Miracle-dz/UniFAR-FTUs huggingface dataset:
To Wish Impossible Things: On the Ontological Status of Latent Variables and the Prospects for Theory in Psychology

In this commentary, we argue that Fried's article, "Lack of theory building and testing impedes progress in the factor and network literature," provides a number of insights that will be very useful for psychologists, but also that he is wishing for psychology to focus on a kind of theory that it cannot generally be expected to produce at this time. As we will explain, Fried asks too much of our models and our theories. In part, this stems from a failure to consistently heed his own warnings about the limitations of statistical models, but it also reflects an overestimation of the kind of evidence and reasoning necessary to develop and test theory. We too believe that psychological research on individual differences should pay attention to theory, but we are more optimistic than Fried about the progress that can be made through a focus on the kind of theory that psychology is actually capable of producing. In order to justify our optimism, we first develop our perspective on the nature of latent variables and then consider how individual-difference researchers should approach theory that has the potential to explain them.
An Empirical Evaluation of Coverage Criteria for FBD Simulation Using Mutation Analysis

SUMMARY Two structural coverage criteria, toggle coverage and modiﬁed condition / decision coverage, for FBD (Function Block Diagram) simulation are proposed in the previous study. This paper empirically evaluates how e ﬀ ective the coverage criteria are to detect faults in an FBD program using the mutation analysis. key words: coverage adequacy criteria, FBD simulation, mutation analysis
Multi-Category Image Super-Resolution with Convolutional Neural Network and Multi-Task Learning

This paper presents an image super-resolution technique using a convolutional neural network (CNN) and multi-task learning for multiple image categories. The image categories include natural, manga, and text images. Their features differ from each other. However, several CNNs for super-resolution are trained with a single category. If the input image category is different from that of the training images, the performance of super-resolution is degraded. There are two possible solutions to manage multi-categories with conventional CNNs. The first involves the preparation of the CNNs for every category. This solution, however, requires a category classifier to select an appropriate CNN. The second is to learn all categories with a single CNN. In this solution, the CNN cannot optimize its internal behavior for each category. Therefore, this paper presents a super-resolution CNN architecture for multiple image categories. The proposed CNN has two parallel outputs for a high-resolution image and a category label. The main CNN for the high-resolution image is a normal three convolutional layer-architecture, and the sub neural network for the category label is branched out from its middle layer and consists of two fully-connected layers. This architecture can simultaneously learn the high-resolution image and its category using multi-task learning. The category information is used for optimizing the super-resolution. In an applied setting, the proposed CNN can automatically estimate the input image category and change the internal behavior. Experimental results of 2× image magnification have shown that the average peak signal-to-noise ratio for the proposed method is approximately 0.22 dB higher than that for the conventional super-resolution with no difference in processing time and parameters. We have ensured that the proposed method is useful when the input image category is varying. key words: super-resolution, resolution enhancement, convolutional neural network, multi-task learning, deep learning
Otherware

people perceive these as counterparts, not as extensions of themselves. Sometimes this is by accident, because one cannot understand the complex, opaque reasoning of a deep-learning algorithm. Sometimes it is by design, as in the case of an anthropomorphic social robot. Either way, it creates a fundamental shift from an embodied relationship with technology to one of alterity: Technology becomes other [3]. We call this class of interactive systems otherware (https://otherware.net). And, so far, HCI has a very limited idea of how people should best interact with it. Otherware implies the application of social metaphors to human-technology interaction: We converse with robots or voice assistants; we cooperate, delegate, From the command line to today’s immersive, tangible, or gesture-based interaction, the evolution of interaction paradigms is a story of increasing embodiment [1]. We predominantly design interactive systems as tools, which withdraw from conscious thought to be always ready-at-hand. In this paradigm, people are at the center of action and use ubiquitous “everyware” technology [2] to extend their minds and bodies. But this paradigm is more and more at odds with what is happening in technology today. New self-learning, proactive, AI-infused systems are no longer tools to act through. When encountering personal assistants, chatbots, smart home devices, selfdriving cars, or autonomous robots, F Insights → People perceive chatbots or robots as counterparts rather than as extensions of themselves. → Interaction paradigms for such "otherware" often mimic humans or pets. This creates many problems as well as overlooks specific potentials of social machines—their superpowers. → Alternative interaction paradigms should acknowledge otherware’s social essence while emphasizing its nonhuman nature.
Presenting Walking Route for VR Zombie

