and fast algorithms for sling bag and backpack detection based on the geometrical properties of bags. The advantage of the proposed algorithms is that it does not require shape information from human silhouettes therefore it can work under crowded condition. In addition, the absence of background subtraction makes the algorithms suitable for mobile platforms such as robots. The system was tested with a low resolution surveillance video dataset. Experimental results demonstrate that our method is promising.
Depression: the nutrition connection

There is little doubt that the incidence of depression in Britain is increasing. According to research at the Universities of London and Warwick, the incidence of depression among young people has doubled in the past 12 years. However, whether young or old, the question is why and what can be done? There are those who argue that the increasingly common phenomenon of depression is primarily psychological, and best dealt with by counselling. There are others who consider depression as a biochemical phenomenon, best dealt with by antidepressant medication. However, there is a third aspect to the onset and treatment of depression that is given little heed: nutrition. Why would nutrition have anything to do with depression? Firstly, we have seen a significant decline in fruit and vegetable intake (rich in folic acid), in fish intake (rich in essential fats) and an increase in sugar consumption, from 2 lb a year in the 1940s to 150 lb a year in many of today’s teenagers. Each of these nutrients is strongly linked to depression and could, theoretically, contribute to increasing rates of depression. Secondly, if depression is a biochemical imbalance it makes sense to explore how the brain normalises its own biochemistry, using nutrients as the precursors for key neurotransmitters such as serotonin. Thirdly, if 21st century living is extra-stressful, it would be logical to assume that increasing psychological demands would also increase nutritional requirements since the brain is structurally and functionally completely dependent on nutrients. So, what evidence is there to support suboptimal nutrition as a potential contributor to depression? These are the common imbalances connected to nutrition that are known to worsen your mood and motivation:
VNF Placement and Chaining in Distributed Cloud

This paper presents a model for Virtual Network Function (VNF) placement and chaining across Cloud environments. We propose a new analytical approach for joint VNFs placement and traffic steering for complex service chains and different VNF types. A custom greedy algorithm is also proposed to compare with our solution. Performance evaluation results show that our approach is fast and stable and has a execution time that essentially depends only on the NFV infrastructure size.
A double-blind, randomized study of olanzapine and olanzapine/fluoxetine combination for major depression with psychotic features.

The purpose of this study was to compare the efficacy and safety of olanzapine (OLZ) monotherapy and an olanzapine/fluoxetine combination (OFC) with placebo (PLA) for unipolar major depression with psychotic features. Under a single protocol, two 8-week, double-blind trials were conducted at 27 sites. Patients (n = 124 trial 1, n = 125 trial 2) were randomized to 1 of 3 treatment groups: OLZ (5 to 20 mg/d), PLA, or OFC (olanzapine 5 to 20 mg/d + fluoxetine 20 to 80 mg/d). The primary outcome measure was the 24-item Hamilton Depression Rating Scale total score. For trial 1, endpoint improvement for the OLZ group (-14.9) was not significantly different from the PLA or OFC groups. The OFC group had significantly greater endpoint improvement (-20.9) than the PLA group (-10.4, P = 0.001); this significant difference was present within 7 days of therapy and maintained at every subsequent visit. The OFC group also had significantly higher response rate (63.6%) than the PLA (28.0%, P = 0.004) or OLZ (34.9%, P = 0.027) groups. For trial 2, there were no significant differences among treatment groups on the 24-item Hamilton Depression Rating Scale total scores or response rates. The combination exhibited a comparable safety profile with OLZ monotherapy and no significant increases in extrapyramidal symptoms compared with placebo. Patients with major depression with psychotic features treated with OLZ monotherapy did not demonstrate significant depressive symptom improvement compared with placebo in either trial; however, an olanzapine/fluoxetine combination was associated with significant improvement compared with placebo in one trial and was well tolerated.
The Ontology of Biological and Clinical Statistics (OBCS) for standardized and reproducible statistical analysis

Statistics play a critical role in biological and clinical research. However, most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The Ontology of Biological and Clinical Statistics (OBCS) is put forward here as a step towards solving this problem.



