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Linked micromaps were originally developed to display geographically indexed statistics in an intuitive way by connecting them to a sequence of small maps. The approach integrates several visualization design principles, such as small multiples, discrete color indexing, and ordering. Linked micromaps allow for other types of data displays that are connected to and conditional on geographic areas. Initial applications of micromaps used data from the National Cancer Institute and the Environmental Protection Agency. In this paper, we will show how linked micromaps can be used to better understand and explore relationships and distributions of statistics linked to US states and Washington, DC. We will compare linked micromaps with other popular data displays of geographic data, such as bubble maps, choropleth maps, and bar charts. We will illustrate how linked micromaps can be used for evidence-based decision-making using data from the Bureau of Labor Statistics, the Census Bureau, and the Economic Research Service. The presentations, R scripts, and the data sets used in this article are available here: https://github.com/wlmcensus/Joint-Statistical-Meetings-Presentation-2024. The work discussed in this article was presented at the Joint Statistical Meetings (JSM) 2024 and the American Association for Public Opinion Research (AAPOR) 2024 Annual Conference., Comment: 18 pages, 13 figures, JSM 2024 Conference Proceedings

Previous studies on peristalsis, the pumping of fluid along a channel by wave-like displacements of the channel walls, have shown that the elastic properties of the channel and the peristaltic wave shape can influence the flow rate. Motivated by the oscillatory flow of cerebrospinal fluid along compliant perivascular spaces, we consider a prescribed wave motion of a single boundary which pumps fluid at small Reynolds number near an elastic half space. We investigate the relationship between flow rate and elastic deformation as a function of the fluid and solid properties. We consider transverse and longitudinal motion of the driving peristaltic wave. We find that a transverse peristaltic wave produces net forward flow and induces elastic motion in which all material points oscillate uniformly. Conversely, a longitudinal peristaltic wave produces a net backward flow and drives elastic motion which is nonuniform in the elastic solid. We use dimensional values relevant to the flow of cerebrospinal fluid and find agreement with in vivo velocity data.

The detection of bias in natural language processing (NLP) is a critical challenge, particularly with the increasing use of large language models (LLMs) in various domains. This paper introduces GUS-Net, an innovative approach to bias detection that focuses on three key types of biases: (G)eneralizations, (U)nfairness, and (S)tereotypes. GUS-Net leverages generative AI and automated agents to create a comprehensive synthetic dataset, enabling robust multi-label token classification. Our methodology enhances traditional bias detection methods by incorporating the contextual encodings of pre-trained models, resulting in improved accuracy and depth in identifying biased entities. Through extensive experiments, we demonstrate that GUS-Net outperforms state-of-the-art techniques, achieving superior performance in terms of accuracy, F1-score, and Hamming Loss. The findings highlight GUS-Net's effectiveness in capturing a wide range of biases across diverse contexts, making it a valuable tool for social bias detection in text. This study contributes to the ongoing efforts in NLP to address implicit bias, providing a pathway for future research and applications in various fields. The Jupyter notebooks used to create the dataset and model are available at: https://github.com/Ethical-Spectacle/fair-ly/tree/main/resources. Warning: This paper contains examples of harmful language, and reader discretion is recommended.

