Impossible Tasks Research Articles

Classification Of Robots and Industrial and Medical Applications

In the last 100 years, robots have evolved from fantasy to reality. Their design, research, and manufacture require the combination of various subjects such as CS, EEE, ME, AI, and others. The main objective is to replace humans in performing heavy or impossible tasks. Robots are classified into different categories based on their era and usefulness. This thesis will analyze industrial robots in terms of their basic stress conditions and activity scope with four degrees of freedom for the normal robot arm. The system architecture, various field interactions, and structure of mechanical fingers for human-like robot arms will also be analyzed. Additionally, this paper will examine the usefulness of service industry robots and highlight the exoskeleton robot for medical treatment by analyzing the forces, torques, and displacements during the walking of patients with lower limb disabilities. The interaction force between humans and exoskeleton robots will be calculated and a model will be constructed. Finally, some difficulties and possible solutions will be presented.

The Five Impossible Tasks of Eden Smith by Tom Llewellyn (review)

The Five Impossible Tasks of Eden Smith by Tom Llewellyn (review)

Impact of Workplace Bullying on Job Burnout

Workplace bullying can result in serious consequences for both employees and employers. Unfortunately, despite the guarantee of safe employment, workplace harassment, violations of employment rights, and bullying continue to occur within organizations. This study investigated the relationships and pathways between workplace bullying and job burnout in insurance company employees. The study consisted of 189 employees. The data were collected using a sociodemographic data questionnaire, the Negative Acts Questionnaire-Revised, and Maslach Burnout Inventory Scale. The model was tested using path analysis techniques within structural equation modeling. The analysis results demonstrated a strong positive correlation between workplace bullying and job burnout. The study revealed that assigning underperforming or impossible tasks, setting unachievable goals, and excessive workloads are indicators of workplace bullying. Hostile acts such as spreading rumors, speaking maliciously, ignoring someone intentionally, and teasing someone also contributed to diminished personal achievements and underperformance. Intimidation tactics, such as recalling mistakes or domineering behavior, also led to job burnout. The study's findings suggest that employers should be aware of workplace bullying's harmful effects and take measures to prevent it from occurring to protect their employees' well-being and organizational success.

GRIMGEP: Learning Progress for Robust Goal Sampling in Visual Deep Reinforcement Learning

Designing agents, capable of learning autonomously a wide range of skills is critical in order to increase the scope of reinforcement learning. It will both increase the diversity of learned skills and reduce the burden of manually designing reward functions for each skill. Self-supervised agents, setting their own goals, and trying to maximize the diversity of those goals have shown great promise towards this end. However, a currently known limitation of agents trying to maximize the diversity of sampled goals is that they tend to get attracted to noise or more generally to parts of the environments that cannot be controlled (distractors). When agents have access to predefined goal features or expert knowledge, absolute Learning Progress (ALP) provides a way to distinguish between regions that can be controlled and those that cannot. However, those methods often fall short when the agents are only provided with raw sensory inputs such as images. In this work we extend those concepts to unsupervised image-based goal exploration. We propose a framework that allows agents to autonomously identify and ignore noisy distracting regions while searching for novelty in the learnable regions to both improve overall performance and avoid catastrophic forgetting. Our framework can be combined with any state-of-the-art novelty seeking goal exploration approaches. We construct a rich 3D image based environment with distractors. Experiments on this environment show that agents using our framework successfully identify interesting regions of the environment, resulting in drastically improved performances. The source code is available at https://sites.google.com/view/grimgep.

Home Sweet Home: The Impact of Lifestyle on a Cat's Approach to Impossible Tasks in the Home Environment.

