Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence Estimates for Pharmacological Neuroenhancement in Austrian University Students: Its Relation to Health-Related Risk Attitude and the Framing Effect of Caffeine Tablets

Background: Pharmacological neuroenhancement (PN) is defined as the use of illicit or prescription drugs by healthy individuals for cognitive-enhancing purposes. The present study aimed (i) to investigate whether including caffeine tablets in the definition of PN within a questionnaire increases the PN prevalence estimate (framing effect), (ii) to investigate whether the health-related risk attitude is increased in students who use PN. Materials and methods: Two versions of a paper-and-pencil questionnaire (first version included caffeine tablets in the definition of PN, the second excluded caffeine tablets) were distributed among university students at the University of Graz, Austria. The unrelated question model (UQM) was used to estimate the 12-month PN prevalence and the German version of the 30-item Domain-Specific Risk-Taking (DOSPERT) scale to assess the health-related risk attitude. Moreover, large-sample z-tests (α = 0.05) were performed for comparing the PN prevalence estimates of two groups. Results: Two thousand four hundred and eighty-nine questionnaires were distributed and 2,284 (91.8%) questionnaires were included in analysis. The overall PN prevalence estimate for all students was 11.9%. One-tailed large-sample z-tests revealed that the PN estimate for students with higher health-related risk attitude was significantly higher compared to students with lower health-related risk attitude (15.6 vs. 8.5%; z = 2.65, p = 0.004). Furthermore, when caffeine tablets were included into the example of PN, the prevalence estimate of PN was significantly higher compared to the version without caffeine tablets (14.9 vs. 9.0%; z = 2.20, p = 0.014). Discussion: This study revealed that the PN prevalence estimate increases when caffeine tablets are included in the definition of PN. Therefore, future studies investigating the prevalence of, and predictors for, PN should be performed and interpreted with respect to potential framing effects. This study further revealed that the PN prevalence estimate is increased in students with a higher health-related risk attitude compared to students with a lower one. Therefore, future education and prevention programs addressing PN in the collective of students should not only inform about potential side effects of its use but also address the limited effects on cognition and potential alternatives of PN.
Critical arcs detection in influence networks

The influence class of network problems models the propagation of influence (an abstraction of cascading beliefs, behaviors, or physical phenomena) in a network. Such problems have applications in social networks, electrical networks, computer networks, viral spreading, and so on. These types of networks have also been studied through the lens of critical arcs detection; that is, which arcs (edges) are the most important for maintaining some property of the network (e.g., connectivity). We introduce a new class of problems at the intersection of these two models. Specifically, given a set of seed nodes and the linear threshold influence propagation model, our work proposes to determine which arcs (e.g., relationships in a social network or communication pathways in a telecommunication network) are most critical to the influence propagation process. We prove NP‐hardness of the problem. Time‐dependent and time‐independent mixed‐integer programming (MIP) models are introduced. Insights gleaned from MIP solutions leads to the development of an improved MIP‐based exact algorithm rooted in the idea of diffusion expansion. A heuristic based upon a new centrality measure is also proposed, and computational results are presented. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 71(4), 412–431 2018
Solving Multi-Objective Workflow Satisfiability Problems with Optimization Modulo Theories Techniques

Security-sensitive workflows impose constraints on the control-flow and authorization policies that may lead to unsatisfiable instances. In these cases, it is still possible to find "least bad" executions where costs associated to authorization violations are minimized, solving the so-called Multi-Objective Workflow Satisfiability Problem (MO-WSP). The MO-WSP is inspired by the Valued WSP and its generalization, the Bi-Objective WSP, but our work considers quantitative solutions to the WSP without abstracting control-flow constraints. In this paper, we define variations of the MO-WSP and solve them using bounded model checking and optimization modulo theories solving. We validate our solutions on real-world workflows and show their scalability on synthetic instances.
The PMEmo Dataset for Music Emotion Recognition

Music Emotion Recognition (MER) has recently received considerable attention. To support the MER research which requires large music content libraries, we present the PMEmo dataset containing emotion annotations of 794 songs as well as the simultaneous electrodermal activity (EDA) signals. A Music Emotion Experiment was well-designed for collecting the affective-annotated music corpus of high quality, which recruited 457 subjects. The dataset is publically available to the research community, which is foremost intended for benchmarking in music emotion retrieval and recognition. To straightforwardly evaluate the methodologies for music affective analysis, it also involves pre-computed audio feature sets. In addition to that, manually selected chorus excerpts (compressed in MP3) of songs are provided to facilitate the development of chorus-related research. In this article, We describe in detail the resource acquisition, subject selection, experiment design and annotation collection procedures, as well as the dataset content and data reliability analysis. We also illustrate its usage in some simple music emotion recognition tasks which testified the PMEmo dataset's competence for the MER work. Compared to other homogeneous datasets, PMEmo is novel in the organization and management of the recruited annotators, and it is also characterized by its large amount of music with simultaneous physiological signals.
BDNF-TrkB Signaling Coupled to nPKCε and cPKCβI Modulate the Phosphorylation of the Exocytotic Protein Munc18-1 During Synaptic Activity at the Neuromuscular Junction

