Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Uncertainty in 1D Unidirectional Moving Target Selection

In contrast to the extensive studies on static target pointing, much less formal understanding of moving target acquisition can be found in the HCI literature. We designed a set of experiments to identify regularities in 1D unidirectional moving target selection, and found a Ternary-Gaussian model to be descriptive of the endpoint distribution in such tasks. The shape of the distribution as characterized by μ and σ in the Gaussian model were primarily determined by the speed and size of the moving target. The model fits the empirical data well with 0.95 and 0.94 R2 values for μ and σ , respectively. We also demonstrated two extensions of the model, including 1) predicting error rates in moving target selection; and 2) a novel interaction technique to implicitly aid moving target selection. By applying them in a game interface design, we observed good performances in both predicting error rates (e.g., 2.7% mean absolute error) and assisting moving target selection (e.g., 33% or a greater increase in pointing accuracy).
Interactions and Optimizations Analysis between Stiffness and Workspace of 3-UPU Robotic Mechanism

Abstract The interactions between stiffness and workspace performances are studied. The stiffness in x, y and z directions as well as the workspace of a 3-UPU mechanism are studied and optimized. The stiffness of the robotic system in every single moveable direction is measured and analyzed, and it is observed that in the case where one tries to make the x and y translational stiffness larger, the z directional stiffness will be reduced, i.e. the x and y translational stiffness contradicts with the one in z direction. Subsequently, the objective functions for the summation of the x and y translational stiffness and z directional stiffness are established and they are being optimized simultaneously. However, we later found that these two objectives are not in the same scale; a normalization of the objectives is thus taken into consideration. Meanwhile, the robotic system’s workspace is studied and optimized. Through comparing the stiffness landscape and the workspace volume landscape, it is also observed that the z translational stiffness shows the same changing tendency with the workspace volume’s changing tendency while the x and y translational stiffness shows the opposite changing tendency compared to the workspace volume’s. Via employing the Pareto front theory and differential evolution, the summation of the x and y translational stiffness and the volume of the workspace are being simultaneously optimized. Finally, the mechanism is employed to synthesize an exercise-walking machine for stroke patients.
Ulnar Nerve Complications After Ulnar Collateral Ligament Reconstruction of the Elbow: A Systematic Review

Background: While ulnar collateral ligament reconstruction (UCLR) of the elbow is an increasingly commonly performed procedure with excellent results reported in the published literature, less attention has been paid to specifically on the characterization of postoperative ulnar nerve complications, and it is unclear what operative strategies may influence the likelihood of these complications. Purpose: The purpose of this study is to examine the prevalence and type of ulnar nerve complications after UCLR of the elbow based on the entirety of previously published outcomes in the English literature. In addition, this study examined how the rate of ulnar nerve complications varied as a function of surgical exposures, graft fixation techniques, and ulnar nerve management strategies. Study Design: Systematic review and meta-analysis. Methods: A systematic review of the literature was completed using the MEDLINE, PubMed, and Ovid databases. UCLR case series that contained complications data were included. Ulnar neuropathy was defined as any symptoms or objective sensory and/or motor deficit(s) after surgery, including resolved transient symptoms. Meta-analysis of the pooled data was completed. Results: Seventeen articles (n = 1518 cases) met the inclusion criteria, all retrospective cohort studies. The mean prevalence of postoperative ulnar neuropathy was 12.0% overall after any UCLR procedure at a mean follow-up of 3.3 years, and 0.8% of cases required reoperation to address ulnar neuropathy. There were no cases of intraoperative ulnar nerve injury reported. The surgical approach associated with the highest rate of neuropathy was detachment of flexor pronator mass (FPM) (21.9%) versus muscle retraction (15.9%) and muscle splitting (3.9%). The fixation technique associated with the highest rate of neuropathy was the modified Jobe (16.9%) versus DANE TJ (9.1%), figure-of-8 (9.0%), interference screw (5.0%), docking technique (3.3%), hybrid suture anchor-bone tunnel (2.9%), and modified docking (2.5%). Concomitant ulnar nerve transposition was associated with a higher neuropathy rate (16.1%) compared with no handling of the ulnar nerve (3.9%). Among cases with concomitant transposition performed, submuscular transposition resulted in a higher rate of reoperation for ulnar neuropathy (12.7%) compared with subcutaneous transposition (0.0%). Conclusion: Despite a perception that UCLR has minimal morbidity, a review of all published literature revealed that 12.0% of UCLR surgeries result in postoperative ulnar nerve complications. UCLR techniques associated with the highest rates of neuropathy were detachment of the FPM, modified Jobe fixation, and concomitant ulnar nerve transposition, although it remains unclear whether there is a causal relationship between these factors and subsequent development of postoperative ulnar neuropathy due to limitations in the current body of published literature.
Social Influences on Executive Functioning in Autism: Design of a Mobile Gaming Platform

