Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Transitional Bottlenecks to Improve Learning in Nearest Sequence Memory Algorithm

Instance-based methods are proven tools to solve reinforcement learning problems with hidden states. Nearest Sequence Memory (NSM) is a widely known instance-based approach mainly based on k-Nearest Neighbor algorithm. It keeps the history of the agent in terms of action-observation-reward tuples and uses it to vote for the best upcoming action. In this work, an improving heuristic is proposed for the NSM algorithm which provides the agent an additional prior information, namely transitional bottlenecks, on the way to goal. Additionally, a tuple extension pattern is shown to further improve the heuristic by means of ambiguity reduction due to the nature of transitional bottlenecks, thus increase the learning speed. Empirical results indicate a significant improvement in learning performance, in terms of number of steps to goal.
Epidemiology of depression.

Depression is one of the most common psychiatric disorders and, from a societal perspective, is perhaps the most costly. Depression is also a highly recurrent disorder with an increasingly younger age of onset for the initial episode. In the six chapters in this part, the authors discuss issues concerning the onset and course of depression, its prevalence and societal costs, and important factors involved in studying this disorder. Kessler (Chapter 1) describes epidemiological aspects of depression: its prevalence and its economic cost. Boland and Keller (Chapter 2) discuss the course and outcome of this disorder, describing the results of several large-scale longitudinal investigations that have monitored the course of depression over many years. Nezu, Nezu, McClure, and Zwick (Chapter 3) describe the most widely used interview-based and self-report measures of depression, and discuss important issues involved in the assessment of this disorder. Extending this discussion, Ingram and Siegle (Chapter 4) describe a number of methodological issues in the study of depressive disorders, and make several noteworthy recommendations concerning how research in this area might proceed most fruitfully. Klein, Durbin, Shankman, and Santiago (Chapter 5) then describe the nature of the relation between depression and various aspects of personality functioning. Finally, Johnson and Kizer (Chapter 6) discuss similarities and differences in the clinical phenomenology and psychosocial predictors of unipolar and bipolar depression.
Evaluating Person Fit for Cognitive Diagnostic Assessment

Methods evaluating person fit for cognitive diagnostic assessment are an important area of research because failing to detect misfitting responses can lead to the misinterpretation of students’ attribute profiles, which may result in faulty remediation decisions. This article aims to examine ways of detecting person misfit for cognitive diagnostic assessments. The authors first investigated whether the well-known lz statistic, developed under the framework of item response theory, can be extended for use in the context of cognitive diagnostic models. The authors also introduce a new person fit statistic, response conformity index (RCI), developed for detecting misfitting response patterns for cognitive diagnostic assessments. The authors conduct both simulation and real data studies to compare the detection rates of lz and our new statistic.
Towards Energy Efficient Device-to-Device Content Dissemination in Cellular Networks

Considering that the future cellular networks are more content-centric, and content sharing among user equipments (UEs) is more universal, device-to-device (D2D) communication techniques become attractive for their applications to content delivery due to the characteristic of proximal transmissions without needs of high-rate backhaul. However, D2D content delivery may not be energy efficient due to shortage of useful contents for neighbor UEs when content caching and delivery are not well designed. In this paper, we propose an architecture using D2D multicast for content delivery in cellular networks and a practical methodology, which can indicate the time for delivery, the content to be disseminated, and the UEs to receive the delivery, to achieve more energy efficient content delivery for future green cellular networks.
Comparing Traditional and IRT Scoring of Forced-Choice Tests

This article explores how traditional scores obtained from different forced-choice (FC) formats relate to their true scores and item response theory (IRT) estimates. Three FC formats are considered from a block of items, and respondents are asked to (a) pick the item that describes them most (PICK), (b) choose the two items that describe them the most and the least (MOLE), or (c) rank all the items in the order of their descriptiveness of the respondents (RANK). The multi-unidimensional pairwise-preference (MUPP) model, which is extended to more than two items per block and different FC formats, is applied to obtain the responses to each item block. Traditional and IRT (i.e., expected a posteriori) scores are computed from each data set and compared. The aim is to clarify the conditions under which simpler traditional scoring procedures for FC formats may be used in place of the more appropriate IRT estimates for the purpose of inter-individual comparisons. Six independent variables are considered: response format, number of items per block, correlation between the dimensions, item discrimination level, and sign-heterogeneity and variability of item difficulty parameters. Results show that the RANK response format outperforms the other formats for both the IRT estimates and traditional scores, although it is only slightly better than the MOLE format. The highest correlations between true and traditional scores are found when the test has a large number of blocks, dimensions assessed are independent, items have high discrimination and highly dispersed location parameters, and the test contains blocks formed by positive and negative items.
Trajectory Optimization On Manifolds with Applications to SO(3) and R3XS2