Multimodal fusion promises better pancreas segmentation. However, where to perform fusion in models is still an open question. It is unclear if there is a best location to fuse information when analyzing pairs of imperfectly aligned images. Two main alignment challenges in this pancreas segmentation study are 1) the pancreas is deformable and 2) breathing deforms the abdomen. Even after image registration, relevant deformations are often not corrected. We examine how early through late fusion impacts pancreas segmentation. We used 353 pairs of T2-weighted (T2w) and T1-weighted (T1w) abdominal MR images from 163 subjects with accompanying pancreas labels. We used image registration (deeds) to align the image pairs. We trained a collection of basic UNets with different fusion points, spanning from early to late, to assess how early through late fusion influenced segmentation performance on imperfectly aligned images. We assessed generalization of fusion points on nnUNet. The single-modality T2w baseline using a basic UNet model had a Dice score of 0.73, while the same baseline on the nnUNet model achieved 0.80. For the basic UNet, the best fusion approach occurred in the middle of the encoder (early/mid fusion), which led to a statistically significant improvement of 0.0125 on Dice score compared to the baseline. For the nnUNet, the best fusion approach was na\"ive image concatenation before the model (early fusion), which resulted in a statistically significant Dice score increase of 0.0021 compared to baseline. Fusion in specific blocks can improve performance, but the best blocks for fusion are model specific, and the gains are small. In imperfectly registered datasets, fusion is a nuanced problem, with the art of design remaining vital for uncovering potential insights. Future innovation is needed to better address fusion in cases of imperfect alignment of abdominal image pairs., Comment: 13.5 pages of manuscript content

This review paper describes the energy-upgraded CEBAF accelerator. This superconducting linac has achieved 12 GeV beam energy by adding 11 new high-performance cryomodules containing eighty-eight superconducting cavities that have operated CW at an average accelerating gradient of 20 MV/m. After reviewing the attributes and performance of the previous 6 GeV CEBAF accelerator, we discuss the upgraded CEBAF accelerator system in detail with particular attention paid to the new beam acceleration systems. In addition to doubling the acceleration in each linac, the upgrade included improving the beam recirculation magnets, adding more helium cooling capacity to allow the newly installed modules to run cold, adding a new experimental hall, and improving numerous other accelerator components. We review several of the techniques deployed to operate and analyze the accelerator performance, and document system operating experience and performance. In the final portion of the document, we present much of the current planning regarding projects to improve accelerator performance and enhance operating margins, and our plans for ensuring CEBAF operates reliably into the future. For the benefit of potential users of CEBAF, the performance and quality measures for beam delivered to each of the experimental halls is summarized in the appendix., Comment: 66 pages, 73 figures, 21 tables

Parametric statistical methods play a central role in analyzing risk through its underlying frequency and severity components. Given the wide availability of numerical algorithms and high-speed computers, researchers and practitioners often model these separate (although possibly statistically dependent) random variables by fitting a large number of parametric probability distributions to historical data and then comparing goodness-of-fit statistics. However, this approach is highly susceptible to problems of overfitting because it gives insufficient weight to fundamental considerations of functional simplicity and adaptability. To address this shortcoming, we propose a formal mathematical measure for assessing the versatility of frequency and severity distributions prior to their application. We then illustrate this approach by computing and comparing values of the versatility measure for a variety of probability distributions commonly used in risk analysis.

Generating ligand molecules for specific protein targets, known as structure-based drug design, is a fundamental problem in therapeutics development and biological discovery. Recently, target-aware generative models, especially diffusion models, have shown great promise in modeling protein-ligand interactions and generating candidate drugs. However, existing models primarily focus on learning the chemical distribution of all drug candidates, which lacks effective steerability on the chemical quality of model generations. In this paper, we propose a novel and general alignment framework to align pretrained target diffusion models with preferred functional properties, named AliDiff. AliDiff shifts the target-conditioned chemical distribution towards regions with higher binding affinity and structural rationality, specified by user-defined reward functions, via the preference optimization approach. To avoid the overfitting problem in common preference optimization objectives, we further develop an improved Exact Energy Preference Optimization method to yield an exact and efficient alignment of the diffusion models, and provide the closed-form expression for the converged distribution. Empirical studies on the CrossDocked2020 benchmark show that AliDiff can generate molecules with state-of-the-art binding energies with up to -7.07 Avg. Vina Score, while maintaining strong molecular properties. Code is available at https://github.com/MinkaiXu/AliDiff.

