Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Assessment of the Cross-Task Mental Workload Using Physiological Measures During Anomaly Detection

The ability to detect anomalies in perceived stimuli is critical to a broad range of practical and applied activities involving human operators. In this paper, we propose a real-time physiological-based system to assess the cross-task mental workload during anomaly detection. Forty participants were recruited to detect anomalous images from a set of different distracting images (Task I) and abnormal activities from surveillance videos (Task II). In Task I, the task difficulty levels were manipulated by changing the number of anomalies/distracting stimuli (15, 21, 28, or 36) with and without time constraints (i.e., 4 × 2 = 8 task difficulty levels). Physiological and behavioral data from four task difficulty levels were divided into four categories according to subjective ratings of the mental workload. The support vector machine (SVM) classifiers were trained on these data to predict the mental workload categories of: 1) the same four task difficulty levels (within level); and 2) the other four task difficulty levels in Task I (cross level). Within-level classifications (with an average of 95.29%) were more accurate than cross-level classifications (average of 72.2%), which were much more accurate than random level classifications (25%). In Task II, the same participants monitored one, two, or four video clips simultaneously in accordance with three task difficulty levels. The same physiological signals were processed for real-time recognition of a participant's mental workload after he or she completed each activity detection task. The three-class SVM classifiers were trained on physiological data from Task I to predict the mental workload categories of the Task II (cross task), achieving an overall classification accuracy of 53.83%, compared to a 33.33% accuracy at random. These results are discussed in terms of their implications for developing situation-aware recognition systems of the mental workload and adaptive human–computer interaction platforms.
Hierarchical real-time scheduling in the multi-core era — An overview

With the accelerating pervasiveness of multi-core platforms in the embedded domains and the on-going need for more computational power and increased integration, multi-core scheduling for real-time and mixed-critical applications is an active research topic. In this paper, we give an overview on the history and the current state-of-the-art on multi-core real-time scheduling. A special focus is put on shared resource access protocols and hierarchical scheduling approaches, both of which are increasingly important due to the higher spatial integration and stronger coupling between the different subsystems, both on the application and on the multi-core architectural level. Moreover, hierarchical scheduling is a promising approach in the area of mixed-criticality systems to enable composability and segregation, which is needed to cope with the complexity of such systems. This survey will be of interest to researchers and practitioners in the field of real-time scheduling for multi-core systems.
A minimax dictionary expansion for sparse continuous reconstruction

The concept of dictionary expansion has been applied in inverse problems as a means to overcome a problem known as off-grid deviation. Within this framework and under the assumption that the off-grid deviations obey an uniform distribution, we propose a minimax error criterion to build expanded dictionaries. To this end, we formulate the problem as a polynomial regression and cast it as a second-order cone program. A robust method for the recovery of continuous time shifts and amplitudes from reconstructed expanded coefficients is also presented. Empirical results with a greedy algorithm and a convex optimization algorithm, both conceived to work with expanded dictionaries, show that the proposed expanded basis provides accurate reconstruction of continuous-time located events in the presence of noise.
Perception of healthy and unhealthy food among Chinese adolescents

Purpose The purpose of this paper is to explore snacking behavior and perspectives on healthy and unhealthy food choices among adolescents in Mainland China. Design/methodology/approach Four focus-group interviews were conducted. Altogether 24 participants were recruited in Changsha, a second-tier city in China, through a convenience sampling process. They were asked to report their snacking behaviors, identify whether certain snacks are healthy or unhealthy and elaborate on factors affecting food choices. Findings Snacking was prevalent among the participants. The most frequently consumed snacks included fruit, milk and instant noodles. Participants’ evaluations for the healthiness of foods were based on the actual nutritional values of those foods, the effects on growth and body weight and word-of-mouth. Choice of snack was driven mainly by taste, image, convenience and health consciousness. Research limitations/implications The finding was based on a non-probability sample. The paper also did not explore the contexts where snacks were consumed. Practical implications Parents can make healthy snacks more accessible at home and at schools. Educators can teach adolescents how to read food labels. Schools can increase the availability of healthy snacks on campus. Social marketers can promote healthy snacks by associating them with fun and high taste. Originality/value This is the first paper on snacking behaviors among adolescents conducted in a second-tier city in China using focus-group methodology.
Computational Modeling of Domain Switching Effects in Piezoceramic Materials - A Micro-Macro Mechanical Approach