The terms in OBCS including 'data collection', 'data transformation in statistics', 'data visualization', 'statistical data analysis', and 'drawing a conclusion based on data', cover the major types of statistical processes used in basic biological research and clinical outcome studies. OBCS is aligned with the Basic Formal Ontology (BFO) and extends the Ontology of Biomedical Investigations (OBI), an OBO (Open Biological and Biomedical Ontologies) Foundry ontology supported by over 20 research communities. Currently, OBCS comprehends 878 terms, representing 20 BFO classes, 403 OBI classes, 229 OBCS specific classes, and 122 classes imported from ten other OBO ontologies. We discuss two examples illustrating how the ontology is being applied. In the first (biological) use case, we describe how OBCS was applied to represent the high throughput microarray data analysis of immunological transcriptional profiles in human subjects vaccinated with an influenza vaccine. In the second (clinical outcomes) use case, we applied OBCS to represent the processing of electronic health care data to determine the associations between hospital staffing levels and patient mortality. Our case studies were designed to show how OBCS can be used for the consistent representation of statistical analysis pipelines under two different research paradigms. Other ongoing projects using OBCS for statistical data processing are also discussed. The OBCS source code and documentation are available at: https://github.com/obcs/obcs .



The Ontology of Biological and Clinical Statistics (OBCS) is a community-based open source ontology in the domain of biological and clinical statistics. OBCS is a timely ontology that represents statistics-related terms and their relations in a rigorous fashion, facilitates standard data analysis and integration, and supports reproducible biological and clinical research.
Comparison of psilocin with psilocybin, mescaline and LSD-25

1. Reactions induced by LSD, mescaline, psilocin, and psilocybin are qualitatively similar. 2. The time course of the psilocin and psilocybin reactions are shorter than those of LSD or mescaline reactions. li 4. Psilocin is approximately 1.4 times as potent as psilocybin. This ratio is the same as that of the molecular weights of the two drugs. Reactions induced by LSD, mescaline, psilocin, and psilocybin are qualitatively similar. The time course of the psilocin and psilocybin reactions are shorter than those of LSD or mescaline reactions. li Psilocin is approximately 1.4 times as potent as psilocybin. This ratio is the same as that of the molecular weights of the two drugs.
Boosting complete-code tool for partial program

To improve software quality, researchers and practitioners have proposed static analysis tools for various purposes (e.g., detecting bugs, anomalies, and vulnerabilities). Although many such tools are powerful, they typically need complete programs where all the code names (e.g., class names, method names) are resolved. In many scenarios, researchers have to analyze partial programs in bug fixes (the revised source files can be viewed as a partial program), tutorials, and code search results. As a partial program is a subset of a complete program, many code names in partial programs are unknown. As a result, despite their syntactical correctness, existing complete-code tools cannot analyze partial programs, and existing partial-code tools are limited in both their number and analysis capability. Instead of proposing another tool for analyzing partial programs, we propose a general approach, called GRAPA, that boosts existing tools for complete programs to analyze partial programs. Our major insight is that after unknown code names are resolved, tools for complete programs can analyze partial programs with minor modifications. In particular, GRAPA locates Java archive files to resolve unknown code names, and resolves the remaining unknown code names from resolved code names. To illustrate GRAPA, we implement a tool that leverages the state-of-the-art tool, WALA, to analyze Java partial programs. We thus implemented the first tool that is able to build system dependency graphs for partial programs, complementing existing tools. We conduct an evaluation on 8,198 partial-code commits from four popular open source projects. Our results show that GRAPA fully resolved unknown code names for 98.5% bug fixes, with an accuracy of 96.1% in total. Furthermore, our results show the significance of GRAPA's internal techniques, which provides insights on how to integrate with more complete-code tools to analyze partial programs.
SfSNet: Learning Shape, Reflectance and Illuminance of Faces 'in the Wild'