Automated market makers with concentrated liquidity capabilities are programmable at the tick level. The maximization of earned fees, plus depreciated reserves, is a convex optimization problem whose vector solution gives the best provision of liquidity at each tick under a given set of parameter estimates for swap volume and price volatility. Surprisingly, early results show that concentrating liquidity around the current price is usually not the best strategy., Comment: 14 pages, 6 figures

The shortage of clinical workforce presents significant challenges in mental healthcare, limiting access to formal diagnostics and services. We aim to tackle this shortage by integrating a customized large language model (LLM) into the workflow, thus promoting equity in mental healthcare for the general population. Although LLMs have showcased their capability in clinical decision-making, their adaptation to severe conditions like Post-traumatic Stress Disorder (PTSD) remains largely unexplored. Therefore, we collect 411 clinician-administered diagnostic interviews and devise a novel approach to obtain high-quality data. Moreover, we build a comprehensive framework to automate PTSD diagnostic assessments based on interview contents by leveraging two state-of-the-art LLMs, GPT-4 and Llama-2, with potential for broader clinical diagnoses. Our results illustrate strong promise for LLMs, tested on our dataset, to aid clinicians in diagnostic validation. To the best of our knowledge, this is the first AI system that fully automates assessments for mental illness based on clinician-administered interviews.

Plasma processing of superconducting radio frequency (SRF) cavities has shown an improvement in accelerating gradient by reducing the radiation due to field emission and multipacting. Plasma processing is a common technique where the free oxygen produced by the plasma breaks down and removes hydrocarbons from surfaces. This increases the work function and reduces the secondary emission coefficient. The hydrocarbon fragments of H2, CO, CO2, and H2O are removed from the system with the process gas which is flowing through the system. Here, we present COMSOL for the first time to simulate the plasma processing of an SRF cavity. In this work, we use Jefferson Lab's C75 SRF cavities design as our case study. Using simulation, we predict the condition of plasma ignition inside the SRF cavity. The simulation provides information about the optimal rf coupling to the cavity, mode for plasma ignition, choice of gas concentration, power, and pressure.

Following the recent release of AI assistants, such as OpenAI's ChatGPT and GitHub Copilot, the software industry quickly utilized these tools for software development tasks, e.g., generating code or consulting AI for advice. While recent research has demonstrated that AI-generated code can contain security issues, how software professionals balance AI assistant usage and security remains unclear. This paper investigates how software professionals use AI assistants in secure software development, what security implications and considerations arise, and what impact they foresee on secure software development. We conducted 27 semi-structured interviews with software professionals, including software engineers, team leads, and security testers. We also reviewed 190 relevant Reddit posts and comments to gain insights into the current discourse surrounding AI assistants for software development. Our analysis of the interviews and Reddit posts finds that despite many security and quality concerns, participants widely use AI assistants for security-critical tasks, e.g., code generation, threat modeling, and vulnerability detection. Their overall mistrust leads to checking AI suggestions in similar ways to human code, although they expect improvements and, therefore, a heavier use for security tasks in the future. We conclude with recommendations for software professionals to critically check AI suggestions, AI creators to improve suggestion security and capabilities for ethical security tasks, and academic researchers to consider general-purpose AI in software development., Comment: Extended version of the paper that appeared at ACM CCS 2024. 21 pages, 2 figures, 3 tables

The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing an adaptive relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.