Cat welfare is a topic of growing interest in the scientific literature. Although previous studies have focused on the effects of living style (i.e., indoor/outdoor) on cat welfare, there has been a noticeable dearth of analysis regarding the impact of lifestyle on cats' inclination and mode of communication with humans. Our research aimed to analyze the possible effect of lifestyle (e.g., living indoors only or indoor/outdoor) on cat-human communication. The cats were tested using the impossible task paradigm test, which consists of some solvable trials in which the subject learns to obtain a reward from an apparatus, followed by an impossible trial through blocking the apparatus. This procedure triggers a violation of expectations and is considered a useful tool for assessing both the decision-making process and the tendency to engage in social behaviors towards humans. A specific ethogram was followed to record the behavioral responses of the cats during the unsolvable trial. Our results show the effects of lifestyle and age on domestic cats, providing valuable insights into the factors that influence their social behaviors. Cats that can roam freely outdoors spent less time interacting with the apparatus compared to indoor-only cats. Additionally, roaming cats showed stress behaviors sooner following the expectancy of violation compared to indoor cats. The lifestyle of cats can influence their problem-solving approach while not affecting their willingness to interact with humans or their overall welfare. Future studies on this topic can be useful for improving the welfare of domestic cats.

Classification and Detection of Obstacles for Rover Navigation

In this research project, the author aims to achieve Level 3 conditional automation whereby the researched Unmanned Ground Vehicle (UGV) is bound to classify and detect its own obstacle with human assistance as it cruises through a plantation field. Recognizing the different classes of obstacles enable the UGV to plan out the most efficient path to meet its desired goal. The purpose of this research project was to develop a classification and detection of obstacle and an optimal path planning algorithm suitable to be implemented for relieving the working process in an extreme condition plantation field. This paper presents an algorithm whereby it can conduct image-based obstacle detection through image masking and model prediction, along with a trigonometrical-based path planning approach. The proposed algorithm should hypothetically allow the UGV to conduct real-time path planning as it classifies some common obstacles such as leaves, rocks, and branches existing in a plantation field. As the waypoints were marked from the Ground Control Station (GCS), the UGV will travel towards the given waypoints to complete the given mission. When the UGV meets an obstacle, it will first differentiate whether it’s traversable, followed by running the proposed algorithm to avoid the risk of destructing the UGV by choosing a collision free path. The basic idea is to apply path planning by considering the available spacing between the detected obstacle by comparing with a predefined threshold. Through the provided threshold value, the UGV can identify the type of obstacle yet to be detected. For instance, obstacles within the given range of value can be labelled to be a leafy obstacle, otherwise it is not considered to be a leafy obstacle. To ensure the behaviour and safety measure of the UGV to run smoothly, the author had undergone model training for an elevated model prediction by training and deploying a custom training loop through TensorFlow. Nevertheless, MATLAB was utilized to test out the concept of the path planning algorithm to examine its behaviour as untraversable obstacles were met. All these implementations can further grow in the agricultural industries as it can aid humans with performing tedious and impossible tasks on site.

Do Machines Really Understand Meaning? (Again)

The adventure of artificial intelligence (AI) is based on a revolutionary idea, namely, that machines are able to understand and produce linguistic acts endowed with meaning. Over the past decades, this idea has gained acceptance in the scientific community, but its consequences for human nature were not really appreciated. Recent developments in AI, due especially to Deep Learning (DL), have changed things dramatically by creating computer architectures capable of performing previously impossible tasks in areas such as image recognition and language understanding. Criticisms that were raised decades ago against this possibility have thus been revived. These criticisms are no different in argument from those made in the first place. The reason they are being raised again is because of the social impact that the new machine performances have been able to achieve, and is not driven by truly scientific reasons, which indeed prove to be inadequate when compared to the more recent artificial semantics based on deep neural networks.

Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model

The design of materials structure for optimizing functional properties and potentially, the discovery of novel behaviors is a keystone problem in materials science. In many cases microstructural models underpinning materials functionality are available and well understood. However, optimization of average properties via microstructural engineering often leads to combinatorically intractable problems. Here, we explore the use of the reinforcement learning (RL) for microstructure optimization targeting the discovery of the physical mechanisms behind enhanced functionalities. We illustrate that RL can provide insights into the mechanisms driving properties of interest in a 2D discrete Landau ferroelectrics simulator. Intriguingly, we find that non-trivial phenomena emerge if the rewards are assigned to favor physically impossible tasks, which we illustrate through rewarding RL agents to rotate polarization vectors to energetically unfavorable positions. We further find that strategies to induce polarization curl can be non-intuitive, based on analysis of learned agent policies. This study suggests that RL is a promising machine learning method for material design optimization tasks, and for better understanding the dynamics of microstructural simulations.