Manifolds are used in almost all robotics applications even if they are not explicitly modeled. We propose a differential geometric approach for optimizing trajectories on a Riemannian manifold with obstacles. The optimization problem depends on a metric and collision function specific to a manifold. We then propose our Safe Corridor on Manifolds (SCM) method of computationally optimizing trajectories for robotics applications via a constrained optimization problem. Our method does not need equality constraints, which eliminates the need to project back to a feasible manifold during optimization. We then demonstrate how this algorithm works on an example problem on SO(3) and a perception-aware planning example for visualinertially guided robots navigating in 3 dimensions. Formulating field of view constraints naturally results in modeling with the manifold R × S which cannot be modeled as a Lie group.
Different structures formed at HII boundaries

Abstract Hydrodynamic simulations on the evolution of molecular clouds (MCs) at HII boundaries are used to show that radiation driven implosion (RDI) model can create almost all of the different morphological structures, such as a single bright-rimmed cloud (BRC), fragment structure and multiple elephant trunk (ET) structures.
Development of a SMA-Fishing-Line-McKibben Bending Actuator

High power-to-weight ratio soft artificial muscles are of overarching importance to enable inherently safer solutions to human–robot interaction. Traditional air-driven soft McKibben artificial muscles are linear actuators, and it is impossible for them to realize bending motions through use of a single muscle. More than two McKibben muscles are normally used to achieve bending or rotational motions, leading to heavier and larger systems. In addition, air-driven McKibben muscles are highly nonlinear in nature, making them difficult to be controlled precisely. An shape memory alloy (SMA)-fishing-line-McKibben (SFLM) bending actuator has been developed. This novel artificial actuator, made of an SMA-fishing-line muscle and a McKibben muscle, was able to produce the maximum output force of 3.0 N and the maximum bending angle (the rotation of the end face) of 61°. This may promote the application of individual McKibben muscles or SMA-fishing-line muscles alone. An output force control method for the SFLM is proposed, and based on MATLAB/Simulink software, an experiment platform is set up and the effectiveness of control system is verified through output force experiments. A three-fingered SFLM gripper driven by three SFLMs has been designed for a case study and for which the maximum carrying capacity is 650.4 ± 0.2 g.
Cortical Thickness and Metacognition in Cognitively Diverse Older Adults

Objective: Metacognition, or the ability to accurately identify, appraise, and monitor one’s deficits, is commonly impaired in Alzheimer’s disease (AD). Poor metacognition prevents correct appraisal of a range of physical, cognitive, and emotional symptoms and facilitates anosognosia, which has important clinical implications for individuals (e.g., diminished treatment adherence, increased engagement in high-risk situations) and caregivers (e.g., higher burden). However, the neural correlates of metacognitive disturbance are still debated in the literature, partly because of the subjective nature of traditional awareness measures. Method: An objective Feeling of Knowing (FOK) task was used to measure metamemory capacity in a group of cognitively diverse older adults, including 14 with mild to moderate AD and 20 cognitively healthy older adults. The association between three different objective metamemory measures of the FOK task and regional cortical thickness (12 bilateral regions of interest [ROIs] hypothesized to support self-awareness) was analyzed using partial correlations. Results: Less accurate metamemory at the local and global levels was associated with reduced right posterior cingulate cortical thickness, r = −0.42, p = . 02 and reduced right medial prefrontal, r = −0.39, p = . 029, respectively. Conclusions: To our knowledge, this was the first study to examine metacognition in relation to cortical thickness. Both global and local metamemory functions appear to rely on the integrity of right sided midline regions, known to be important for processing self-referential information. Findings are conceptualized with regard to the Default Mode Network, and also considered in relation to recent findings pointing to the right insula as a region critical for self-awareness.
Highly sensitive wearable strain sensors using copper nanowires and elastomers

One-dimensional nanomaterials show distinguishing characteristics in optical, electrical, mechanical, and thermal properties. Especially, the noble metal nanowires are good candidates for transparent conductive materials. In this paper, we coated copper nanowires (CuNWs) on PDMS as the flexible substrate and combined the conductive liquid to form a flexible wearable strain sensor. In electrical characterizations, the sensors show high sensitivity and tolerance to mechanical forces during multiple stretching, which are superior than the conventional devices with conducting polymers only. We further demonstrate various applications of the sensor in wearable electronic devices, which indicate good potential of such new devices in wearable electronics.
System Level CPS design using Non-Euclidean Training method

For the purpose of improving the design automoation of Cyber-Physical system from system level to detailed design stage. A novel non-euclidean training based method for categorizing functions from engineering data is proposed in this paper. In order to achieve the training task, a Structured Graph Convolutional Neural Network (SGCNN) is developed to perform graph invariant learning tasks at graph and subgraph level. The proposed method enables the first step toward the design automation of CPS at system level. The preliminary results show that we can achieve an accuracy of around 85% for machines to learn the functionality of a CPS.
SoGeM: Social Based Generative Model for Top-N Recommendation

