nils-herrmann/clean_scirepeval huggingface dataset:
Personality development through natural language

Ego level is a broad construct that summarizes individual differences in personality development 1 . We examine ego level as it is represented in natural language, using a composite sample of four datasets comprising nearly 44,000 responses. We find support for a developmental sequence in the structure of correlations between ego levels, in analyses of Linguistic Inquiry and Word Count (LIWC) categories 2 and in an examination of the individual words that are characteristic of each level. The LIWC analyses reveal increasing complexity and, to some extent, increasing breadth of perspective with higher levels of development. The characteristic language of each ego level suggests, for example, a shift from consummatory to appetitive desires at the lowest stages, a dawning of doubt at the Self-aware stage, the centrality of achievement motivation at the Conscientious stage, an increase in mutuality and intellectual growth at the Individualistic stage and some renegotiation of life goals and reflection on identity at the highest levels of development. Continuing empirical analysis of ego level and language will provide a deeper understanding of ego development, its relationship with other models of personality and individual differences, and its utility in characterizing people, texts and the cultural contexts that produce them. A linguistic analysis of nearly 44,000 responses to the Washington University Sentence  Test elucidates the construct of ego development (personality development through adulthood) and identifies unique linguistic markers of each level of development.
A low profile LLC resonant converter using novel planar transformer

In this paper, a low profile LLC resonant converter with two planar transformers is proposed for a slim SMPS (Switching Mode Power Supply). Design procedures and voltage gain characteristics on the proposed planar transformer and converter are described in detail. Two planar transformers applied to LLC resonant converter are connected in series at primary and in parallel by the center-tap winding at secondary. Based on the theoretical analysis and simulation results of the voltage gain characteristics, a 300W LLC resonant converter for LED TV power module is designed and tested.
Usability Meanings and Interpretations in ISO Standards

The usability of a software product has recently become a key software quality factor. The International Organization for Standardization (ISO) has developed a variety of models to specify and measure software usability but these individual models do not support all usability aspects. Furthermore, they are not yet well integrated into current software engineering practices and lack tool support. The aim of this research is to survey the actual representation (meanings and interpretations) of usability in ISO standards, indicate some of existing limitations and address them by proposing an enhanced, normative model for the evaluation of software usability.
The genome of Prunus mume

Prunus mume (mei), which was domesticated in China more than 3,000 years ago as ornamental plant and fruit, is one of the first genomes among Prunus subfamilies of Rosaceae been sequenced. Here, we assemble a 280M genome by combining 101-fold next-generation sequencing and optical mapping data. We further anchor 83.9% of scaffolds to eight chromosomes with genetic map constructed by restriction-site-associated DNA sequencing. Combining P. mume genome with available data, we succeed in reconstructing nine ancestral chromosomes of Rosaceae family, as well as depicting chromosome fusion, fission and duplication history in three major subfamilies. We sequence the transcriptome of various tissues and perform genome-wide analysis to reveal the characteristics of P. mume, including its regulation of early blooming in endodormancy, immune response against bacterial infection and biosynthesis of flower scent. The P. mume genome sequence adds to our understanding of Rosaceae evolution and provides important data for improvement of fruit trees.
A Family of Droids: Analyzing Behavioral Model based Android Malware Detection via Static and Dynamic Analysis

Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device’s owner. Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed. While the pros and cons of these analysis techniques are known, they are usually compared in the context of their limitations e.g., static analysis is not able to capture runtime behaviors, full code coverage is usually not achieved during dynamic analysis, etc. Whereas, in this paper, we analyze the performance of static and dynamic analysis methods in the detection of Android malware and attempt to compare them in terms of their detection performance, using the same modeling approach. To this end, we build on MAMADROID, a state-of-the-art detection system that relies on static analysis to create a behavioral model from the sequences of abstracted API calls. Then, aiming to apply the same technique in a dynamic analysis setting, we modify CHIMP, a platform recently proposed to crowdsource human inputs for app testing, in order to extract API calls’ sequences from the traces produced while executing the app on a CHIMP virtual device. We call this system AUNTIEDROID and instantiate it by using both automated (Monkey) and user-generated inputs. We find that combining both static and dynamic analysis yields the best performance, with F -measure reaching 0.92. We also show that static analysis is at least as effective as dynamic analysis, depending on how apps are stimulated during execution, and, finally, investigate the reasons for inconsistent misclassifications across methods.
Process of Building Reference Ontology for Higher Education