Public–Private Partnership as a Form of Ensuring Sustainable Development of the Forest Management Sphere

This article examines the application of one of the forms of modern public management—public–private partnership (PPP)—in the forestry sector. This contributes to the search for new forms and methods that uphold the principles of sustainable development, decentralization, liberalization and capitalization of natural resources. The purpose of this study is to determine the characteristic features and advantages of PPP as a special partnership between the state and business-entrepreneurial structures, as well as to analyze the prospects and justify the feasibility of using PPP tools to ensure effective forestry. The research methodology was based on the critical analysis of the scholarly literature. Strategic documents, political reports and programs relevant to the forestry sector were also examined. In summary, it can be said that PPP models are a significant addition to other types of cooperation, such as more formal, top-down initiatives. PPP forestry projects can enable the accomplishment of otherwise impossible tasks.

A Deep Learning–Based Diagnosis Model Driven by Tuyere Images Big Data for Iron‐Making Blast Furnaces

Deep learning, applied to traditional industries, makes many challenges and previously impossible tasks turn practicable. However, progress is still limited in the steelmaking industry. This study aims to establish a deep convolutional neural network–based diagnosis model for blast furnaces (BF) driven by tuyere image big data, which is not effectively exploited but contains valuable information. The model is characterized by having the ability to learn value features of abnormal events hidden in images and acts as a high‐dimensional complex function of abnormal events correlated to image features. The implementation framework of the model includes two stages: offline training based on the labeled historical database and online real‐time diagnosis. Specifically, the image data are first de‐noised and enhanced via the median filter and Gamma transformation algorithms. Then, when the model is offline trained, a preferable clustering algorithm combined with the manual determination is applied to labeling image data. Finally, the built model is examined for accuracy and performance. It is shown in the results that the model has an accuracy of 98% and 88.58% for diagnosing results in training and validating datasets, respectively. Thereby, the established model may be applied to the online diagnosis of an industrial BF using real‐time images.

Mission impossible: honey bees adjust time allocation when facing an unsolvable task

The degree to which miniature-brained insects make individual adjustments in response to changing conditions remains unclear. Honey bees, Apis mellifera , are an important insect model and have been shown to make individual speed–accuracy trade-offs, but it is currently not known whether they can adjust speed–accuracy strategies to solve tasks in dynamic ecological contexts. Further, it is unclear how they may behave when facing unsolvable or impossible tasks. We therefore tested whether honey bees flexibly allocate time in a colour discrimination task with four levels of task difficulty, and how such differences in time allocation affect accuracy. We found that response time differed between task difficulties, although accuracy did not appear to be influenced by response time. Specifically, bees allocated less time when facing an impossible task than when addressing easier tasks. Our findings suggest that honey bees demonstrate a time allocation strategy depending on context, and that there was little effect of speed on accuracy in both the easy and impossible tasks. • Honey bees spent less time in impossible tasks compared to easier tasks. • Fast choices do not cost accuracy if the task is very easy or impossible to solve. • Honey bees may favour a fast guessing strategy in low-risk foraging contexts. • This contrasts with bumble bee behaviour and may be due to differences in ecology.

Six Impossible Things Before Breakfast

Six Impossible Things Before Breakfast

Co-Designing Change

Co-Designing Change

Sentiment Analysis of Film Reviews using Machine Learning Techniques

A huge amount of data is generated everyday on the web and it is only going to increase as more and more people start using the internet. Because of social networking platforms, more and more people are able to express their opinion and their views on different topics such as writing reviews about what movies they watched on different social media platforms including sites like IMDB. Sorting through these reviews manually to determine whether a certain review is positive or negative is an impossible tasks. Hence, the need to automate this arises. To provide a solution to such problems there are many ways including Sentimental Analysis. Sentiment Analysis deals with interaction between machine such as computers and natural languages used by human beings in short we can say training machine in accordance to the problem statement. We can derive high quality information by using simple text entered by the user through different patterns and trends leading towards the output we expect by the various evaluations and interpretations, thus we categorise and cluster our text in respect to the problem statement we are dealing with. After that we compute our output by using the dataset we have using different models, algorithms, mathematical computations with comes under the category of computational linguistics. This field is mainly applied when we have to take reviews or survey from our customers on products or services. Sentimental Analysis is a relatively new variant in the research area. It basically refers to as the opinions or views of the different data that is being collected using surveys, comments and reviews over the web.