Within the last decades, a remarkable development in materials science took place – nowadays, materials are not only constructed for the use of inert structures but rather designed for certain predefined functions. This innovation was accompanied with the appearance of smart materials with reliable recognition, discrimination and capability of action as well as reaction. In recent years, piezoelectric and ferroelectric materials such as BaTiO3, PZT, PLZT ceramics have widely been used for the design of smart materials and intelligent systems e.g., as transducers for sonar applications and actuator applications including vibration suppression, structural deformation, and noise control. Even though ferroelectric materials serve smartly in real applications, they also possess several restrictions at high performance usage. The behavior of these materials is almost linear under the action of low electric fields or low mechanical stresses, but exhibits strong nonlinear response under high electric fields or mechanical stresses. High electromechanical loading conditions result in a change of the spontaneous polarization direction with respect to individual domains, which is commonly referred to as domain switching. This phase transformation apparently also causes a change in mechanical strains. Domain switching in polyand single-crystals, or rather grains, is in general not homogeneous since the domain walls, which separate different phases, move through the crystal. Moreover, domain switching is identified as the main reason for the overall nonlinear behavior of piezoelectric materials. In order to better understand the nonlinear behavior of piezoelectric materials, many experiments have been performed and reported in the literature. Experiments are usually carried out in the bulk ceramic specimen so that the macroscopic electrical displacements and strains are commonly monitored. The mathematical modeling of piezoelectric materials can be separated into two major approaches: One of these modeling concepts refers to macroscopic or phenomenological formulations. Phenomenological models are commonly based on a chosen set of state variables and correlated functions connecting them as, e.g., particular representations of the free energy function. Further relations as for instance evolution equations are derived from established thermodynamic concepts. The other approach refers to micromechanical modeling concepts so that certain substructures of the material are directly taken into account, i.e. specific physically motivated relations are promptly incorporated in order to predict the material behavior. The aim of the present work is to develop a three-dimensional coupled finite element model,
Cubical Type Theory: A Constructive Interpretation of the Univalence Axiom

This paper presents a type theory in which it is possible to directly manipulate n-dimensional cubes (points, lines, squares, cubes, etc.) based on an interpretation of dependent type theory in a cubical set model. This enables new ways to reason about identity types, for instance, function extensionality is directly provable in the system. Further, Voevodsky's univalence axiom is provable in this system. We also explain an extension with some higher inductive types like the circle and propositional truncation. Finally we provide semantics for this cubical type theory in a constructive meta-theory.
Childhood-Diagnosed ADHD, Symptom Progression, and Reversal Learning in Adulthood

Objective: ADHD persists in up to 60% into adulthood, and the reasons for persistence are not fully understood. The objective of this study was to characterize the neurofunctional basis of decision making in those with a childhood diagnosis of ADHD with either persistent or remitted symptoms in adulthood versus healthy control participants. Method: Thirty-two adults diagnosed with ADHD as children were split into persistent (n = 18) or remitted (n = 14) ADHD groups. Their neural activity and neurofunctional connectivity during a probabilistic reversal learning task were compared with 32 healthy controls. Results: Remitters showed significantly higher neural connectivity in final reversal error and probabilistic error conditions, and persisters depict higher neural connectivity in reversal errors than controls at a family-wise error (FWE) corrected whole-brain corrected threshold. Conclusion: Remitters may have utilized higher neural connectivity than controls to make successful decisions. Also, remitters may have utilized compensatory strategies to override any potential underlying ADHD deficits.
The why and how of DNA unlinking