We present SfSNet, an end-to-end learning framework for producing an accurate decomposition of an unconstrained human face image into shape, reflectance and illuminance. SfSNet is designed to reflect a physical lambertian rendering model. SfSNet learns from a mixture of labeled synthetic and unlabeled real world images. This allows the network to capture low frequency variations from synthetic and high frequency details from real images through the photometric reconstruction loss. SfSNet consists of a new decomposition architecture with residual blocks that learns a complete separation of albedo and normal. This is used along with the original image to predict lighting. SfSNet produces significantly better quantitative and qualitative results than state-of-the-art methods for inverse rendering and independent normal and illumination estimation.
An investigation of the Therac-25 accidents

Between June 1985 and January 1987, the Therac-25 medical electron accelerator was involved in six massive radiation overdoses. As a result, several people died and others were seriously injured. A detailed investigation of the factors involved in the software-related overdoses and attempts by users, manufacturers, and government agencies to deal with the accidents is presented. The authors demonstrate the complex nature of accidents and the need to investigate all aspects of system development and operation in order to prevent future accidents. The authors also present some lessons learned in terms of system engineering, software engineering, and government regulation of safety-critical systems containing software components.<<ETX>>
c-Ha-ras oncogene expression in immortalized human keratinocytes (HaCaT) alters growth potential in vivo but lacks correlation with malignancy.

Spontaneously immortalized human skin keratinocytes (HaCaT) were transfected with the c-Ha-ras (EJ) oncogene via a plasmid construct which also contained the selectable neomycin gene. Clones were selected on the basis of G418 resistance. Those clones that had stable integrants of Ha-ras fell into 3 classes with respect to tumorigenicity. Class I clones were nontumorigenic, i.e., formed nodules which rapidly regressed. This phenotype is identical to that seen with parental HaCaT cells. Class II clones formed slowly growing, highly differentiated cystic or papillomatous-type benign tumors, and class III clones formed highly differentiated, locally invasive squamous cell carcinomas. The clones of all three classes exhibited similar morphology and growth potential in culture and retained the ability to reconstitute an epidermis-like stratified epithelium in transplantation experiments. Only the malignant clones showed locally invasive growth. Both the benign and the malignant clones exhibited higher levels of ras integration and variable levels of mutated p21 protein product. Thus, expression of the cellular Ha-ras oncogene in these human epithelial cells significantly altered growth regulation, resulting in varying degrees of growth potential in vivo, ranging from benign to malignant tumors. However, no direct correlation was seen between high levels of p21 expression and malignant growth.
Three critical challenges for modeling and simulation in healthcare

By most measures, the adoption of modeling and simulation techniques in healthcare service development falls well short of the uptake of such techniques evident in other sectors, such as business and commerce or aerospace and the military. The question is, why? To answer this, we consider three questions and then turn to the nature of answer which might lead towards greater adoption. The first is the vexed question of how good is good enough? The second concerns how best modeling should link through to decision-making; and the third concerns the culture needed to make the most of modeling and simulation (and whether it is worth the effort to make the transformation). From these, we draw an agenda for further enquiry in terms of stakeholders, their culture, data, and expectations, and the case in terms of value.
Low-Profile, Quasi-Omnidirectional Substrate Integrated Waveguide (SIW) Multihorn Antenna

A low-profile, quasi-omnidirectional substrate integrated waveguide (SIW) multihorn antenna is proposed for small nonmetallic unmanned aerial vehicle (UAV) applications. The proposed antenna consists of four H-plane horn antennas and four matching vias. The four H-plane horn antennas are integrated in a single rectangular substrate using the SIW technology and are located concentrically toward the four edges of the substrate. Four shorting vias are designed to match the horn antennas without increasing the antenna size. The proposed antenna is fed by a probe at the center of the substrate. The antenna is vertically polarized and has a very low profile (0.028λ0). The radiation pattern of the antenna is quasi-omnidirectional on the horizontal plane. A prototype with a dimension of 95.5 × 95.5 × 3.5 mm2 (0.76λ0 × 0.76λ0 × 0.028λ0) is designed, fabricated, and measured. The antenna achieves an impedance bandwidth of 2.1% from 2.39 to 2.44 GHz. The antenna does not need a ground plane. Thus, it is a good candidate for small nonmetallic UAV applications.
Unifying Conceptual Spaces: Concept Formation in Musical Creative Systems