THE ROLE AND PLACE OF THE MYSTERY IN METAPHYSICS OF MAMLEEV’S CHAOSMOS

The article examines the role and place of the Mystery within the structure of Mamleev’s metaphysics. The author of the article implicates the concept of “chaosmos’’, introduced by James Joyce in the experimental novel “Finnegans Wake”, to the Mamleev’s Universe. This leads to the transformation of the formula “chaos – osmosis – cosmos”, actualized by postmodern discourse, into the formula “Chaos – Osmosis – Cosmos”. Chaos here is Sacred Chaos, being one of the metaphysical manifestations of Eternal Russia. Osmosis appears to be an antinomic Russian life, which combines the incompatible, and sets itself fundamentally impossible tasks. The Сosmos brings us to the tradition of Russian cosmism with its problem of microcosm and macrocosm conformity. The author of the article points out that the all-pervading and unifying element of Mamleev’s chaos is the Mystery. It is one of the five key metathemes of Mamleev’s oeuvre. The particularity of the Mystery is that it provides a link between other metathemes: utrism “I”, Death, the Abyss and Russia. In the plane of utrism “I” the Mystery is seen as the mystery of man (his higher “I”). The Mystery is related to Death by the person’s interest of his own destiny and finiteness. The problem of overcoming death and the passion for eternal life are the central themes that concern almost all the heroes of Mamleev’s novels and short stories. For Mamleev they are the source of values aimed at the human need to achieve a higher “I”. The connection between the Mystery and the Abyss, as well as the connection between the Mystery and Russia are the strongest and the most important for Mamleev. Consideration of these connections prompts the author to conclude that Yuri Mamleev describes such dimensions of the Mystery, which go beyond the categories “unfathomable-to-us” and “unfathomable-in-itself”, introduced by S. Frank. This requires the introduction of new concepts that would bring out the essence of Mamleev’s innovations. The author of the article proposes to consider the Abyss as a “Last Mystery” by the fact it is transcendent to the transcendents themselves, that is it goes beyond the Absolute, as well as to consider Eternal Russia as a Russia-Mystery on the grounds that Mamleev gives it the status of the third metaphysical Beginning, along with the Absolute and the Abyss.

In Memoriam: Ugo Fisch March 3, 1931 to December 12, 2019

In Memoriam: Ugo Fisch March 3, 1931 to December 12, 2019

Physiological Correlates of Sluggish Cognitive Tempo in Children: Examining Autonomic Nervous System Reactivity during Social and Cognitive Stressor Tasks.

It is important to establish correlates of sluggish cognitive tempo (SCT) across units of analysis and to better understand how SCT may be conceptualized in models of psychopathology. The current study examined SCT symptoms in relation to automatic nervous system reactivity during social and cognitive stressor tasks. Participants were 61 children ages 8-12years with a full range of attention-deficit/hyperactivity disorder (ADHD) symptom severity. Parents provided ratings of SCT and parents and teachers completed measures that were used to create composite indices of ADHD symptoms. Children were administered standardized peer rejection and impossible puzzle tasks, during which their respiratory sinus arrhythmia (RSA) and skin conductance level (SCL) reactivity were recorded. Regression analyses indicated that SCT symptoms were unassociated with RSA reactivity to either task. Greater SCT symptoms were significantly associated with greater SCL reactivity to peer rejection. Greater SCT symptoms were not significantly associated with SCL reactivity to the impossible puzzle task. The pattern of findings was unchanged in sensitivity analyses that controlled for ADHD symptoms, internalizing symptoms, medication status, or sex. This study provides the first evidence that SCT symptoms are associated with sympathetic nervous system reactivity. These findings suggest that SCT symptoms may be associated with greater behavioral inhibition system activation, and reactivity may be especially pronounced in social challenges.