The nucleotide sequence of DNA is the repository of hereditary information. Yet, it is now clear that the DNA itself plays an active role in regulating the ability of the cell to extract its information. Basic biological processes, including control of gene transcription, faithful DNA replication and segregation, maintenance of the genome and cellular differentiation are subject to the conformational and topological properties of DNA in addition to the regulation imparted by the sequence itself. How do these DNA features manifest such striking effects and how does the cell regulate them? In this review, we describe how misregulation of DNA topology can lead to cellular dysfunction. We then address how cells prevent these topological problems. We close with a discussion on recent theoretical advances indicating that the topological problems, themselves, can provide the cues necessary for their resolution by type-2 topoisomerases.
QUBE: a quick algorithm for updating betweenness centrality

The betweenness centrality of a vertex in a graph is a measure for the participation of the vertex in the shortest paths in the graph. The Betweenness centrality is widely used in network analyses. Especially in a social network, the recursive computation of the betweenness centralities of vertices is performed for the community detection and finding the influential user in the network. Since a social network graph is frequently updated, it is necessary to update the betweenness centrality efficiently. When a graph is changed, the betweenness centralities of all the vertices should be recomputed from scratch using all the vertices in the graph. To the best of our knowledge, this is the first work that proposes an efficient algorithm which handles the update of the betweenness centralities of vertices in a graph. In this paper, we propose a method that efficiently reduces the search space by finding a candidate set of vertices whose betweenness centralities can be updated and computes their betweenness centeralities using candidate vertices only. As the cost of calculating the betweenness centrality mainly depends on the number of vertices to be considered, the proposed algorithm significantly reduces the cost of calculation. The proposed algorithm allows the transformation of an existing algorithm which does not consider the graph update. Experimental results on large real datasets show that the proposed algorithm speeds up the existing algorithm 2 to 2418 times depending on the dataset.
A Contribution of Mouse Dendritic Cell–Derived IL-2 for NK Cell Activation

Dendritic cells (DCs) play a predominant role in activation of natural killer (NK) cells that exert their functions against pathogen-infected and tumor cells. Here, we used a murine model to investigate the molecular mechanisms responsible for this process. Two soluble molecules produced by bacterially activated myeloid DCs are required for optimal priming of NK cells. Type I interferons (IFNs) promote the cytotoxic functions of NK cells. IL-2 is necessary both in vitro and in vivo for the efficient production of IFNγ, which has an important antimetastatic and antibacterial function. These findings provide new information about the mechanisms that mediate DC–NK cell interactions and define a novel and fundamental role for IL-2 in innate immunity.
A mobile sensor network based road surface monitoring system

This paper presents a road surface defect identification system based on 3D accelerometers, GPS and video modules deployed on vehicles. The mobile platform architecture and the central data aggregation algorithm are also discussed. Because the mobile system is deployed over a large outdoor area, we also present a solution for the wireless communication coverage problem. Finally, we are highlighting the importance of the gathered information by making it available for the users using a GIS platform.
The Prevalence of Vitamin D Deficiency and the Determinants of 25(OH)D Concentration in Older Irish Adults: Data From The Irish Longitudinal Study on Ageing (TILDA)