We examine Gärdenfors’ theory of conceptual spaces, a geometrical form of knowledge representation (Conceptual spaces: The geometry of thought, MIT Press, Cambridge, 2000), in the context of the general Creative Systems Framework introduced by Wiggins (J Knowl Based Syst 19(7):449–458, 2006a; New Generation Comput 24(3):209–222, 2006b). Gärdenfors’ theory offers a way of bridging the traditional divide between symbolic and sub-symbolic representations, as well as the gap between representational formalism and meaning as perceived by human minds. We discuss how both these qualities may be advantageous from the point of view of artificial creative systems. We take music as our example domain, and discuss how a range of musical qualities may be instantiated as conceptual spaces, and present a detailed conceptual space formalisation of musical metre.
An Enhanced Slicing Algorithm Using Nearest Distance Analysis for Layer Manufacturing

Although the STL (stereo lithography) file format is widely used as a de facto industry standard in the rapid prototyping industry due to its simplicity and ability to tessellation of almost all surfaces, but there are always some defects and shortcoming in their usage, which many of them are difficult to correct manually. In processing the complex models, size of the file and its defects grow extremely, therefore, correcting STL files become difficult. In this paper through optimizing the exiting algorithms, size of the files and memory usage of computers to process them will be reduced. In spite of type and extent of the errors in STL files, the tail-to-head searching method and analysis of the nearest distance between tails and heads techniques were used. As a result STL models sliced rapidly, and fully closed contours produced effectively and errorless. Layer manufacturing, STL files, slicing algorithm, nearest distance analysis.
Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit Detection

Fully-automatic facial expression recognition (FER) is a key component of human behavior analysis. Performing FER from still images is a challenging task as it involves handling large interpersonal morphological differences, and as partial occlusions can occasionally happen. Furthermore, labelling expressions is a time-consuming process that is prone to subjectivity, thus the variability may not be fully covered by the training data. In this work, we propose to train random forests upon spatially-constrained random local subspaces of the face. The output local predictions form a categorical expression-driven high-level representation that we call local expression predictions (LEPs). LEPs can be combined to describe categorical facial expressions as well as action units (AUs). Furthermore, LEPs can be weighted by confidence scores provided by an autoencoder network. Such network is trained to locally capture the manifold of the non-occluded training data in a hierarchical way. Extensive experiments show that the proposed LEP representation yields high descriptive power for categorical expressions and AU occurrence prediction, and leads to interesting perspectives towards the design of occlusion-robust and confidence-aware FER systems.
Vibration control of a nonlinear quarter-car active suspension system by reinforcement learning

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NOMA-Based Spatial Modulation

Spatial modulation (SM) has emerged as a low-complexity and energy-efficient multiple-input multiple-output transmission technique, where the information bits are not only transmitted by amplitude phase modulation but also conveyed by the index of activated transmit antenna (TA). By deploying SM in downlink multi-user (DL-MU) scenarios, conventional orthogonal multiple access-based SM (OMA-SM) allocates exclusive time-frequency resources to users, but suffers from low spectral efficiency. TA grouping-based SM (TAG-SM) divides TAs into sub-groups to serve different users independently, but suffers from severe inter-user interference. By introducing non-OMA (NOMA) into SM for DL-MU transmission, NOMA-based SM (NOMA-SM) is proposed to mitigate inter-user interference, while maintaining high spectral efficiency. Specifically, by applying successive interference cancellation at user side, the inter-user interference could be effectively eliminated with the sacrifice of increased computational complexity. Afterward, based on a symbol error rate analysis, a low-complexity power allocation scheme is provided to achieve high spectral efficiency through power domain multiplexing. When considering near-far effect from user distribution, user pairing issue is also discussed. Numerical simulation compares NOMA-SM with OMA-SM and TAG-SM, and verifies the effectiveness of the proposed low-complexity power allocation and user pairing methodologies.