Most studies of executive function (EF) in Autism Spectrum Disorder (ASD) focus on cognitive information processing, emphasizing less the social interaction deficits core to ASD. We designed a mobile game that uses social and nonsocial stimuli to assess children's EF skills. The game comprised three components involving different EF skills: cognitive flexibility (shifting/inference), inhibitory control, and short-term memory. By recruiting 65 children with and without ASD to play the mobile game, we investigated the potential of such platforms for capturing important phenotypic characteristics of individuals with autism. Results highlighted between-diagnostic-group differences in playing patterns with children with ASD showing broad patterns of EF deficits, but with relative strengths in nonsocial short-term memory, and preserved response to emotional inhibition cues. We showed the system could predict IQ, an important target for clinical treatment, towards the goal of developing platforms to act as long-term, efficient, and effective behavioral biomarkers for ASD.
Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks

Distributed representation plays an important role in deep learning based natural language processing. However, the representation of a sentence often varies in different tasks, which is usually learned from scratch and suffers from the limited amounts of training data. In this paper, we claim that a good sentence representation should be invariant and can benefit the various subsequent tasks. To achieve this purpose, we propose a new scheme of information sharing for multi-task learning. More specifically, all tasks share the same sentence representation and each task can select the task-specific information from the shared sentence representation with attention mechanisms. The query vector of each task's attention could be either static parameters or generated dynamically. We conduct extensive experiments on 16 different text classification tasks, which demonstrate the benefits of our architecture. Source codes of this paper are available on Github.
Characterization and bioactive potentials of secondary metabolites from mollusks Crassostrea madrasensis and Amphioctopus marginatus

Abstract Chemical investigations of the ethyl acetate-methanol (EtOAc-MeOH) extract of the selected mollusks from the south-west coast of Arabian Sea led to the isolation of methyl-3-(26-phenylacetyloxy)-icosahydro-19-hydroxy-4,4,20-trimethylpicene-23-carboxylate (1) and methyl-3-(26-phenylacetyloxy)-icosahydro-1,19-dihydroxy-4,4,20-trimethylpicene-23-carboxylate (2) from Crassostrea madrasensis, whereas cholesta-5-en-3β-yl-(32-methyl-(30-((E)-34-amino-36-ethyl-39-oxohept-36-enyl)-pentanedioate (3) and 7-ethyl-9-vinyl-octahydroazuleno [1,8-bc]pyran-3,12-dione (4) were isolated from Amphioctopus marginatus. Their structures were assigned by detailed spectroscopic experiments. The studied compounds were checked for antioxidant and anti-inflammatory activities by various in vitro experiments. The radical quenching potential of 2,2-diphenyl-1-picrylhydrazyl and 2,2’-azino-bis-3-ethylbenzothiozoline-6-sulfonic acid diammonium salt were greater for 2 (IC50 0.85 and 1.10 mg/mL, respectively) than other studied compounds. The pro-inflammatory cyclooxygenase-2 inhibitory activity of 2 was greater (IC50 0.95 mg/mL) than those displayed by other studied compounds (>1.0 mg/mL). The 5-lipoxygenase inhibitory potential of 1 and 2 (~1.36 mg/mL) were greater than those displayed by 3 (1.64 mg/mL) and 4 (1.45 mg/mL).
WSe2 homojunctions and quantum dots created by patterned hydrogenation of epitaxial graphene substrates

