Event Planning: Understanding the Process Through Gained Experience and Research

Event Planning: Understanding the Process Through Gained Experience and Research Event planning is a complex and challenging job as it requires a creative, driven individual who excels as a multitasker and problem solver. The focus of this master’s project is to engage in a deeper understanding and gain volunteer experience as part of an event planner’s team in preparation for a desired future career in the field. Both communication with and opportunities to work with local event planners assisted with accelerating experience and insight into the field of event planning. The outcomes of the project include insights into various volunteer experiences and lessons learned from conversations with various experts as well as informed thinking through examining literature and searching for resources and organizations related to event planning. An additional unexpected outcome includes an unsolicited invitation to accept a job as an event planner which the author accepted

Sensorimotor transformation:The hand that 'sees' to grasp

New findings advance our understanding of how vision is used to guide the hand during object grasping

Far from Equilibrium Percolation, Stochastic and Shape Resonances in the Physics of Life

Key physical concepts, relevant for the cross-fertilization between condensed matter physics and the physics of life seen as a collective phenomenon in a system out-of-equilibrium, are discussed. The onset of life can be driven by: (a) the critical fluctuations at the protonic percolation threshold in membrane transport; (b) the stochastic resonance in biological systems, a mechanism that can exploit external and self-generated noise in order to gain efficiency in signal processing; and (c) the shape resonance (or Fano resonance or Feshbach resonance) in the association and dissociation processes of bio-molecules (a quantum mechanism that could play a key role to establish a macroscopic quantum coherence in the cell)

CARSO: Counter-Adversarial Recall of Synthetic Observations

In this paper, we propose a novel adversarial defence mechanism for image classification -- CARSO -- inspired by cues from cognitive neuroscience. The method is synergistically complementary to adversarial training and relies on knowledge of the internal representation of the attacked classifier. Exploiting a generative model for adversarial purification, conditioned on such representation, it samples reconstructions of inputs to be finally classified. Experimental evaluation by a well-established benchmark of varied, strong adaptive attacks, across diverse image datasets and classifier architectures, shows that CARSO is able to defend the classifier significantly better than state-of-the-art adversarial training alone -- with a tolerable clean accuracy toll. Furthermore, the defensive architecture succeeds in effectively shielding itself from unforeseen threats, and end-to-end attacks adapted to fool stochastic defences. Code and pre-trained models are available at https://github.com/emaballarin/CARSO .Comment: 20 pages, 5 figures, 10 table

The Role of Perspective in Mental Time Travel

Recent years have seen accumulating evidence for the proposition that people process time by mapping it onto a linear spatial representation and automatically “project” themselves on an imagined mental time line. Here, we ask whether people can adopt the temporal perspective of another person when travelling through time. To elucidate similarities and differences between time travelling from one’s own perspective or from the perspective of another person, we asked participants to mentally project themselves or someone else (i.e., a coexperimenter) to different time points. Three basic properties of mental time travel were manipulated: temporal location (i.e., where in time the travel originates: past, present, and future), motion direction (either backwards or forwards), and temporal duration (i.e., the distance to travel: one, three, or five years). We found that time travels originating in the present lasted longer in the self- than in the other-perspective. Moreover, for self-perspective, but not for other-perspective, time was differently scaled depending on where in time the travel originated. In contrast, when considering the direction and the duration of time travelling, no dissimilarities between the self- and the other-perspective emerged. These results suggest that self- and other-projection, despite some differences, share important similarities in structure

Doing it your way: How individual movement styles affect action prediction

Individuals show significant variations in performing a motor act. Previous studies in the action observation literature have largely ignored this ubiquitous, if often unwanted, characteristic of motor performance, assuming movement patterns to be highly similar across repetitions and individuals. In the present study, we examined the possibility that individual variations in motor style directly influence the ability to understand and predict others’ actions. To this end, we first recorded grasping movements performed with different intents and used a two-step cluster analysis to identify quantitatively ‘clusters’ of movements performed with similar movement styles (Experiment 1). Next, using videos of the same movements, we proceeded to examine the influence of these styles on the ability to judge intention from action observation (Experiments 2 and 3). We found that motor styles directly influenced observers’ ability to ‘read’ others’ intention, with some styles always being less ‘readable’ than others. These results provide experimental support for the significance of motor variability for action prediction, suggesting that the ability to predict what another person is likely to do next directly depends on her individual movement style

The visible face of intention: why kinematics matters

A key component of social understanding is the ability to read intentions from movements. But how do we discern intentions in others’ actions? What kind of intention information is actually available in the features of others’ movements? Based on the assumption that intentions are hidden away in the other person’s mind, standard theories of social cognition have mainly focused on the contribution of higher level processes. Here, we delineate an alternative approach to the problem of intention-from-movement understanding. We argue that intentions become “visible” in the surface flow of agents’ motions. Consequently, the ability to understand others’ intentions cannot be divorced from the capability to detect essential kinematics. This hypothesis has far reaching implications for how we know other minds and predict others’ behavior

Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels

Time series classification is essential in many fields, such as medicine, finance, environmental science, and manufacturing, enabling tasks like disease diagnosis, anomaly detection, and stock price prediction. Machine learning models like Recurrent Neural Networks and InceptionTime, while successful in numerous applications, can face scalability limitations due to intensive training requirements. To address this, random convolutional kernel models such as Rocket and its derivatives have emerged, simplifying training and achieving state-of-the-art performance by utilizing a large number of randomly generated features from time series data. However, due to their random nature, most of the generated features are redundant or non-informative, adding unnecessary computational load and compromising generalization. Here, we introduce Sequential Feature Detachment (SFD) as a method to identify and prune these non-essential features. SFD uses model coefficients to estimate feature importance and, unlike previous algorithms, can handle large feature sets without the need for complex hyperparameter tuning. Testing on the UCR archive demonstrates that SFD can produce models with $10\%$ of the original features while improving $0.2\%$ the accuracy on the test set. We also present an end-to-end procedure for determining an optimal balance between the number of features and model accuracy, called Detach-ROCKET. When applied to the largest binary UCR dataset, Detach-ROCKET is capable of reduce model size by $98.9\%$ and increases test accuracy by $0.6\%$.Comment: 13 pages, 4 figures, 1 tabl