Retraction of Soft Growing Robots Without Buckling

Tip-extending soft robots that "grow" via pneumatic eversion of their body material have demonstrated applications in exploration of cluttered environments. During growth, the motion and force of the robot tip can be controlled in three degrees of freedom using actuators that direct the tip in combination with extension. However, when reversal of the growth process is attempted by retracting the internal body material from the base, the robot body often responds by buckling rather than inverting the body material, making control of tip motion and force impossible. We present and validate a model to predict when buckling occurs instead of inversion, and we present an electromechanical device that can be added to a tip-extending soft robot to prevent buckling during retraction, restoring the ability of steering actuators to control the robot's motion and force during inversion. Using our retraction device, we demonstrate three previously impossible tasks: exploring different branches of a forking path, reversing growth while applying minimal force on the environment, and bringing back environment samples to the base.

Psyche Meets the Gatekeepers: Creating a More Humane Culture for Women in Medicine.

In the Greek myth of Psyche and Eros, Psyche must fulfill four seemingly impossible tasks to achieve full consciousness before she can be reunited with her bridegroom, Eros. From early childhood, girls and women can encounter gender stereotyping, sexual harassment and assault, and other gender-related challenges. Although both men and women can face mistreatment in medical school, female students experience sexual harassment and sexual assault at higher rates than male students. In medical training and career advancement, women often face additional obstacles unrelated to the formal education of physicians, such as salary and promotional disparities, and lack of mentorship, sponsorship, or role models. The suicide rate for male physicians is nearly 1.5 times higher than the general population; for female physicians, it is more than twice as high. Parenthood can pose additional challenges for mothers and fathers early in their academic careers, and women are vastly underrepresented as they try to move past the "gatekeepers" into leadership roles. Using the framework of the Psyche and Eros myth to examine the trajectory of a female physician's career, this article provides insights into these challenges, as well as strategies to address some of these inequities, such as programs to support female promotion and leadership, expanded mentorship and mentor models, sponsorship opportunities, leadership accountability, implicit bias training, and others. The authors call for the participation of both men and women as essential in ventures to create a more humane environment for the training and practice of medicine.

Validation of the Serbian version of inflammatory Rasch-built overall disability scale in patients with chronic inflammatory demyelinating polyradiculoneuropathy.

Inflammatory Rasch-built overall disability scale (I-RODS) seems to be a valid activity measure for use in patients with chronic inflammatory demyelinating polyradiculoneuropathy (CIDP). Our aim was to translate and validate the I-RODS for use in CIDP patients from Serbia. Study comprised 83 patients diagnosed with CIDP. I-RODS was translated and cross-culturally validated using the standard guidelines. Following scales were also administered: Medical Research Council (MRC) sum score, Inflammatory Neuropathy Cause and Treatment (INCAT) sensory and disability scores, Krupp's Fatigue Severity Scale, Beck Depression Inventory, Visual Analogue Scale for pain, and health survey-36 (SF-36) as a quality of life measure. According to the I-RODS, significant proportion of our patients reported that "running" (51%), "dancing" (41%), and "standing for hours" (40%) were impossible tasks to perform, while "teeth brushing" (94%), "eating" (88%), and "reading a newspaper/book" (82%) were noted as the easiest items. Patients with more muscle weakness (lower MRC sum score) and more severe INCAT sensory score had lower I-RODS score (P < .01). Also, patients with fatigue, depression and pain had lower I-RODS scores (P < .01). I-RODS score correlated with the INCAT disability score (P < 0.01) was 78 ± 19 compared to 51 ± 30 in patients with INCAT >1 (P < .01). I-RODS score correlated with the total SF-36 score (rho = +0.73, P < .01), as well as with all SF-36 domain scores. Serbian version of the I-RODS seems to be a valid activity measure for use in CIDP patients. I-RODS was able to assess different levels of disability, it was in association with impairment measures, INCAT disability scale and quality of life. Further studies are needed to assess its responsiveness.