SUMMARY The aim of this study is to calculate optimal walking routes in real space for users partaking in immersive virtual reality (VR) games without compromising their immersion. To this end, we propose a navigation system to automatically determine the route to be taken by a VR user to avoid collisions with surrounding obstacles. The proposed method is evaluated by simulating a real environment. It is veriﬁed to be capable of calculating and displaying walking routes to safely guide users to their destinations without compromising their VR immersion. In addition, while walking in real space while experiencing VR content, users can choose between 6-DoF (six degrees of freedom) and 3-DoF (three degrees of freedom). However, we expect users to prefer 3-DoF conditions, as they tend to walk longer while using VR content. In dynamic situations, when two pedestrians are added to a designated computer-generated real environment, it is necessary to calculate the walking route using moving body prediction and display the moving body in virtual space to preserve immersion.
Optimization of a Reverse Osmosis System Using Genetic Algorithm

Abstract Reverse Osmosis (RO) has found extensive application in industry as a highly efficient separation process. In most cases, it is required to select the optimum set of operating variables such that the performance of the system is maximized. In this work, an attempt has been made to optimize the performance of RO system with a cellulose acetate membrane to separate NaCl‐Water system using Genetic Algorithm (GA). The GAs are faster and more efficient than conventional gradient based optimization techniques. The optimization problem was to maximize the observed rejection of the solute by varying the feed flowrate and overall permeate flux across the membrane for a constant feed concentration. To model the system, a well‐established transport model for RO system, the Spiegler‐Kedem model was used. It was found that the GA converged rapidly to the optimal solution at the 8th generation. The effect of varying GA parameters like size of population, crossover probability, and mutation probability on the result was also studied. The algorithm converged to the optimum solution set at the 8th generation. It was also seen that varying the computational parameters significantly affected the results.
Polyurethane Films for Wound Dressing Applications

Polyurethane films based on renewable sources were prepared as an alternative to petroleum-based polyurethane. The effects of the structure and ratio of diisocyanate components on polymer properties were investigated. Hexamethylene diisocyanate (HMDI) and diphenylmethylene diisocyanate (MDI) were chosen as the diisocyanate components in polymer synthesis. Membranes were prepared by casting-evaporation technique and characterized for their gas permeability, film and mechanical properties. The oil-based membranes were found to be suitable for wound dressing applications.
First‐principles molecular dynamics simulations of models for the myoglobin active center

A quantitative study of the interplay between the structure, energy, and dynamics of heme models is presented. Our calculations are based on density functional theory (DFT) combined with molecular dynamics (MD), within the Car–Parrinello scheme. The systems analyzed are the five-coordinated FeP–AB (FeP = iron–porphyrin, AB = CO, NO, O2) and Fe(Heme)–O2, the six-coordinated FeP(Im)–AB (Im = imidazole), and the picket fence–oxygen complex, Fe(TpivP)(2me–Im)–O2. Starting from the optimized structures of these systems, we analyze the trends in their ligand binding properties. Our calculations show the peculiar trans repulsive effect of the NO ligand when binding to iron–porphyrin (it elongates and weakens the trans axial ligand bond), in contrast with the synergistic effect of CO and O2. Energy variations associated to the rotation of the O2 molecule around the Fe–O bond in Fe(TpivP)(2me–Im)–O2 are analyzed, in comparison with that of a simpler FeP(Im)–O2 model. A small energy barrier (<2 kcal/mol) is found in both cases. Room temperature molecular dynamics simulations reveal how the O2 ligand overcomes this barrier at room temperature: It undergoes large-amplitude oscillations within one porphyrin quadrant, jumping to another one in the picosecond timescale. In contrast, the CO dynamics is characterized by small, albeit extremely complex, displacements around its equilibrium position. Small fluctuations of the FeCO tilt (δ) and bend (θ) angles (δ≤8°, θ≤13°) are observed. © 2000 John Wiley & Sons, Inc. Int J Quant Chem 80: 1172–1180, 2000
Upwelling in the liquid region surrounding the keyhole in penetration welding with a laser