Social recommendation which incorporates social information has attracted wide attention across both academia and industry for its superior performance. However, most existing approaches interpret social information in a heuristic manner which is not effective to capture the strong interplay between social connections and behaviors of users. This paper proposes SoGeM (SOcial based GEnerative Model) which simulates user's behaviors in a generative way and models intrinsic preferences and social influences of users simultaneously. Different from the most approaches that preassign similarity weights between friends, SoGeM learns the social influences automatically and quantitatively. Thus, the learnt influence has a probabilistic interpretation, for it is produced along with the generative process. We use Gibbs Sampling to train SoGeM and conduct comprehensive experiments on three real datasets. The results show that SoGeM outperforms other state-of-the-art approaches.
Current-Efficient Preamplifier Architecture for CMRR Sensitive Neural Recording Applications

There are neural recording applications in which the amplitude of common-mode interfering signals is several orders of magnitude higher than the amplitude of the signals of interest. This challenging situation for neural amplifiers occurs, among other applications, in neural recordings of weakly electric fish or nerve activity recordings made with cuff electrodes. This paper reports an integrated neural amplifier architecture targeting in-vivo recording of local field potentials and unitary signals from the brain stem of a weakly electric fish <italic>Gymnotus omarorum</italic>. The proposed architecture offers low noise, high common-mode rejection ratio (CMRR), current-efficiency, and a high-pass frequency fixed without MOS pseudoresistors. The main contributions of this work are the overall architecture coupled with an efficient and simple single-stage circuit for the amplifier main transconductor, and the ability of the amplifier to acquire biopotential signals from high-amplitude common-mode interference in an unshielded environment. A fully-integrated neural preamplifier, which performs well in line with the state-of-the-art of the field while providing enhanced CMRR performance, was fabricated in a 0.5 <inline-formula> <tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m CMOS process. Results from measurements show that the gain is 49.5 dB, the bandwidth ranges from 13 Hz to 9.8 kHz, the equivalent input noise is 1.88 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>V<inline-formula><tex-math notation="LaTeX"> $_{\text{rms}}$</tex-math></inline-formula>, the CMRR is 87 dB and the Noise Efficiency Factor is 2.1. In addition, in-vivo recordings of weakly electric fish neural activity performed by the proposed amplifier are introduced and favorably compared with those of a commercial laboratory instrumentation system.
An Immersive System with Multi-Modal Human-Computer Interaction

We introduce an immersive system prototype that integrates face, gesture and speech recognition techniques to support multi-modal human-computer interaction capability. Embedded in an indoor room setting, a multi-camera system is developed to monitor the user facial behavior, body gesture and spatial location in the room. A server that fuses different sensor inputs in a time-sensitive manner so that our system knows who is doing what at where in real-time. When correlating with speech input, the system can better understand the user intention for interaction purpose. We evaluate the performance of core recognition techniques on both benchmark and selfcollected datasets and demonstrate the benefit of the system in various use cases.
Detection of road vehicle's centre of gravity

The centre of gravity is an imaginary point in which the whole vehicle mass is concentrated. The point's coordinates affect the load of particular wheels of the vehicle and they are necessary to be known also when calculating the vehicle dynamics. The position of the centre of gravity has therefore an impact on the level of active vehicle safety. The position of this point is unstable and influenced by several factors. The article focuses on the methodology of the centre of gravity's measurement and on the change of its position depending on the vehicle occupation by persons and luggage. The introduction of the article describes the basic issues of the centre of gravity and its position. The main part further pays attention to the methodology of measuring the centre of gravity's position using the vehicle tensometric scales. The position of the centre of gravity is firstly measured with only 1 person sitting in the vehicle, then with 2, 3, and 4 persons and finally, it is measured with 5 persons sitting in the vehicle together with the luggage. The main contribution of the article is the measurement of the change of the centre of gravity's position depending on the vehicle occupation what can lead to the change of the vehicle running characteristics.
Nanopipettes as Monitoring Probes for the Single Living Cell: State of the Art and Future Directions in Molecular Biology

Examining the behavior of a single cell within its natural environment is valuable for understanding both the biological processes that control the function of cells and how injury or disease lead to pathological change of their function. Single-cell analysis can reveal information regarding the causes of genetic changes, and it can contribute to studies on the molecular basis of cell transformation and proliferation. By contrast, whole tissue biopsies can only yield information on a statistical average of several processes occurring in a population of different cells. Electrowetting within a nanopipette provides a nanobiopsy platform for the extraction of cellular material from single living cells. Additionally, functionalized nanopipette sensing probes can differentiate analytes based on their size, shape or charge density, making the technology uniquely suited to sensing changes in single-cell dynamics. In this review, we highlight the potential of nanopipette technology as a non-destructive analytical tool to monitor single living cells, with particular attention to integration into applications in molecular biology.
Comparison of Profilometric and Microindentation Analyses for Determining the Impact of Saliva on the Abrasion of Initially Eroded Enamel