Munc18-1, a neuron-specific member of the Sec1/Munc18 family, is involved in neurotransmitter release by binding tightly to syntaxin. Munc18-1 is phosphorylated by PKC on Ser-306 and Ser-313 in vitro which reduces the amount of Munc18-1 able to bind syntaxin. We have previously identified that PKC is involved in neurotransmitter release when continuous electrical stimulation imposes a moderate activity on the NMJ and that muscle contraction through TrkB has an important impact on presynaptic PKC isoforms levels, specifically cPKCβI and nPKCε. Therefore, the present study was designed to understand how Munc18-1 phosphorylation is affected by (1) synaptic activity at the neuromuscular junction, (2) nPKCε and cPKCβI isoforms activity, (3) muscle contraction per se, and (4) the BDNF/TrkB signaling in a neuromuscular activity-dependent manner. We performed immunohistochemistry and confocal techniques to evidence the presynaptic location of Munc18-1 in the rat diaphragm muscle. To study synaptic activity, we stimulated the phrenic nerve (1 Hz, 30 min) with or without contraction (abolished by μ-conotoxin GIIIB). Specific inhibitory reagents were used to block nPKCε and cPKCβI activity and to modulate the tropomyosin receptor kinase B (TrkB). Main results obtained from Western blot experiments showed that phosphorylation of Munc18-1 at Ser-313 increases in response to a signaling mechanism initiated by synaptic activity and directly mediated by nPKCε. Otherwise, cPKCβI and TrkB activities work together to prevent this synaptic activity–induced Munc18-1 phosphorylation by a negative regulation of cPKCβI over nPKCε. Therefore, a balance between the activities of these PKC isoforms could be a relevant cue in the regulation of the exocytotic apparatus. The results also demonstrate that muscle contraction prevents the synaptic activity–induced Munc18-1 phosphorylation through a mechanism that opposes the TrkB/cPKCβI/nPKCε signaling.
Fully Convolutional Networks With Sequential Information for Robust Crop and Weed Detection in Precision Farming

Reducing the use of agrochemicals is an important component toward sustainable agriculture. Robots that can perform targeted weed control offer the potential to contribute to this goal, for example, through specialized weeding actions such as selective spraying or mechanical weed removal. A prerequisite of such systems is a reliable and robust plant classification system that is able to distinguish crop and weed in the field. A major challenge in this context is the fact that different fields show a large variability. Thus, classification systems have to robustly cope with substantial environmental changes with respect to weed pressure and weed types, growth stages of the crop, visual appearance, and soil conditions. In this letter, we propose a novel crop-weed classification system that relies on a fully convolutional network with an encoder-decoder structure and incorporates spatial information by considering image sequences. Exploiting the crop arrangement information that is observable from the image sequences enables our system to robustly estimate a pixel-wise labeling of the images into crop and weed, i.e., a semantic segmentation. We provide a thorough experimental evaluation, which shows that our system generalizes well to previously unseen fields under varying environmental conditions—a key capability to actually use such systems in precision framing. We provide comparisons to other state-of-the-art approaches and show that our system substantially improves the accuracy of crop-weed classification without requiring a retraining of the model.
Grade of Cataract and Its Influence on Measurement of Macular Pigment Optical Density Using Autofluorescence Imaging

Purpose To evaluate the influence of cataracts on measuring macular pigment optical density (MPOD) using a dual-wavelength confocal scanning autofluorescence imaging technique and to establish methods to compensate for the influence of cataracts. Methods This prospective case series comprised 100 eyes that underwent cataract surgery. Cataracts were graded based on the World Health Organization classification. MPOD levels were measured with the MPOD module of the Spectralis MultiColor instrument (Spectralis-MP), pre- and postoperatively. We investigated the relationship between change in MPOD values and age, cataract grade, and quality of autofluorescence images. Local MPOD levels were evaluated for four strategically chosen eccentricities within the macular region, and the total MPOD volume was evaluated within 8.98° eccentricity from the center. Results MPOD levels could be obtained in 67 eyes before surgery. Local and volume MPOD levels were higher postoperatively relative to preoperatively in all eyes. The mean ratio of local MPOD levels after and before surgery (correction factor, CF) ranged from 1.42 to 1.77, with larger CFs required for eccentricities closer to the foveal center. The CF for the MPOD volume was 1.31. Age, grade of nuclear cataract (NUC), posterior subcapsular opacity, and image quality index (IQI) significantly contributed to CFs. For example, regression equation for CF at 0.23° = 0.17 + 0.16 × IQI + 0.29 × NUC grade + 0.01 × age (P < 0.001). Conclusions Cataracts affected MPOD measurements with the Spectralis-MP, but corrected MPOD results could be obtained via regression equations.
Query Answering in Probabilistic Data and Knowledge Bases