Scanning tunneling microscopy (STM) at 5 K is used to study WSe2 layers grown on epitaxial graphene which is formed on Si-terminated SiC(0 0 0 1). Specifically, a partial hydrogenation process is applied to intercalate hydrogen at the SiC–graphene interface, yielding areas of quasi-free-standing bilayer graphene coexisting with bare monolayer graphene. We find that an abrupt and structurally perfect homojunction (band-edge offset ~0.25 eV) is formed when WSe2 overgrows a lateral junction between adjacent monolayer and quasi-free-standing bilayer areas in the graphene. The band structure modulation in the WSe2 overlayer arises from the varying work function (electrostatic potential) of the graphene beneath. Scanning tunneling spectroscopy measurements reveal that this effect can be also utilized to create WSe2 quantum dots that confine either valence or conduction band states, in agreement with first-principles band structure calculations.
Watch Me Play: Does Social Facilitation Apply to Digital Games?

The presence of observers and virtual characters can significantly shape our gaming experience. Researchers suppose that most of the basic socio-psychological phenomena are also applicable for digital games. However, the social processes in gaming setups can differ from our experience in other social situations. Our work emphasizes that awareness. Insights are needed for the purposeful design of a game's social setting, specifically in applied contexts of learning and training. Here, we focus on the social facilitation effect, which describes an unconscious change in performance due to the presence of others, by investigating the impact of real observers and virtual agents on player experience and performance in four different games. The results of our four studies show that, in contrast to previous assumptions, in-game success was not significantly influenced by the presence of any social entity, indicating that social facilitation does not generally apply to the context of playing digital games.
Dynamic magneto-viscoelastic model for magnetorheological nanocomposites with imperfect interface

A dynamic magneto-viscoelastic interface model is proposed to study the effective magneto-mechanical responses of magnetorheological nanocomposites filled with carbon nanotubes. It is incorporated with the fundamental micromechanics principles, microstructural magnetic and mechanical coupling, and computational homogenization procedures. The field-dependent effective dynamic stiffness and damping of randomly dispersed, chain-structured nanocomposites are investigated with the consideration of imperfect interfacial conditions among nanofillers, micro-particles and the matrix. Comparisons are performed between the model prediction and experimental data for a specific type of Fe particle-reinforced elastomer nanocomposites filled with multi-walled carbon nanotubes to demonstrate the capability of the proposed model framework.
The Burkholderia Genome Database: facilitating flexible queries and comparative analyses

Summary: As the genome sequences of multiple strains of a given bacterial species are obtained, more generalized bacterial genome databases may be complemented by databases that are focused on providing more information geared for a distinct bacterial phylogenetic group and its associated research community. The Burkholderia Genome Database represents a model for such a database, providing a powerful, user-friendly search and comparative analysis interface that contains features not found in other genome databases. It contains continually updated, curated and tracked information about Burkholderia cepacia complex genome annotations, plus other Burkholderia species genomes for comparison, providing a high-quality resource for its targeted cystic fibrosis research community. Availability: http://www.burkholderia. com. Source code: GNU GPL. Contact: brinkman@sfu.ca.
Multi-view Bi-clustering to Identify Smartphone Sensing Features Indicative of Depression