A key objective in nuclear and high-energy physics is to describe nonequilibrium dynamics of matter, e.g., in the early universe and in particle colliders, starting from the Standard Model. Classical-computing methods, via the framework of lattice gauge theory, have experienced limited success in this mission. Quantum simulation of lattice gauge theories holds promise for overcoming computational limitations. Because of local constraints (Gauss's laws), lattice gauge theories have an intricate Hilbert-space structure. This structure complicates the definition of thermodynamic properties of systems coupled to reservoirs during equilibrium and nonequilibrium processes. We show how to define thermodynamic quantities such as work and heat using strong-coupling thermodynamics, a framework that has recently burgeoned within the field of quantum thermodynamics. Our definitions suit instantaneous quenches, simple nonequilibrium processes undertaken in quantum simulators. To illustrate our framework, we compute the work and heat exchanged during a quench in a $Z_2$ lattice gauge theory coupled to matter in 1+1 dimensions. The thermodynamic quantities, as functions of the quench parameter, evidence an expected phase transition. For general thermal states, we derive a simple relation between a quantum many-body system's entanglement Hamiltonian, measurable with quantum-information-processing tools, and the Hamiltonian of mean force, used to define strong-coupling thermodynamic quantities., Comment: 5.5 pages (3 figures) main text, 13.5 pages total

There is general agreement that some form of regulation is necessary both for AI creators to be incentivised to develop trustworthy systems, and for users to actually trust those systems. But there is much debate about what form these regulations should take and how they should be implemented. Most work in this area has been qualitative, and has not been able to make formal predictions. Here, we propose that evolutionary game theory can be used to quantitatively model the dilemmas faced by users, AI creators, and regulators, and provide insights into the possible effects of different regulatory regimes. We show that creating trustworthy AI and user trust requires regulators to be incentivised to regulate effectively. We demonstrate the effectiveness of two mechanisms that can achieve this. The first is where governments can recognise and reward regulators that do a good job. In that case, if the AI system is not too risky for users then some level of trustworthy development and user trust evolves. We then consider an alternative solution, where users can condition their trust decision on the effectiveness of the regulators. This leads to effective regulation, and consequently the development of trustworthy AI and user trust, provided that the cost of implementing regulations is not too high. Our findings highlight the importance of considering the effect of different regulatory regimes from an evolutionary game theoretic perspective.

School ratings are a ubiquitous feature of the U.S. educational system. Alongside state-mandated measures of school performance, non-state organizations such as GreatSchools.org and Niche have created consumer-oriented systems of school ratings that draw on publicly available information about schools. Claiming the purpose of their rating systems is to help families navigate increasingly complex school choice options, these organizations present their ratings as authoritative sources of information. In the growing evidence base about consumer-oriented systems, there is consistent research showing that the measures that comprise school ratings are associated with schools' demographic characteristics, such as students' race/ethnicity and socioeconomic status. This brief examines these consumer-oriented rating systems and their implications for educational equity, and provides recommendations to minimize potential harms.

Conversions between neutrons $n$ and Dark Matter candidate sterile neutrons $n'$ have been proposed as a mechanism for Baryon Number $\mathcal{B}$ violation. In the case that there is a small mass difference $\Delta{m}$ between the $n$ and the $n'$ states, oscillations can be induced by compensating for $\Delta{m}$ with a magnetic field. A search for such neutron oscillations was performed at the Spallation Neutron Source by looking for anomalous neutron transmission through a strongly absorbing cadmium wafer inside of a $6.6$~T magnet. The approach described here saw no regenerated neutrons above background, which provides an improved limit for neutron - sterile neutrons transformations for a range of $\Delta{m}$ between $0.1$~neV and $1000$~neV., Comment: 18 pages, 15 figures