Probabilistic data and knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic database systems, which are widely and successfully employed. Beyond all the success stories, however, such systems still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which limits their potential applications in practice. In particular, in their classical form, such systems are typically based on strong, unrealistic limitations, such as the closed-world assumption, the closed-domain assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These limitations do not only lead to unwanted consequences, but also put such systems on weak footing in important tasks, querying answering being a very central one. In this thesis, we enhance probabilistic data and knowledge bases with more realistic data models, thereby allowing for better means for querying them. Building on the long endeavor of unifying logic and probability, we develop different rigorous semantics for probabilistic data and knowledge bases, analyze their computational properties and identify sources of (in)tractability and design practical scalable query answering algorithms whenever possible. To achieve this, the current work brings together some recent paradigms from logics, probabilistic inference, and database theory.
Exact, complete expressions for the thermodynamic costs of circuits

The generalized Landauer's bound gives the minimal amount of heat generated by any physical process that implements a given computation, when there are no constraints on the process. However, common engineered systems implement computations using circuits, which introduces constraints on the physical process. So the minimal amount of heat that a circuit generates is greater than the minimal amount of heat required to implement its computation. To analyze this increase in the minimal heat, we first decompose the heat generated by any physical process that implements a given dynamics, into a sum of three terms. The first term, the drop of entropy of the state distribution, is independent of the details of the physical process, beyond the fact that it implements the desired dynamics. The second and third terms instead reflect the "nitty gritty details" of the process. The second term has a purely information-theoretic character, while the third term is a linear function of the initial distribution of states. (As an important example, we show that all of the heat generated by wires in circuits arises in this third term.) Next, we introduce a special kind of process, "subsystem processes", which formalize what it means to run a particular gate in a circuit. We then use these results to derive an exact equation for the amount of heat generated by any physical process that implements a specified circuit, i.e., that runs all the gates in the circuit, in succession. This equation provides a new optimization problem for circuit designers: how to design a circuit that both implements a desired computation and generates minimal heat. Interestingly, the objective function of this optimization problem involves functions that are information-theoretic in character but are apparently new to the information theory literature.
Multimodal Continuous Prediction of Emotions in Movies using Long Short-Term Memory Networks

Predicting emotions that movies are designed to evoke, can be useful in entertainment applications such as content personalization, video summarization and ad placement. Multimodal input, primarily audio and video, helps in building the emotional content of a movie. Since the emotion is built over time by audio and video, the temporal context of these modalities is an important aspect in modeling it. In this paper, we use Long Short-Term Memory networks (LSTMs) to model the temporal context in audio-video features of movies. We present continuous emotion prediction results using a multimodal fusion scheme on an annotated dataset of Academy Award winning movies. We report a significant improvement over the state-of-the-art results, wherein the correlation between predicted and annotated values is improved from 0.62 vs 0.84 for arousal, and from 0.29 to 0.50 for valence.
SWOOP: software-hardware co-design for non-speculative, execute-ahead, in-order cores

Increasing demands for energy efficiency constrain emerging hardware. These new hardware trends challenge the established assumptions in code generation and force us to rethink existing software optimization techniques. We propose a cross-layer redesign of the way compilers and the underlying microarchitecture are built and interact, to achieve both performance and high energy efficiency. In this paper, we address one of the main performance bottlenecks — last-level cache misses — through a software-hardware co-design. Our approach is able to hide memory latency and attain increased memory and instruction level parallelism by orchestrating a non-speculative, execute-ahead paradigm in software (SWOOP). While out-of-order (OoO) architectures attempt to hide memory latency by dynamically reordering instructions, they do so through expensive, power-hungry, speculative mechanisms.We aim to shift this complexity into software, and we build upon compilation techniques inherited from VLIW, software pipelining, modulo scheduling, decoupled access-execution, and software prefetching. In contrast to previous approaches we do not rely on either software or hardware speculation that can be detrimental to efficiency. Our SWOOP compiler is enhanced with lightweight architectural support, thus being able to transform applications that include highly complex control-flow and indirect memory accesses.
Multi-task learning of daily work and study round-trips from survey data