Depression is a major public health issue with direct and significant effects on both physical and mental health. In this study, we analyze smartphone sensing data to find differential behavioral features that are correlated with depression measures such as patient health questionnaire (PHQ-9). Our approach uses an innovative multi-view bi-clustering algorithm. It takes multiple views of sensing data as input to identify homogeneous behavioral groups and simultaneously the key sensing features that characterize the different groups. Using a publicly available dataset, we discover that these behavioral groups with differential sensing features are highly discriminative of PHQ-9 scores that are self reported by the study subjects. For instance, the group comprising less active users in the sensed activities corresponds to overall higher PHQ-9 scores. We then employ the key sensing features that distinguish the different groups to create predictive models to predict the group assignment of individuals. We verify the generalizability of these models using the support vector machine classifier. Cross validation studies show that our classifiers can classify individuals into the correct subgroups with an overall accuracy of 87%.
Feasibility of a Humor Training to Promote Humor and Decrease Stress in a Subclinical Sample: A Single-Arm Pilot Study

The present study investigates the feasibility of a humor training for a subclinical sample suffering from increased stress, depressiveness, or anxiety. Based on diagnostic interviews, 35 people were invited to participate in a 7-week humor training. Evaluation measures were filled in prior training, after training, and at a 1-month follow-up including humor related outcomes (coping humor and cheerfulness) and mental health-related outcomes (perceived stress, depressiveness, anxiety, and well-being). Outcomes were analyzed using repeated-measures ANOVAs. Within-group comparisons of intention-to-treat analysis showed main effects of time with large effect sizes on all outcomes. Post hoc tests showed medium to large effect sizes on all outcomes from pre to post and results remained stable until follow-up. Satisfaction with the training was high, attrition rate low (17.1%), and participants would highly recommend the training. Summarizing the results, the pilot study showed promising effects for people suffering from subclinical symptoms. All outcomes were positively influenced and showed stability over time. Humor trainings could be integrated more into mental health care as an innovative program to reduce stress whilst promoting also positive emotions. However, as this study was a single-arm pilot study, further research (including also randomized controlled trials) is still needed to evaluate the effects more profoundly.
HCI meets Material Science: A Literature Review of Morphing Materials for the Design of Shape-Changing Interfaces

With the proliferation of flexible displays and the advances in smart materials, it is now possible to create interactive devices that are not only flexible but can reconfigure into any shape on demand. Several Human Computer Interaction (HCI) and robotics researchers have started designing, prototyping and evaluating shape-changing devices, realising, however, that this vision still requires many engineering challenges to be addressed. On the material science front, we need breakthroughs in stable and accessible materials to create novel, proof-of-concept devices. On the interactive devices side, we require a deeper appreciation for the material properties and an understanding of how exploiting material properties can provide affordances that unleash the human interactive potential. While these challenges are interesting for the respective research fields, we believe that the true power of shape-changing devices can be magnified by bringing together these communities. In this paper we therefore present a review of advances made in shape-changing materials and discuss their applications within an HCI context.
A new binarization method for high accuracy handwritten digit recognition of slabs in steel companies

Handwritten digit recognition plays a crucial role in various automatic systems and industries nowadays. In steel companies, the code digits written on slab forehead should be recognized with high accuracy because these codes are used for determination of suitable thickness of steel slabs in rolling procedure. Because of special artifacts exist in these kinds of images such as color sagging, faint colors, and color separation, most of the common digit recognition algorithms have low accuracy in recognition of code digits. In this paper, a new two stage algorithm is proposed in order to reach high accuracy in determination of the handwritten codes written on slabs forehead and are corrupted by color sagging, faint colors and color separation. Applying our proposed algorithm on a dataset of images of slabs in Isfahan Steel Company shows that accuracy of the algorithm is 89.12% which is much higher than common and recently proposed algorithms.
Fast & Furious: Detecting Stress with a Car Steering Wheel