Hallucinations and delusions are often grouped together within the positive symptoms of psychosis. However, recent evidence suggests they may be driven by distinct computational and neural mechanisms. Examining the time course of their emergence may provide insights into the relationship between these underlying mechanisms. Participants from the second (N = 719) and third (N = 699) iterations of the North American Prodrome Longitudinal Study (NAPLS 2 and 3) were assessed for timing of CHR-P-level delusion and hallucination onset. Pre-onset symptom patterns in first-episode psychosis patients (FEP) from the Prevention and Early Intervention Program for Psychosis (PEPP-Montreal; N = 694) were also assessed. Symptom onset was determined at baseline assessment and the evolution of symptom patterns examined over 24 months. In all three samples, participants were more likely to report the onset of delusion-spectrum symptoms prior to hallucination-spectrum symptoms (odds ratios (OR): NAPLS 2 = 4.09; NAPLS 3 = 4.14; PEPP, Z = 7.01, P < 0.001) and to present with only delusions compared to only hallucinations (OR: NAPLS 2 = 5.6; NAPLS 3 = 11.11; PEPP = 42.75). Re-emergence of delusions after remission was also more common than re-emergence of hallucinations (Ps < 0.05), and hallucinations more often resolved first (Ps < 0.001). In both CHR-P samples, ratings of delusional ideation fell with the onset of hallucinations (P = 0.007). Delusions tend to emerge before hallucinations and may play a role in their development. Further work should examine the relationship between the mechanisms driving these symptoms and its utility for diagnosis and treatment.

Charge order pervades the phase diagrams of many quantum materials where it competes with superconducting and magnetic phases, hosts electronic phase transitions and topological defects, and couples to the lattice generating intricate structural distortions. Incommensurate charge order is readily stabilized in manganese oxides where it is associated with anomalous electronic and magnetic properties, but its nanoscale structural inhomogeneity complicates precise characterization and understanding of these phases. Leveraging atomic-resolution variable temperature cryogenic scanning transmission electron microscopy (cryo-STEM), we characterize the thermal evolution of charge order in a model manganite system, finding that mobile networks of topological defects generate phase inhomogeneity and induce incommensuration in an otherwise lattice-locked modulation. The melting of charge order at high temperatures is accompanied by generation of additional defects which decouple large areas of the order from the lattice., Comment: 14 pages, 6 figures

The progression of a single point in volleyball starts with a serve and then alternates between teams, each team allowed up to three contacts with the ball. Using charted data from the 2022 NCAA Division I women's volleyball season (4,147 matches, 600,000+ points, more than 5 million recorded contacts), we model the progression of a point as a Markov chain with the state space defined by the sequence of contacts in the current volley. We estimate the probability of each team winning the point, which changes on each contact. We attribute changes in point probability to the player(s) responsible for each contact, facilitating measurement of performance on the point scale for different skills. Traditional volleyball statistics do not allow apples-to-apples comparisons across skills, and they do not measure the impact of the performances on team success. For adversarial contacts (serve/receive and attack/block/dig), we estimate a hierarchical linear model for the outcome, with random effects for the players involved; and we adjust performance for strength of schedule not only on the conference/team level but on the individual player level. We can use the results to answer practical questions for volleyball coaches., Comment: 23 pages, 10 figures, 2023 NESSIS

We have measured magnetic torque on a $T_N=7$ K single crystal of $\alpha$-RuCl$_3$, as a function of the field angle in the $ab$-plane, focusing on temperatures between 2 and 20 K and fields from 0 to 9 T. We find a number of features, many of which can be classified by their angular periodicity. The sample shows an oscillation with a period of 180$^\circ$ (i.e.\ two-fold periodicity) and within the magnetically ordered zigzag phase there is a 60$^\circ$ period (i.e.\ six-fold) sawtooth pattern, which can be explained by reorientation of the zigzag domains as the crystal rotates in the applied field. We argue that the six-fold sawtooth and the two-fold sinusoidal signals arise from distinct regions of the crystal. Suppressing the zigzag order with an applied field above $\sim8$ T at low temperature, a six-fold {\sl sinusoidal} signal remains, suggesting that there is fluctuating zigzag order in the putative field-induced quantum spin liquid state. Finally, in testing theoretical results which predict a torque response with divergent slope across C$_2$-preserving $b$-axes (B1-axis), we find no features like that predicted for Ising topological order. Instead we find features at low temperatures and fields just above the zigzag phase across the non-C$_2$-preserving $b$-axes (B2-axes). Interpretation of this feature is complicated by the development of other similar signatures nearby at slightly lower fields, and by clear enhancement with thermal cycling. Additionally, we contrast the torque response of $T_N \sim$ 7 K and 14 K samples., Comment: 18 pages, 16 figures; extended explanation of model, and additional experimental results in appendices