Most ontologies are application ontologies, that are not reusable and are difficult to link together as they are too specific. Reference ontology is able to contribute significantly in reducing the issue of ontology applications specificity. Particularly considering higher education domain, we think that a reference ontology dedicated to this knowledge area, can be regarded as a valuable tool for several stakeholders interested in analyzing the system of higher education as a whole, especially in a context of academic systems diversity all over the world. Motivated by this potential application and even more, we decided to build a reference ontology called HERO ontology, which stands for “Higher Education Reference Ontology”. In this paper we explain HERO ontology building process from requirements specification until ontology evaluation using NeOn methodology.
Compact combline filter with improved cross coupling assembly and temperature compensation

A base-station bandpass filter using compact stepped combline resonators is presented. The bandpass filter consists of 4 resonators, has a center-frequency of 2.0175 GHz, a bandwidth of 15 MHz and cross-coupling by a cascaded quadruplet for improved blocking performance. The combline resonators have different size. Therefore, different temperature compensation arrangements need to be applied to guarantee stable performance in the temperature range from -40deg C to 85deg C. The layout will be discussed. A novel cross coupling assembly is introduced. Furthermore, measurement results are shown.
Adipose tissue as an endocrine organ.

Adipose tissue is a complex, essential, and highly active metabolic and endocrine organ. Besides adipocytes, adipose tissue contains connective tissue matrix, nerve tissue, stromovascular cells, and immune cells. Together these components function as an integrated unit. Adipose tissue not only responds to afferent signals from traditional hormone systems and the central nervous system but also expresses and secretes factors with important endocrine functions. These factors include leptin, other cytokines, adiponectin, complement components, plasminogen activator inhibitor-1, proteins of the renin-angiotensin system, and resistin. Adipose tissue is also a major site for metabolism of sex steroids and glucocorticoids. The important endocrine function of adipose tissue is emphasized by the adverse metabolic consequences of both adipose tissue excess and deficiency. A better understanding of the endocrine function of adipose tissue will likely lead to more rational therapy for these increasingly prevalent disorders. This review presents an overview of the endocrine functions of adipose tissue.
Event-centric Context Modeling : The Case of Story Comprehension and Story Generation

In this opinion piece, we argue that there is a need for alternative design directions to complement existing AI efforts in narrative and character generation and algorithm development. To make our argument, we a) outline the predominant roles and goals of AI research in storytelling; b) present existing discourse on the benefits and harms of narratives; and c) highlight the pain points in character creation revealed by semi-structured interviews we conducted with 14 individuals deeply involved in some form of character creation. We conclude by proffering several specific design avenues that we believe can seed fruitful research collaborations. In our vision, AI collaborates with humans during creative processes and narrative generation, helps amplify voices and perspectives that are currently marginalized or misrepresented, and engenders experiences of narrative that support spectatorship and listening roles.
Undithering using linear filtering and non-linear diffusion techniques

Data compression is a method of improving the efficiency of transmission and storage of images. Dithering, as a method of data compression, can be used to convert an 8-bit gray level image into a 1-bit / binary image. Undithering is the process of reconstruction of gray image from binary image obtained from dithering of gray image. In the present paper, I propose a method of undithering using linear filtering followed by anisotropic diffusion which brings the advantage of smoothing and edge enhancement. First-order statistical parameters, second-order statistical parameters, mean-squared error (MSE) between reconstructed image and the original image before dithering, and peak signal to noise ratio (PSNR) are evaluated at each step of diffusion. Results of the experiments show that the reconstructed image is not as sharp as the image before dithering but a large number of gray values are reproduced with reference to those of the original image prior to dithering.
Joint rate and channel width adaptation for 802.11 MIMO wireless networks

The emergence of MIMO antennas and channel bonding in 802.11n wireless networks has resulted in a huge leap in capacity compared with legacy 802.11 systems. This leap, however, adds complexity to selecting the right transmission rate. Not only does the appropriate data rate need to be selected, but also the MIMO transmission technique (e.g., Spatial Diversity or Spatial Multiplexing), the number of streams, and the channel width. Incorporating these features into a rate adaptation (RA) solution requires a new set of rules to accurately evaluate channel conditions and select the appropriate transmission setting with minimal overhead. To address these challenges, we propose ARAMIS (Agile Rate Adaptation for MIMO Systems), a standard-compliant, closed-loop RA solution that jointly adapts rate and bandwidth. ARAMIS adapts transmission rates on a per-packet basis; we believe it is the first 802.11n RA algorithm that simultaneously adapts rate and channel width. We have implemented ARAMIS on Atheros-based devices and deployed it on our 15-node testbed. Our experiments show that ARAMIS accurately adapts to a wide variety of channel conditions with negligible overhead. Furthermore, ARAMIS outperforms existing RA algorithms in 802.11n environments with up to a 10 fold increase in throughput.
Evaluation of Wet Cupping Treatment in Patients with Chronic Urticaria and Angioedema

