Physics and Astronomy
The Andromeda Galaxy, also known as M31, is the closest spiral galaxy to the Milky Way (MW). If you’ve ever seen an image of M31 you’ve likely seen its spiral structure. Such an image is made from the light that is emitted by the galaxy, or more generally, the electromagnetic radiation that is emitted, i.e. it may be a composite image of radio, optical, infrared, etc. This radiation essentially comes from the stars, gas, and dust of which M31 is made. However, the stars, gas, and dust only amount to roughly 10% of M31’s total mass. The other 90% of the mass has not been detected by conventional astrophysical methods, but its existence is inferred primarily from its gravitational influence. This mass is known as dark matter (DM), and it is thought to be prevalent throughout the Universe. The DM of M31 extends well beyond the inner regions where the spiral disk can be seen. Although the true nature of DM remains unknown, one of the leading theories postulates that it’s a heavy fundamental particle, somewhat akin to the fundamental particles that make up the elements of the periodic table. If this is indeed true, then it is possible that DM will (indirectly) emit high-energy gamma-ray radiation. In fact, such a signal may have already been detected in 2009 coming from the center of the MW, but to confirm that the signal is indeed coming from DM, other complementary signals still need to be detected. In this talk I will present new evidence for a possible complimentary signal coming from the outer regions of M31, and I will discuss the implications for DM.
Fully-implantable, bi-directional brain-computer interfaces (BCIs) necessitate simultaneous cortical recording and stimulation. This is challenging since electrostimulation of cortical tissue typically causes strong artifacts that may saturate ultra-low power (ULP) analog front-end of fully implantable BCIs. To address this problem, we propose an
efficient hardware-based method for artifact suppression that
employs an auxiliary stimulator with polarity opposite to that
of the primary stimulator. The feasibility of this method was explored first in simulations, and then experimentally with brain phantom tissue and electrocorticogram (ECoG) electrode grids. We find that the canceling stimulator can reduce stimulation artifacts below the saturation limit of a typical ULP front-end, while delivering only ~10% of the primary stimulator’s voltage.
Civil and Environmental Engineering
Bottlenecks are changes in the geometry of the road (i.e. merging section, uphill grade stretch, lane-drop, tunnel, etc.) that can accommodate less vehicles than other sections of the road. The capacity drop phenomenon is directly related to the activation of these bottleneck. This phenomenon causes a reduction in the maximum flow of the freeway when queues start forming on the bottleneck. To prevent the capacity drop formation, the vehicles need to arrive to the bottleneck in a controlled manner. The use of Variable Speed Limits (VSL) has become more popular in the last decades to restrict the demand of vehicles at bottlenecks. Its goal is to limit road traffic speed in real time, by displaying a specific speed that is meant to adapt to different situations: weather, accidents, traffic state, etc. The location of VSL application area is another important design question that has been largely overlooked in the literature, with only few practical guidelines. It has been suggested that vehicles should achieve the maximum speed before entering an active bottleneck. On the other hand, the VSL application area cannot be too far away from the bottleneck to avoid queue spillback to upstream on-ramps. This study brings some light into the optimal location of VSL control both theoretically and numerically.
Craig G Anderson
Some video games create challenging environments that presume that players will often fail as part of developing the skills and knowledge they need to progress through the game. Psychology has a rich history of studying the ways that individuals react towards failure, including mastery orientation. Mastery oriented individuals are characterized by positive reactions to failure, such as renewed effort, heightened affect, and positive, affirming language. However, the challenging environments of which these video games are comprised have not yet been investigated fully in their potential impact on how players react to failure. In an initial exploration, we surveyed 928 undergraduates at a major university in the Pacific United States on their gameplay experience and attitudes towards challenge and failure. Analyses partially replicate previous findings and show that players who are more attracted to challenge in video games report higher mastery orientation. While this suggests that challenge in video games has some association with positive reactions to failure, further analyses are warranted to understand how challenge in video games influence player reactions and the impact this may have beyond of the game. In my dissertation work, I aim to answer this question through Data-driven Retrospective Interviews (DDRI) in response to free-play of notoriously challenging game, Cuphead. Along with validated mastery-orientation surveys, this study will introduce new methodology to understanding how individuals respond to failing in environments that are designed to encourage persistence. This could lead to game-based interventions to promote positive reactions to failure, a better understanding of how game developers can incorporate challenge into games, and how failure can be quantified in other data-driven technologies.
The recent wealth of astronomical data collected by modern telescopes is so immense that it no longer can be studied manually. I will discuss with you the scale of this problem and how we have addressed it using artificial neural networks. I will also share with you how our team has used machine learning to winnow out millions of galaxies to discover a few rare ones known as gravitational lenses. These are natural telescopes made of dark matter, enabling us to look deeper in space and in time.
Muhammad Twaha Naqash Ibrahim
The domain of projection-based augmented reality (PAR) systems deal with creating interactive 3D experiences without headsets by projecting light on an arbitrarily shaped physical 3D surface, creating a display on top of it. Sensors are used in a feedback loop to enable users to interact with the display.
A critical aspect of PAR systems is to recover the shape of the surface to be projected on using the feedback sensors so that the display can conform to the underlying 3D shape. Previous work has sought to address this in one of two ways: 1) projecting a set of patterns and determining the surface geometry by observing the distortions, or 2) embedding imperceptible patterns in the display content. While the first technique is robust, it is quite disruptive as any change to the surface means interrupting the display content and projecting patterns. The latter technique is not disruptive though, however embedding imperceptible patterns comes at the cost of reduced projection quality, projector-camera synchronization and slow adaptation to changes in shape.
My research seeks to address the drawbacks of both techniques enabling shape recovery from the displayed image by using the displayed image itself without any specialized patterns. The goal is to track the distortions in specific feature points in the displayed content to compute the surface geometry and adapt to the underlying shape in a continuous manner without compromising the display quality.
Micro-appearance models have brought unprecedented fidelity and details to cloth rendering.
Yet, these models neglect fabric mechanics: when a piece of cloth interacts with the environment, its yarn and fiber arrangement usually changes in response to external contact and tension forces.
Since subtle changes of a fabric’s microstructures can greatly affect its macroscopic appearance, mechanics-driven appearance variation of fabrics has been a phenomenon that remains to be captured.
We introduce a mechanics-aware model that adapts the microstructures of cloth yarns in a physics-based manner. Our technique works on two distinct physical scales: using physics-based simulations of individual yarns, we capture the rearrangement of yarn-level structures in response to external forces. These yarn structures are further enriched to obtain appearance-driving fiber-level details.
The cross-scale enrichment is made practical through a new parameter fitting algorithm for simulation, an augmented procedural yarn model coupled with a custom-design regression neural network.
We train the network using a dataset generated by joint simulations at both the yarn and the fiber levels. Through several examples, we demonstrate that our model is capable of synthesizing photorealistic cloth appearance in a mechanically plausible way.