In penetration welding with a laser, the pressure in the keyhole is in excess at atmospheric pressure. A pressure gradient related to this is produced in the liquid region surrounding it, with the result that there is a flow parallel to the axis of the laser. The velocity of this flow is found as a function of the material constants, and the volume flow rate calculated. From this it is possible to construct an estimate of the elevation or depression of the surface of the weld. the shape of the surface cross section is discussed, and some deductions made about the pressure distribution in the liquid metal.
High rate anaerobic filter with floating supports for the treatment of effluents from small-scale agro-food industries

The performance of four laboratory-scale upflow anaerobic filters (UAFs) of about 10 L effective volume packed with small buoying polyethylene media was investigated for the treatment of wastewater discharged from various small-scale agro-food industries with different composition and concentrations viz. synthetically prepared low strength (˜1.9 g CODt/L), fruit canning (˜10 g CODt/L), winery (˜20 g CODt/L) and cheese-dairy (˜30 g CODt/L) wastewaters. For the low strength wastewater, HRT was the limiting parameter with a minimum of 4 h corresponding to a maximum OLR of 12 g COD/L.d. For the high concentrated substrates, OLR was the limiting parameter, which was influenced by the nature of the substrates treated and not by the concentration of the substrates. Indeed, the UAF treating winery wastewater (20 g CODt/L) had reached the highest OLR of 27 g COD/L.d with 80% COD removal efficiency, while that of the other two reactors treating fruit canning and cheese-dairy wastewaters were comparatively lower (19.
Development of a Novel Carbon-Fiber Mono-Lateral External Fixator

This paper reports the design of an innovative mono-lateral external fixator made of carbon fiber composite materials. The designed system can be easily assembled in comparison with commercial fixators and follows orthopedic requirements with sufficient stability and stiffness. The change of operation mode between distraction and fixation is achieved with a wedge that blocks axial translation in one position, while allows sliding with a 90º rotation. The prototypes were produced by the method of molding by compaction. A mold was developed for each part; the rail, the clamp and the cover. Each mold consisted of a cavity that gave form to the piece and a piston that exerted pressure on the composite. Mechanical tests were performed to determine the stiffness under axial compression, and anteroposterior and mediolateral bending. For comparison, tests were also performed on two Orthofix commercial systems, one with the rail made of carbon fiber and the other with an aluminum rail. The axial compression, anteroposterior and mediolateral bending stiffness of the developed system were 200.7, 13.4 and 87.0 N/mm, respectively, which were 38%, 35% and 27% lower than those obtained for the Orthofix system. However, these values were in the range of other similar systems reported in the literature. Therefore, the developed system presented promising results and may be clinically evaluated.
DeepFake Videos Detection Using Machine Learning

With the advancement of technology and machine learning the feasibility for people to produce fake videos and spread fake news is becoming more and more easy especially with the creation of GAN models which are capable of producing accurate enough fake videos. The main agenda of this project is to help detect these fake videos from real using machine learning techniques. In this project the data from faceforencis was considered and frames are generated from videos followed by application of novel technique in classiﬁcation by using face detection over videos. These images are transformed using various parameters and models are compared against them. Highest accuracy of 95% with AUC value of 0.95 is obtained using laplace transformed images. Quantum machine learning is also applied and was able to train the model with 50% less time than classical model.
Mechanistic Understanding of Translational Pharmacokinetic-Pharmacodynamic Relationships in Nonclinical Tumor Models: A Case Study of Orally Available Novel Inhibitors of Anaplastic Lymphoma Kinase