Key properties of physical systems can be described by the eigenvalues of matrices that represent the system. Computational algorithms that determine the eigenvalues of these matrices exist, but they generally suffer from a loss of performance as the matrix grows in size. This process can be expanded to quantum computation to find the eigenvalues with better performance than the classical algorithms. One application of such an eigenvalue solver is to determine energy levels of a molecule given a matrix representation of its Hamiltonian using the variational principle. Using a variational quantum eigensolver, we determine the ground state energies of different molecules. We focus on the choice of optimization strategy for a Qiskit simulator on low-end hardware. The benefits of several different optimizers were weighed in terms of accuracy in comparison to an analytic classical solution as well as code efficiency.

In stars and planets natural processes heat convective flows in the bulk of a convective region rather than at hard boundaries. By characterizing how convective dynamics are determined by the strength of an internal heating source we can gain insight into the processes driving astrophysical convection. Internally heated convection has been studied extensively in incompressible fluids, but the effects of stratification and compressibility have not been examined in detail. In this work, we study fully compressible convection driven by a spatially uniform heating source in 2D and 3D Cartesian, hydrodynamic simulations. We use a fixed temperature upper boundary condition which results in a system that is internally heated in the bulk and cooled at the top. We find that the flow speed, as measured by the Mach number, and turbulence, as measured by the Reynolds number, can be independently controlled by separately varying the characteristic temperature gradient from internal heating and the diffusivities. 2D simulations at a fixed Mach number (flow speed) demonstrate consistent power at low wavenumber as diffusivities are decreased. We observe convection where the velocity distribution is skewed towards cold, fast downflows, and that the flow speed is related to the length scale and entropy gradient of the upper boundary where the downflows are driven. We additionally find a heat transport scaling law which is consistent with prior incompressible work., Comment: 22 pages, 12 figures, submitted to Phys. Rev. Fluids

Hybrid organic-inorganic nanomaterials can exhibit transitional behavior that deviates from models developed for all-organic or all-inorganic materials systems. Here, we reveal the complexity of exciton-phonon interactions in a recently discovered 2D layered hybrid organic-inorganic semiconductor, silver phenylselenolate (AgSePh). Using femtosecond resonant impulsive vibrational spectroscopy and non-resonant Raman scattering, we measure multiple hybrid organic-inorganic vibrational modes and identify a subset of these modes that strongly couple to the electronic excited state. Calculations by density functional perturbation theory show that these strongly coupled modes exhibit large out-of-plane silver atomic motions and silver-selenium spacing displacements. Moreover, analysis of photoluminescence fine-structure splitting and temperature-dependent peak-shifting/linewidth-broadening suggests that light emission in AgSePh is most strongly affected by a compound 100 cm-1 mode involving the wagging motion of phenylselenolate ligands and accompanying metal-chalcogen stretching. Finally, red-shifting of vibrational modes with increasing temperature reveals a high degree of anharmonicity arising from non-covalent interactions between phenyl rings. These findings reveal the unique effects of hybrid vibrational modes in organic-inorganic semiconductors and motivate future work aimed at specifically engineering such interactions through chemical and structural modification.

The derivatives with respect to the variable $r$ of $\pi r^2$ and $\frac{4}{3}\pi r^3$ are $2\pi r$ and $4\pi r^2$, respectively. This relates, through the derivative, the area enclosed in a circle to the length of that circle and, likewise, the volume of a sphere to the surface area of that sphere. The reasons why this works are basic to a first course in calculus. In this brief article, we expand on these ideas to shapes other than circles and spheres. Our approach is with the first year calculus student in mind.