In this study, we present a machine learning approach to infer the worker and student mobility flows on daily basis from static censuses. The rapid urbanization has made the estimation of the human mobility flows a critical task for transportation and urban planners. The primary objective of this paper is to complete individuals' census data with working and studying trips, allowing its merging with other mobility data to better estimate the complete origin-destination matrices. Worker and student mobility flows are among the most weekly regular displacements and consequently generate road congestion problems. Estimating their round-trips eases the decision-making processes for local authorities. Worker and student censuses often contain home location, work places and educational institutions. We thus propose a neural network model that learns the temporal distribution of displacements from other mobility sources and tries to predict them on new censuses data. The inclusion of multi-task learning in our neural network results in a significant error rate control in comparison to single task learning.
High Dimensional Data Enrichment: Interpretable, Fast, and Data-Efficient

High dimensional structured data enriched model describes groups of observations by shared and per-group individual parameters, each with its own structure such as sparsity or group sparsity. In this paper, we consider the general form of data enrichment where data comes in a fixed but arbitrary number of groups G. Any convex function, e.g., norms, can characterize the structure of both shared and individual parameters. We propose an estimator for high dimensional data enriched model and provide conditions under which it consistently estimates both shared and individual parameters. We also delineate sample complexity of the estimator and present high probability non-asymptotic bound on estimation error of all parameters. Interestingly the sample complexity of our estimator translates to conditions on both per-group sample sizes and the total number of samples. We propose an iterative estimation algorithm with linear convergence rate and supplement our theoretical analysis with synthetic and real experimental results. Particularly, we show the predictive power of data-enriched model along with its interpretable results in anticancer drug sensitivity analysis.
Effects of thermal treatment on the properties of defatted soya bean flour and its adhesion to plywood

With an attempt to economically and efficiently improve the water resistance of defatted soya bean flour (DSF)-based wood adhesives, DSF was subjected to thermal treatment at various temperatures (65°C, 80°C, 95°C, 110°C and 125°C) for 30 min. The effects of thermal treatment temperature onto the chemical structure, crystalline degree, water-insoluble content and acetaldehyde value of the thermally treated DSF (T-DSF) were investigated. The thermal stabilities and bonding properties of soya bean adhesives prepared from T-DSF and cross-linker epichlorohydrin-modified polyamide (EMPA) were also investigated. Test results indicated that both the water-insoluble content and the acetaldehyde value of T-DSF increased after thermal treatment, reaching the highest values of 27.28% and 26.81 mg g−1, respectively. All plywood bonded with the T-DSF-based adhesive withstood a 28 h boiling–dry–boiling accelerated ageing treatment, while plywood bonded with the DSF-based adhesive delaminated after 4 h of water boiling, demonstrating the significantly improved water resistance of the T-DSF-based adhesives. Related analyses also confirmed that this improvement was due to: (i) the formation of insoluble cross-linked structures of T-DSF resulting from protein–protein self-cross-linking reactions and the protein–carbohydrate Maillard reaction and (ii) increased cross-linking efficiency between T-DSF and cross-linker EMPA owing to more T-DSF-reactive groups being released after thermal treatment.
Invasive plants accelerate nitrogen cycling: evidence from experimental woody monocultures

Although it is widely believed that non‐native invasive species threaten the functional integrity of forest ecosystems, their impact on important ecosystem processes such as nitrogen (N) cycling is not well understood. To examine how invasive species alter ecosystem N dynamics, we established monocultures of five phylogenetic pairs of native and non‐native invasive understory woody species common to Eastern U.S. forests. After 3 years, we found invaders increased N cycling by enhancing the flow of N to the soil through greater litter N production and litter N content, and increased the uptake of available soil N, via greater fine root production and specific root length. Synthesis. Our results highlight the importance of linking above‐ and below‐ground processes to better understand invader impacts on ecosystem nutrient processes. The rapid shifts in soil N processes as a result of invader dominance observed in our study suggest that invaders may be an important driver of forest ecosystem functioning.
Linguistic Patterns and Cross Modality-based Image Retrieval for Complex Queries

With the rising prevalence of social media, coupled with the ease of sharing images, people with specific needs and applications such as known item search, multimedia question answering, etc., have started searching for visual content, which is expressed in terms of complex queries. A complex query consists of multiple concepts and their attributes are arranged to convey semantics. It is less effective to answer such queries by simply appending the search results gathered from individual or subsets of concepts present in the query. In this paper, we propose to exploit the query constituents and relationships among them. The proposed approach finds image-query relevance by integrating three models - the linguistic pattern-based textual model, the visual model, and the cross modality model. We extract linguistic patterns from complex queries, gather their related crawled images, and assign relevance scores to images in the corpus. The relevance scores are then used to rank the images. We experiment on more than 140k images and compare the NDCG@n scores with the state-of-the-art image ranking methods for complex queries. Also, ranking of images obtained by our approach outperforms than that of obtained by a popular search engine.