The orally available novel small molecules PF06463922 [(10R)-7-amino-12-fluoro-2,10,16-trimethyl-15-oxo-10,15,16,17-tetrahydro-2H-8,4-(metheno)pyrazolo[4,3-h][2,5,11]benzoxadiazacyclotetradecine-3-carbonitrile] and PF06471402 [(10R)-7-amino-12-fluoro-2,10,16-trimethyl-15-oxo-10,15,16,17-tetrahydro-2H-8,4-(azeno)pyrazolo[4,3-h][2,5,11]benzoxadiazacyclo-tetradecine-3-carbonitrile] are second-generation anaplastic lymphoma kinase (ALK) inhibitors targeted to both naïve and resistant patients with non–small cell lung cancer (NSCLC) to the first-generation ALK inhibitor, crizotinib. The objectives of the present study were to characterize and compare the pharmacokinetic-pharmacodynamic (PKPD) relationships of PF06463922 and PF06471402 for target modulation in tumor and antitumor efficacy in athymic mice implanted with H3122 NSCLC cells expressing a crizotinib-resistant echinoderm microtubule-associated protein-like 4 (EML4)-ALK mutation, EML4-ALKL1196M. Furthermore, the PKPD relationships for these ALK inhibitors were evaluated and compared between oral administration and subcutaneous constant infusion (i.e., between different pharmacokinetic [PK] profiles). Oral and subcutaneous PK profiles of these ALK inhibitors were adequately described by a one-compartment PK model. An indirect response model extended with a modulator fit the time courses of PF06463922- and PF06471402-mediated target modulation (i.e., ALK phosphorylation) with an estimated unbound EC50,in vivo of 36 and 20 nM, respectively, for oral administration, and 100 and 69 nM, respectively, for subcutaneous infusion. A drug-disease model based on the turnover concept fit tumor growth curves inhibited by PF06463922 and PF06471402 with estimated unbound tumor stasis concentrations of 51 and 27 nM, respectively, for oral administration, and 116 and 70 nM, respectively, for subcutaneous infusion. Thus, the EC50,in vivo to EC60,in vivo estimates for ALK inhibition corresponded to the concentrations required tumor stasis in all cases, suggesting that the pharmacodynamic relationships of target modulation to antitumor efficacy were consistent among the ALK inhibitors, even when the PK profiles with different administration routes were considerably different.
New bioacoustic records of Rana bedriagae Camerano , 1882 ( Anura : Ranidae ) from Turkey

The advertisement calls of water frogs at nine sites along the south coast and within Turkey were recorded, analyzed and compared with those of Rana ridibimda Pallas, 1771, and R. bedriagae Camerano, 1882. All the call characteristics, whether temperaturedependent or not, are consistent with those of R. bedriagae but not those of R. ridibimda. It follows that these localities are inhabited by Levantine frogs, R. bedriagae.
SHILEY FLEXIBLE ADULT TRACHEOSTOMY TUBES VERSUS SHILEY DCT AND FEN TRACHEOSTOMY TUBES PHONATION PERFORMANCE RESULTS

INTRODUCTION Tracheostomy can influence the work of breathing by introducing additional resistance to airflow through the native airway. In a 1996 study, Hussey and Bishop determined that the presence of a deflated cuff in the airway significantly increased the work of breathing and theorized that a lower profile cuff would introduce less resistance to airflow. Hussey and Bishop also demonstrated that the presence of fenestration reduced the work of breathing. Interestingly, the authors noted that when they tested a cuffless No. 8 tube, the required inspiratory pressures remained below 1 cm of water at all flow rates. These findings suggest that the majority of the observed resistance to airflow for the cuffed tubes was due to the floppy, deflated cuff. In recent years, next-generation tracheostomy tubes with a taper-shaped cuff have been developed. Evidence to date suggests that a taper-shaped cuff provides a better seal against the tracheal wall at lower cuff pressures than a traditional cylindrical cuff, resulting in reduced fluid leak across the cuff and a lower potential for tracheal injury from excess pressure. Given the influence of cuff shape and fenestration on tracheal airflow, the purpose of this analysis was to evaluate airflow around the tube for a range of Shiley tracheostomy tubes. More specifically, Shiley Flexible tracheostomy tubes with a taper-shaped TaperGuard cuff were compared to predicate tubes with cylindrical cuffs, with and without fenestration. METHODS Test Product, Equipment and Procedure Shiley Flexible adult tracheostomy tubes with TaperGuard cuff and disposable inner cannula (4CN65A, 6CN75A and 10CN10A), Shiley (DCT) tracheostomy tube cuffed with disposable inner cannula tubes (4DCT, 6DCT and 10DCT), and Shiley (FEN) tracheostomy tube cuffed with inner disposable cannula fenestrated (4FEN, 6FEN and 10FEN) were evaluated using a benchtop phonation test apparatus.
Stereotype Threat and Self-Affirmation: Reconsidering the Protective Influence of Value Affirmation Interventions