The aim of this in vitro study was to investigate the impact of saliva on the abrasion of eroded enamel using two measuring methods. A total of 80 bovine enamel specimens from 20 bovine incisors were allocated to four experimental groups (n = 20 specimens per group). After baseline surface microhardness (SMH) measurements and profilometry all specimens were subjected to erosion (2 min, 1% citric acid, pH: 3.6, 37°C). SMH was determined again, and the depths of the Knoop indentations were calculated. Thereafter, specimens were incubated in human saliva (group 1 – no incubation/control, group 2 – 0.5 h, group 3 – 1 h, group 4 – 2 h) before toothbrush abrasion was performed. After final SMH measurements and profilometry, indentations were remeasured, and surface loss was calculated. SMH did not return to baseline values regardless of the length of saliva incubation. Further, an irreversible substance loss was observed for all specimens. With the indentation method, significantly (p < 0.05) more substance loss was found for controls (least square means ± standard error of 198 ± 19 nm) than for groups 2–4 (110 ± 10, 114 ± 11, and 105 ± 14 nm). Profilometric assessment showed significantly more substance loss for controls (122 ± 8 nm) than for group 4 (106 ± 5 nm). Intraclass correlation for interrater reliability between measurement methods was low (0.21, CI: 0.1–0.3), indicating poor agreement. Exposure of eroded enamel to saliva for up to 2 h could not re-establish the original SMH. The amount of measured substance loss depended on the measurement method applied.
Association between KIF1B (rs17401966) polymorphism and hepatocellular carcinoma susceptibility: a meta-analysis

Introduction The results of the earlier published studies on the association between KIF1B (rs17401966) polymorphism and hepatocellular carcinoma (HCC) risk are inconclusive. Hence, we performed this meta-analysis to evaluate the relationship between KIF1B (rs17401966) polymorphism and HCC risk. Methods Databases including PubMed, Web of Science and the Cochrane Library and bibliographies of relevant papers were screened to identify relevant studies published up to March 25, 2018. Pooled ORs and 95% CIs were calculated to evaluate the association. The subgroup analysis was conducted based on ethnicity, age, region and environment. A total of 19 studies from 11 eligible articles published from 2010 to 2016, with 8,741 cases and 10,812 controls, were included. Results The pooled results indicated that the association between KIF1B (rs17401966) polymorphism and the decreased HCC risk was significant. Subgroup analysis stratified by ethnicity showed the same association in Chinese, but not in non-Chinese population. When stratified by age, both old and young patients showed a decrease in HCC risk. When stratified by region, we detected the same association in Chinese in southern China. Similarly when stratified by environment, we observed the same association in Chinese in inland areas; however, no statistically significant association was observed in those in coastal areas. Conclusion This meta-analysis suggested that KIF1B (rs17401966) polymorphism could decrease HCC risk in Chinese and in overall population, but not in non-Chinese. This association remained significant in Chinese in southern China and inland areas, but not in those in northern and central China and coastal areas. Further large-scale multicenter studies are warranted to confirm these findings.
Remote influence of off-shore fish farm waste on Mediterranean seagrass 1 ( Posidonia oceanica ) meadows . 2 3 4 5 6

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Remote influence of off-shore fish farm waste on Mediterranean seagrass () meadows J.M. Ruiz, C. Marco-Méndez, J.L. Sánchez-Lizaso
Comparing the Two- and Three-Parameter Logistic Models via Likelihood Ratio Tests

Selection of an appropriate item response model is critical in the measurement of latent examinee ability. The one-, two-, and three-parameter logistic (1PL, 2PL, and 3PL) models are nested, and as such can be compared using likelihood ratio (LR) tests. The null hypothesis in the LR test for selection among the 2PL and 3PL models sets the guessing parameters to their lower bound of 0. This violates one of the assumptions of the LR test and renders the usual χ 2 reference distribution inappropriate for the comparison. A review of the current literature revealed that this problem is not well understood in the educational measurement field. Ignoring this issue can lead to selection of an overly simplified model, with implications for the ability estimates. In this article, the use of the LR test for item response model selection is investigated, with the goal of providing practitioners with an appropriate method of selecting the most parsimonious model. The results of simulation studies indicate the nature of the problem, with inaccurate Type I error rates for cases where the inappropriate null distribution was used. An analysis of data from a statewide mathematics test showed differences pertinent to subsequent analyses.