We carry out Monte Carlo simulations on fluid membranes with orientational order and multiple edges in the presence and absence of external forces. The membrane resists bending and has an edge tension, the orientational order couples with the membrane surface normal through a cost for tilting, and there is a chiral liquid crystalline interaction. In the absence of external forces, a membrane initialized as a vesicle will form a disk at low chirality, with the directors forming a smectic-A phase with alignment perpendicular to the membrane surface except near the edge. At large chirality a catenoid-like shape or a trinoid-like shape is formed, depending on the number of edges in the initial vesicle. This shape change is accompanied by cholesteric ordering of the directors and multiple $\pi$ walls connecting the membrane edges and wrapping around the membrane neck. If the membrane is initialized instead in a cylindrical shape and stretched by an external force, it maintains a nearly cylindrical shape but additional liquid crystalline phases appear. For large tilt coupling and low chirality, a smectic-A phase forms. For lower values of the tilt coupling, a nematic phase appears at zero chirality with the average director oriented perpendicular to the long axis of the membrane, while for nonzero chirality a cholesteric phase appears. The $\pi$ walls are tilt walls at low chirality and transition to twist walls as chirality is increased. We construct a continuum model of the director field to explain this behavior., Comment: 12 pages, 13 figures

Using a minimal hydrodynamic model, we theoretically and computationally study active gels in straight and annular two-dimensional channels subject to an externally imposed shear. The gels are isotropic in the absence of externally- or activity-driven shear, but have nematic order that increases with shear rate. Using the finite element method, we determine the possible flow states for a range of activities and shear rates. Linear stability analysis of an unconfined gel in a straight channel shows that an externally imposed shear flow can stabilize an extensile fluid that would be unstable to spontaneous flow in the absence of the shear flow, and destabilize a contractile fluid that would be stable against spontaneous flow in the absence of shear flow. These results are in rough agreement with the stability boundaries between the base shear flow state and the nonlinear flow states that we find numerically for a confined active gel. For extensile fluids, we find three kinds of nonlinear flow states in the range of parameters we study: unidirectional flows, oscillatory flows, and dancing flows. To highlight the activity-driven spontaneous component of the nonlinear flows, we characterize these states by the average volumetric flow rate and the wall stress. For contractile fluids, we only find the linear shear flow and a nonlinear unidirectional flow in the range of parameters that we studied. For large magnitudes of the activity, the unidirectional contractile flow develops a boundary layer. Our analysis of annular channels shows how curvature of the streamlines in the base flow affects the transitions among flow states.

Event centric approaches to modeling physics have gained traction in recent decades. In this work, we present a first principles approach to this idea, which assumes nothing but the existence of causal networks of events and their relationships. The modeling elements we employ consist solely of classical bits, or the abstract symbols $0$ and $1$. Using sequences of these symbols, we model primitive elements of causal networks consisting of two causally connected events. By introducing an epistemic constraint on observers, we derive a statistical picture of these network elements, leading to the emergence of non-determinism and the subsequent derivation of a quantum theory. We then apply this event centric framework to three physical scenarios involving spin, including a Bell test. Comparing the resulting predictions to non-relativistic quantum mechanics, we find good agreement, including a violation of the CHSH inequality. More broadly, the results presented here highlight this novel framework's explanatory and predictive power. When coupled with recent advancements in event centric approaches to modeling spacetime, we argue that this framework may provide some insight into the issue of quantum gravity., Comment: 32 pages, 9 figures