The current study was designed to examine the influence of self-affirmation on learners' executive attention and mathematical performance when confronted with stereotype threat. Participants (N = 206) were exposed to self-affirmation and stereotype threat manipulations, completed operation-span and letter memory tasks, and a series of high-difficulty modular subtraction problems. Our results revealed that self-affirmed participants demonstrated lower mathematical performance when problems were completed under high stereotype threat conditions. Further, our data revealed the self-affirmation and stereotype threat manipulations had no impact on components of executive attention hypothesized to underlie stereotype threat effects. These findings add to recent literature calling into question the viability of selfaffirmation as a strategy for protecting at-risk students' achievement.
Clusters of Faulty States for Debugging Behavioural Models

Designing and developing distributed software has always been a tedious and error-prone task, and the ever increasing software complexity is making matters even worse. Model checking is an established technique for automatically finding bugs by verifying that a model satisfies a given temporal property. When the model violates the property, the model checker returns a counterexample, which is a sequence of actions leading to a state where the property is not satisfied. Understanding this counterexample for debugging the specification or program is a complicated task because the counterexample gives only a partial view of the source of the problem, and because there is usually little support beyond that counterexample to identify the source of the problem. In this paper, we focus on behavioural models (Labelled Transition Systems) and we propose some techniques for simplifying the debugging of erroneous models. We first focus on the erroneous part of the model and we detect specific states (called faulty states) where a choice is possible between executing a correct behaviour or falling into an erroneous part of the model. The goal of this paper is to group these faulty states into clusters. Clusters help the user to identify the source of the bug since each cluster of states provides some information about the bug. We implemented this technique into a tool, which allows the visualization of the faulty model and the computation of clusters.
TEDIC: Neural Modeling of Behavioral Patterns in Dynamic Social Interaction Networks

Dynamic social interaction networks are an important abstraction to model time-stamped social interactions such as eye contact, speaking and listening between people. These networks typically contain informative while subtle patterns that reflect people’s social characters and relationship, and therefore attract the attentions of a lot of social scientists and computer scientists. Previous approaches on extracting those patterns primarily rely on sophisticated expert knowledge of psychology and social science, and the obtained features are often overly task-specific. More generic models based on representation learning of dynamic networks may be applied, but the unique properties of social interactions cause severe model mismatch and degenerate the quality of the obtained representations. Here we fill this gap by proposing a novel framework, termed TEmporal network-DIffusion Convolutional networks (TEDIC), for generic representation learning on dynamic social interaction networks. We make TEDIC a good fit by designing two components: 1) Adopt diffusion of node attributes over a combination of the original network and its complement to capture long-hop interactive patterns embedded in the behaviors of people making or avoiding contact; 2) Leverage temporal convolution networks with hierarchical set-pooling operation to flexibly extract patterns from different-length interactions scattered over a long time span. The design also endows TEDIC with certain self-explaining power. We evaluate TEDIC over five real datasets for four different social character prediction tasks including deception detection, dominance identification, nervousness detection and community detection. TEDIC not only consistently outperforms previous SOTA’s, but also provides two important pieces of social insight. In addition, it exhibits favorable societal characteristics by remaining unbiased to people from different regions. Our project website is: http://snap.stanford. edu/tedic/.