Background Few data are available examining the determinants of vitamin D status exclusively in older adults. We aimed to investigate the prevalence and determinants of vitamin D deficiency in a representative sample of the older Irish population (aged 50-98 years). Methods The concentration of 25-hydroxyvitamin D (25(OH)D) was measured in 5,356 community-dwelling older Irish adults from The Irish Longitudinal Study on Ageing (TILDA). Detailed demographic, geographic, lifestyle, and socioeconomic factors were assessed by questionnaire. Proportions of deficiency prevalence were generated by season sampled. Linear regression was used to investigate the association between 25(OH)D concentration and reported risk factors. Results The prevalence of deficiency (25(OH)D < 30 nmol/L) was 13.1% (95% CI: 12.1-14.2). Deficiency status was more prevalent in nonsupplement users, in winter, in smokers, in obese adults, the physically inactive, those living alone, and in the oldest old (>80 years). The main predictors (p < .05) of 25(OH)D concentration were supplement use (coefficient nmol/L: 27.2 [95% CI: 15.3-39.2]), smoking (-8.9 [-12.6--5.2]), summer season (5.9 [2.7-9.1]), and obesity (-4.0 [-6.3--1.7]). Conclusion Vitamin D deficiency is common among older Irish adults. These data indicate the need for targeted strategies within sections of the older population to improve vitamin D status.
Tidal Flushing Restores the Physiological Condition of Fish Residing in Degraded Salt Marshes

Roads, bridges, and dikes constructed across salt marshes can restrict tidal flow, degrade habitat quality for nekton, and facilitate invasion by non-native plants including Phragmites australis. Introduced P. australis contributes to marsh accretion and eliminates marsh surface pools thereby adversely affecting fish by reducing access to intertidal habitats essential for feeding, reproduction, and refuge. Our study assessed the condition of resident fish populations (Fundulus heteroclitus) at four tidally restricted and four tidally restored marshes in New England invaded by P. australis relative to adjacent reference salt marshes. We used physiological and morphological indicators of fish condition, including proximate body composition (% lipid, % lean dry, % water), recent daily growth rate, age class distributions, parasite prevalence, female gravidity status, length-weight regressions, and a common morphological indicator (Fulton’s K) to assess impacts to fish health. We detected a significant increase in the quantity of parasites infecting fish in tidally restricted marshes but not in those where tidal flow was restored to reduce P. australis cover. Using fish length as a covariate, we found that unparasitized, non-gravid F. heteroclitus in tidally restricted marshes had significantly reduced lipid reserves and increased lean dry (structural) mass relative to fish residing in reference marshes. Fish in tidally restored marshes were equivalent across all metrics relative to those in reference marshes indicating that habitat quality was restored via increased tidal flushing. Reference marshes adjacent to tidally restored sites contained the highest abundance of young fish (ages 0–1) while tidally restricted marshes contained the lowest. Results indicate that F. heteroclitus residing in physically and hydrologically altered marshes are at a disadvantage relative to fish in reference marshes but the effects can be reversed through ecological restoration.
Initial experiences with grid-based volume visualization of fluid flow simulations on PC clusters

Over the last 18 months, our team at the Laboratory for Computational Science & Engineering (LCSE) at the University of Minnesota has been moving our data analysis and visualization applications from small clusters of PCs within our lab to a Grid-based approach using multiple PC clusters with dynamically varying availability. Under support from an NSF CISE Research Resources grant, we have outfitted 52 Dell PCs in a student lab in our building that is operated by the University's Academic and Distributed Computing Services (ADCS) organization. This PC cluster supplements another PC cluster of 10 machines in our lab. As the students gradually leave this ADCS lab after 10 PM, the PCs are rebooted into an operating system image that sees the 400 GB disk subsystems we have installed on them and communicates with a central, 32-processor Unisys ES-7000 machine in our lab. The ES-7000 hosts databases that coordinate the work of these 52 PCs along with that of 10 additional Dell PCs in our lab that drive our PowerWall display. This equipment forms a local Grid that we coordinate to analyze and visualize data generated on remote clusters at NCSA. The PCs of the student lab offer a 20 TB pool of disk storage for our simulation data as well as a large movie rendering capability with their Nvidia graphics engines. However, these machines do not become available to us in force until after about 1 AM. This fact has forced us to automate our visualization process to an unusual degree. It has also forced us to address problems of security and run error diagnosis that we could easily avoid in a more standard environment. In this paper we report our methods of addressing these challenges and describe the software tools that we have developed and made available for this purpose on our Web site, www.lcse. umn.edu. We also report our experience in using this system to visualize 1.4 TB of vorticity volumetric data from a recent simulation of homogeneous, compressible turbulence with our PPM code. This code was run on the NSF TeraGrid cluster at NCSA, and the data was transported to our lab at 8 MB/sec over the Internet using the same ipRIO software that we developed to move this data around within our own environment. The move visualizations generated on the ADCS PCs overnight are viewed on the LCSE 10-panel, 13 Mpixel PowerWall the next day. Smaller trial movies can be generated on the small PC cluster in our lab before submitting an overnight large movie request.
GestKeyboard: enabling gesture-based interaction on ordinary physical keyboard