There is increasing evidence that people with hallucinations overweight perceptual beliefs relative to incoming sensory evidence. Much past work demonstrating prior overweighting has used simple, non-linguistic stimuli. However, auditory hallucinations in psychosis are often complex and linguistic. There may be an interaction between the type of auditory information being processed and its perceived quality in engendering hallucinations. We administered a linguistic version of the Conditioned Hallucinations (CH) task to an online sample of 88 general population participants. Metrics related to hallucination-proneness, recent auditory hallucinations, stimulus thresholds, and stimulus detection were collected; data was used to fit parameters of a Hierarchical Gaussian Filter model of perceptual inference to determine how latent perceptual states influenced task behavior. Replicating past results, higher CH rates were associated with measures of higher hallucination-proneness and recent hallucinatory experiences; CH rates were positively correlated with increased prior weighting; and increased prior weighting was related to recent hallucinatory experiences. Unlike past results, participants with recent hallucinatory experiences as well as those with higher hallucination-proneness had higher stimulus thresholds, lower sensitivity to stimuli presented at the highest threshold, and tended to have lower response confidence, consistent with lower precision of sensory evidence. We show that hallucination-prone individuals in the general population have increased conditioned hallucination rates using a linguistic version of the CH task, and replicated the finding that increased CH rates and recent hallucinations correlate with increased prior weighting. Results support a role for reduced sensory precision in the interplay between prior weighting and hallucination-proneness. *contributed equally

Plasma processing using a mixture of noble gas and oxygen is a technique that is currently being used to reduce field emission and multipacting in accelerating cavities. Plasma is created inside the cavity when the gas mixture is exposed to an electromagnetic field that is generated by applying RF power through the fundamental power or higher-order mode couplers. Oxygen ions and atomic oxygen are created in the plasma which breaks down the hydrocarbons on the surface of the cavity and the residuals from this process are removed as part of the process gas flow. Removal of hydrocarbons from the surface increases the work function and reduces the secondary emission coefficient. This work describes the initial results of plasma simulation, which provides insight into the ignition process, distribution of different species, and interactions of free oxygen and oxygen ions with the cavity surfaces. The simulations have been done with an Ar/O2 plasma using COMSOL multiphysics. These simulations help in understanding the dynamics and control of plasma inside the cavity and the exploration of different gas mixtures.

The ongoing deep learning revolution has allowed computers to outclass humans in various games and perceive features imperceptible to humans during classification tasks. Current machine learning techniques have clearly distinguished themselves in specialized tasks. However, we have yet to see robots capable of performing multiple tasks at an expert level. Most work in this field is focused on the development of more sophisticated learning algorithms for a robot's controller given a largely static and presupposed robotic design. By focusing on the development of robotic bodies, rather than neural controllers, I have discovered that robots can be designed such that they overcome many of the current pitfalls encountered by neural controllers in multitask settings. Through this discovery, I also present novel metrics to explicitly measure the learning ability of a robotic design and its resistance to common problems such as catastrophic interference. Traditionally, the physical robot design requires human engineers to plan every aspect of the system, which is expensive and often relies on human intuition. In contrast, within the field of evolutionary robotics, evolutionary algorithms are used to automatically create optimized designs, however, such designs are often still limited in their ability to perform in a multitask setting. The metrics created and presented here give a novel path to automated design that allow evolved robots to synergize with their controller to improve the computational efficiency of their learning while overcoming catastrophic interference. Overall, this dissertation intimates the ability to automatically design robots that are more general purpose than current robots and that can perform various tasks while requiring less computation., Comment: arXiv admin note: text overlap with arXiv:2008.06397

Robots in home environments need to be able to learn new skills continuously as data becomes available, becoming ever more capable over time while using as little real-world data as possible. However, traditional robot learning approaches typically assume large amounts of iid data, which is inconsistent with this goal. In contrast, continual learning methods like CLEAR and SANE allow autonomous agents to learn off of a stream of non-iid samples; they, however, have not previously been demonstrated on real robotics platforms. In this work, we show how continual learning methods can be adapted for use on a real, low-cost home robot, and in particular look at the case where we have extremely small numbers of examples, in a task-id-free setting. Specifically, we propose SANER, a method for continuously learning a library of skills, and ABIP (Attention-Based Interaction Policies) as the backbone to support it. We learn four sequential kitchen tasks on a low-cost home robot, using only a handful of demonstrations per task.