Urticaria is a kind of skin rash that sometimes caused by allergic reactions. Acute viral infection, stress, pressure, exercise and sunlight are some other causes of urticaria. However, chronic urticaria and angioedema could be either idiopathic or caused by autoimmune reaction. They last more than six weeks and could even persist for a very long time. It is thought that the level of C-reactive protein CRP increases and the level of Erythrocyte sedimentation rate (ESR) decreases in patients with chronic urticaria. Thirty four patients with chronic or recurrent urticaria were selected for the treatment with wet cupping. Six of them, because of having a history of recent infection/cold urticaria, were eliminated and the remaining 28 were chosen for this study. ESR and CRP were measured in these patients aged 21-59, comprising 12 females and 16 males, ranged from 5-24 mm/h for ESR with a median 11 mm/h and 3.3-31.2 mg/L with a median of 11.95 mg/L for CRP before and after phlebotomy (250-450mL) which was performed as a control for wet cupping therapy. Three weeks after phlebotomy, wet cupping was performed on the back of these patients between two shoulders and the levels of ESR and CRP were measured again three weeks after wet cupping. The changes were observed in the level of CRP and ESR after phlebotomy being negligible. However, the level of CRP with a median 11.95 before wet cupping dramatically dropped to 1.1 after wet cupping. The level ESR also with a median 11 before wet cupping rose to 15.5 after wet cupping therapy. The clear correlation between the urticaria/angioedema and the rise of CRP was observed as was anticipated. No recurrence has been observed on twenty five of these patients and three of them are still recovering from the lesions.
Nutritional Ketosis for Weight Management and Reversal of Metabolic Syndrome.

The goal of this paper is to review current literature on nutritional ketosis within the context of weight management and metabolic syndrome, namely, insulin resistance, lipid profile, cardiovascular disease risk, and development of non-alcoholic fatty liver disease. We provide background on the mechanism of ketogenesis and describe nutritional ketosis.



Nutritional ketosis has been found to improve metabolic and inflammatory markers, including lipids, HbA1c, high-sensitivity CRP, fasting insulin and glucose levels, and aid in weight management. We discuss these findings and elaborate on potential mechanisms of ketones for promoting weight loss, decreasing hunger, and increasing satiety. Humans have evolved with the capacity for metabolic flexibility and the ability to use ketones for fuel. During states of low dietary carbohydrate intake, insulin levels remain low and ketogenesis takes place. These conditions promote breakdown of excess fat stores, sparing of lean muscle, and improvement in insulin sensitivity.
On Social Laws for Arti cial AgentSocieties : OLine Design

We are concerned with the utility of social laws in a computational environment laws which guarantee the successful coexistence of multi ple programs and programmers In this paper we are interested in the o line design of social laws where we as designers must decide ahead of time on useful social laws In the rst part of this paper we sug gest the use of social laws in the domain of mobile robots and prove analytic results about the usefulness of this approach in that setting In the second part of this paper we present a general model of social law in a computational system and investigate some of its proper ties This includes a de nition of the basic computational problem involved with the design of multi agent systems and an investigation of the automatic synthesis of useful social laws in the framework of a model which refers explicitly to social laws This work was supported in part by a grant from the US Israel Binational Science Foundation
Generalized Team Draft Interleaving

Interleaving is an online evaluation method that compares two ranking functions by mixing their results and interpreting the users' click feedback. An important property of an interleaving method is its sensitivity, i.e. the ability to obtain reliable comparison outcomes with few user interactions. Several methods have been proposed so far to improve interleaving sensitivity, which can be roughly divided into two areas: (a) methods that optimize the credit assignment function (how the click feedback is interpreted), and (b) methods that achieve higher sensitivity by controlling the interleaving policy (how often a particular interleaved result page is shown).
 In this paper, we propose an interleaving framework that generalizes the previously studied interleaving methods in two aspects. First, it achieves a higher sensitivity by performing a joint data-driven optimization of the credit assignment function and the interleaving policy. Second, we formulate the framework to be general w.r.t. the search domain where the interleaving experiment is deployed, so that it can be applied in domains with grid-based presentation, such as image search. In order to simplify the optimization, we additionally introduce a stratified estimate of the experiment outcome. This stratification is also useful on its own, as it reduces the variance of the outcome and thus increases the interleaving sensitivity.
 We perform an extensive experimental study using large-scale document and image search datasets obtained from a commercial search engine. The experiments show that our proposed framework achieves marked improvements in sensitivity over effective baselines on both datasets.
Lactate Inhibits the Pro-Inflammatory Response and Metabolic Reprogramming in Murine Macrophages in a GPR81-Independent Manner