Stroke gestures are intuitive and efficient but often require gesture-capable input hardware such as a touchscreen. In this paper, we present GestKeyboard, a novel technique for gesturing over an ordinary, unmodified physical keyboard that remains the major input modality for existing desktop and laptop computers. We discuss an exploratory study for understanding the design space of gesturing on a physical keyboard and our algorithms for detecting gestures in a modeless way, without interfering with the keyboard's major functionality such as text entry and shortcuts activation. We explored various features for detecting gestures from a keyboard event stream. Our experiment based on the data collected from 10 participants indicated it is feasible to reliably detect gestures from normal keyboard use, 95% detection accuracy within a maximum latency of 200ms.
Loss Factor and Impedance Analysis of Warm Components of BERLinPro

The ongoing component design for the HZB 50 MeV, 100 mA ERL project bERLinPro is accompanied by loss factor and impedance computations. A list of accelerator components that are likely to induce geometric wake fields including bellows, collimators, tapers, shutter valves etc. is given. Loss factors and impedance spectra, both calculated using CST-ParticleStudio®, are presented. Scaling of the loss factors with respect to the bunch length is calculated on base of the numerical simulations and is used to extrapolate down to a rms bunch length of 0.6 mm, which is hard to reach directly in numerical simulations.
The prostate metastasis suppressor gene NDRG1 differentially regulates cell motility and invasion

Experimental and clinical evidence suggests that N‐myc downregulated gene 1 (NDRG1) functions as a suppressor of prostate cancer metastasis. Elucidating pathways that drive survival and invasiveness of NDRG1‐deficient prostate cancer cells can help in designing therapeutics to target metastatic prostate cancer cells. However, the molecular mechanisms that lead NDRG1‐deficient prostate cancer cells to increased invasiveness remain largely unknown. In this study, we demonstrate that NDRG1‐deficient prostate tumors have decreased integrin expression and reduced cell adhesion and motility. Our data indicate that loss of NDRG1 differentially affects Rho GTPases. Specifically, there is a downregulation of active RhoA and Rac1 GTPases with a concomitant upregulation of active Cdc42 in NDRG1‐deficient cells. Live cell imaging using a fluorescent sensor that binds to polymerized actin revealed that NDRG1‐deficient cells have restricted actin dynamics, thereby affecting cell migration. These cellular and molecular characteristics are in sharp contrast to what is expected after loss of a metastasis suppressor. We further demonstrate that NDRG1‐deficient cells have increased resistance to anoikis and increased invasiveness which is independent of its elevated Cdc42 activity. Furthermore, NDRG1 regulates expression and glycosylation of EMMPRIN, a master regulator of matrix metalloproteases. NDRG1 deficiency leads to an increase in EMMPRIN expression with a concomitant increase in matrix metalloproteases and thus invadopodial activity. Using a three‐dimensional invasion assay and an in vivo metastasis assay for human prostate xenografts, we demonstrate that NDRG1‐deficient prostate cancer cells exhibit a collective invasion phenotype and are highly invasive. Thus, our findings provide novel insights suggesting that loss of NDRG1 leads to a decrease in actin‐mediated cellular motility but an increase in cellular invasion, resulting in increased tumor dissemination which positively impacts metastatic outcome.