Stress affects the lives of millions of people every day. In-situ sensing could enable just-in-time stress management interventions. We present the first work to detect stress using the movements of a car's existing steering wheel. We extend prior work on PC peripherals and demonstrate that stress, expressed through muscle tension in the limbs, can be measured through the way we drive a car. We collected data in a driving simulator under controlled circumstances to vary the levels of induced stress, within subjects. We analyze angular displacement data to estimate coefficients related to muscle tension using an inverse filtering technique. We prove that the damped frequency of a mass spring damper model representing the arm is significantly higher during stress. Stress can be detected with only a few turns during driving. We validate these measures against a known stressor and calibrate our sensor against known stress measurements.
Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories

The first stage of every knowledge base question answering approach is to link entities in the input question. We investigate entity linking in the context of question answering task and present a jointly optimized neural architecture for entity mention detection and entity disambiguation that models the surrounding context on different levels of granularity. We use the Wikidata knowledge base and available question answering datasets to create benchmarks for entity linking on question answering data. Our approach outperforms the previous state-of-the-art system on this data, resulting in an average 8% improvement of the final score. We further demonstrate that our model delivers a strong performance across different entity categories.
Testing ontogenetic patterns of sexual size dimorphism against expectations of the expensive tissue hypothesis, an intraspecific example using oyster toadfish (Opsanus tau)

Trade-offs associated with sexual size dimorphism (SSD) are well documented across the Tree of Life. However, studies of SSD often do not consider potential investment trade-offs between metabolically expensive structures under sexual selection and other morphological modules. Based on the expectations of the expensive tissue hypothesis, investment in one metabolically expensive structure should come at the direct cost of investment in another. Here, we examine allometric trends in the ontogeny of oyster toadfish (Opsanus tau) to test whether investment in structures known to have been influenced by strong sexual selection conform to these expectations. Despite recovering clear changes in the ontogeny of a sexually selected trait between males and females, we find no evidence for predicted ontogenetic trade-offs with metabolically expensive organs. Our results are part of a growing body of work demonstrating that increased investment in one structure does not necessarily drive a wholesale loss of mass in one or more organs.
Diagnostic accuracy of clinical parameters to monitor peri‐implant conditions: A matched case‐control study

BACKGROUND The aim of this case-control study was to estimate the diagnostic accuracy of the standard clinical parameters in diagnosing healthy peri-implant tissues, peri-implant mucositis, and peri-implantitis. METHODS A case-control study was designed to compare the clinical parameters used in the diagnosis of peri-implant diseases such as: probingdepth (PD), bleeding on probing (BOP), mucosal redness (MR), suppuration (SUP), and plaque index (PI). Furthermore, the influence of patient- (sex, age) and implant-related variables (implant neck configuration, time in function after loading) were evaluated to investigate the association with the clinical findings. The inferential analysis consisted of estimation by generalized estimating equations (GEE) of multilevel logistic regression models. RESULTS In total, 1,572 sites were evaluated around 262 implants from 141 patients. Sites with implant mucositis showed significant levels of BOP (OR = 3.56), MR (OR = 7.66) and PD (OR = 1.48) compared to healthy sites. The specificity was 90.3% while the sensitivity was only 43.6%. Likewise, sites exhibiting peri-implantitis showed significant levels of BOP (OR = 2.32), MR (OR = 7.21), PD (OR = 2.43) and SUP (OR = 6.81) compared to healthy sites. Again, the multiple logistic regressions showed high specificity (92.1%) but modest sensitivity (52.5%). PD was the only diagnostic marker displaying significance comparing peri-implant mucositis and peri-implantitis sites (OR = 1.76). Moreover, tissue-level compared to bone-level implants were less associated with SUP+ (OR = 0.20), and PI (OR = 0.36) and demonstrated statistical significance. In addition, age, sex, and function time significantly influenced the tested clinical parameters. CONCLUSIONS The diagnosis of peri-implant diseases cannot rely solely upon individual clinical parameters but rather require a combination of criteria. The clinical parameters, particularly probing depth, might accurately discern between diagnoses among peri-implant conditions. Nevertheless, the specificity of the clinical parameters surpasses the sensitivity in the detection of peri-implant diseases, validating its potential use as a diagnostic tool.