Lactate is an essential component of carbon metabolism in mammals. Recently, lactate was shown to signal through the G protein coupled receptor 81 (GPR81) and to thus modulate inflammatory processes. This study demonstrates that lactate inhibits pro-inflammatory signaling in a GPR81-independent fashion. While lipopolysaccharide (LPS) triggered expression of IL-6 and IL-12 p40, and CD40 in bone marrow-derived macrophages, lactate was able to abrogate these responses in a dose dependent manner in Gpr81-/- cells as well as in wild type cells. Macrophage activation was impaired when glycolysis was blocked by chemical inhibitors. Remarkably, lactate was found to inhibit LPS-induced glycolysis in wild type as well as in Gpr81-/- cells. In conclusion, our study suggests that lactate can induce GPR81-independent metabolic changes that modulate macrophage pro-inflammatory activation.
clayodor: Retrieving Scents through the Manipulation of Malleable Material

clayodor (\klei-o-dor\) is a clay-like malleable material that changes smell based on user manipulation of its shape. This work explores the tangibility of shape changing materials to capture smell, an ephemeral and intangible sensory input. We present the design of a proof-of-concept prototype, and discussions on the challenges of navigating smell though form.
Location of lesion determines motor vs. cognitive consequences in patients with cerebellar stroke

Cerebellar lesions can cause motor deficits and/or the cerebellar cognitive affective syndrome (CCAS; Schmahmann's syndrome). We used voxel-based lesion-symptom mapping to test the hypothesis that the cerebellar motor syndrome results from anterior lobe damage whereas lesions in the posterolateral cerebellum produce the CCAS. Eighteen patients with isolated cerebellar stroke (13 males, 5 females; 20-66 years old) were evaluated using measures of ataxia and neurocognitive ability. Patients showed a wide range of motor and cognitive performance, from normal to severely impaired; individual deficits varied according to lesion location within the cerebellum. Patients with damage to cerebellar lobules III-VI had worse ataxia scores: as predicted, the cerebellar motor syndrome resulted from lesions involving the anterior cerebellum. Poorer performance on fine motor tasks was associated primarily with strokes affecting the anterior lobe extending into lobule VI, with right-handed finger tapping and peg-placement associated with damage to the right cerebellum, and left-handed finger tapping associated with left cerebellar damage. Patients with the CCAS in the absence of cerebellar motor syndrome had damage to posterior lobe regions, with lesions leading to significantly poorer scores on language (e.g. right Crus I and II extending through IX), spatial (bilateral Crus I, Crus II, and right lobule VIII), and executive function measures (lobules VII-VIII). These data reveal clinically significant functional regions underpinning movement and cognition in the cerebellum, with a broad anterior-posterior distinction. Motor and cognitive outcomes following cerebellar damage appear to reflect the disruption of different cerebro-cerebellar motor and cognitive loops.
An Empirical Comparison between Kelly Criterion and Vince's Optimal F

In this paper, we compare the differences between traditional Kelly Criterion and Vince's optimal f through backtesting actual financial transaction data. We apply a momentum trading strategy to the Taiwan Weighted Index Futures, and analyze its profit-and-loss vectors of Kelly Criterion and Vince's optimal f, respectively. Our numerical experiments demonstrate that there is nearly 90% chance that the difference gap between the bet ratio recommended by Kelly criterion and and Vince's optimal f lies within 2%. Therefore, in the actual transaction, the values from Kelly Criterion could be taken directly as the optimal bet ratio for funds control.
Annealed Sparsity via Adaptive and Dynamic Shrinking

Sparse learning has received tremendous amount of interest in high-dimensional data analysis due to its model interpretability and the low-computational cost. Among the various techniques, adaptive l1-regularization is an effective framework to improve the convergence behaviour of the LASSO, by using varying strength of regularization across different features. In the meantime, the adaptive structure makes it very powerful in modelling grouped sparsity patterns as well, being particularly useful in high-dimensional multi-task problems. However, choosing an appropriate, global regularization weight is still an open problem. In this paper, inspired by the annealing technique in material science, we propose to achieve "annealed sparsity" by designing a dynamic shrinking scheme that simultaneously optimizes the regularization weights and model coefficients in sparse (multi-task) learning. The dynamic structures of our algorithm are twofold. Feature-wise (spatially), the regularization weights are updated interactively with model coefficients, allowing us to improve the global regularization structure. Iteration-wise (temporally), such interaction is coupled with gradually boosted l1-regularization by adjusting an equality norm-constraint, achieving an annealing effect to further improve model selection. This renders interesting shrinking behaviour in the whole solution path. Our method competes favorably with state-of-the-art methods in sparse (multi-task) learning. We also apply it in expression quantitative trait loci analysis (eQTL), which gives useful biological insights in human cancer (melanoma) study.