cssi/SciSciGPT-SciSciNet huggingface dataset:
Explaining Differences in Classes of Discrete Sequences

While there are many machine learning methods to classify and cluster sequences, they fail to explain what are the differences in groups of sequences that make them distinguishable. Although in some cases having a black box model is sufficient, there is a need for increased explainability in research areas focused on human behaviors. For example, psychologists are less interested in having a model that predicts human behavior with high accuracy and more concerned with identifying differences between actions that lead to divergent human behavior. This paper presents techniques for understanding differences between classes of discrete sequences. Approaches introduced in this paper can be utilized to interpret black box machine learning models on sequences. The first approach compares k-gram representations of sequences using the silhouette score. The second method characterizes differences by analyzing the distance matrix of subsequences. As a case study, we trained black box supervised learning methods to classify sequences of GitHub teams and then utilized our sequence analysis techniques to measure and characterize differences between event sequences of teams with bots and teams without bots. In our second case study, we classified Minecraft event sequences to infer their high-level actions and analyzed differences between low-level event sequences of actions.
Agile: From Software to Mission System

The Resource Prospector (RP) is an in-situ resource utilization (ISRU) technology demonstration mission, designed to search for volatiles at the Lunar South Pole. This is NASA's first near real time tele-operated rover on the Moon. The primary objective is to search for volatiles at one of the Lunar Poles. The combination of short mission duration, a solar powered rover, and the requirement to explore shadowed regions makes for an operationally challenging mission. To maximize efficiency and flexibility in Mission System design and thus to improve the performance and reliability of the resulting Mission System, we are tailoring Agile principles that we have used effectively in ground data system software development and applying those principles to the design of elements of the mission operations system.
8d supergravity, reconstruction of internal geometry and the Swampland

We sharpen Swampland constraints on 8d supergravity theories by studying consistency conditions on worldvolume theory of 3-brane probes. Combined with a stronger form of the cobordism conjecture, this leads to the reconstruction of the compact internal geometry and implies strong restrictions on the gauge algebra and on some higher derivative terms (related to the level of the current algebra on the 1-brane). In particular we argue that 8d supergravity theories with $$ {\mathfrak{g}}_2 $$ gauge symmetry are in the Swampland. These results provide further evidence for the string lamppost principle in 8d with 16 supercharges.
Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging

Computational image reconstruction algorithms generally produce a single image without any measure of uncertainty or confidence. Regularized Maximum Likelihood (RML) and feed-forward deep learning approaches for inverse problems typically focus on recovering a point estimate. This is a serious limitation when working with under-determined imaging systems, where it is conceivable that multiple image modes would be consistent with the measured data. Characterizing the space of probable images that explain the observational data is therefore crucial. In this paper, we propose a variational deep probabilistic imaging approach to quantify reconstruction uncertainty. Deep Probabilistic Imaging (DPI) employs an untrained deep generative model to estimate a posterior distribution of an unobserved image. This approach does not require any training data; instead, it optimizes the weights of a neural network to generate image samples that fit a particular measurement dataset. Once the network weights have been learned, the posterior distribution can be efficiently sampled. We demonstrate this approach in the context of interferometric radio imaging, which is used for black hole imaging with the Event Horizon Telescope, and compressed sensing Magnetic Resonance Imaging (MRI).
Matrix Model: Emergence of a Quantum Number in the Strong Coupling Regime

We continue here to study simple matrix models of quantum mechanical Hamiltonians. The eigenvalues and eigenfunctions were associated energy levels and wave functions. Whereas previously, we consid...
A theory of financial media

Abstract We present a model of media coverage of corporate announcements. Firms strategically use the media to communicate corporate announcements to a group of traders who observe announcements not directly but through media reports. Journalists strategically select which announcements to report to readers. Media coverage inadvertently incentivizes firms to manipulate the underlying announcements. In equilibrium, media coverage is tilted towards less manipulated negative news. The presence of financial journalists leads to more manipulation but makes stock prices more informative on average. We provide additional predictions regarding the media’s impact on the quality of firm announcements and stock prices.
UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering

Aligning partial views of a scene into a single whole is essential to understanding one’s environment and is a key component of numerous robotics tasks such as SLAM and SfM. Recent approaches have proposed end-to-end systems that can outperform traditional methods by leveraging pose supervision. However, with the rising prevalence of cameras with depth sensors, we can expect a new stream of raw RGB-D data without the annotations needed for supervision. We propose UnsupervisedR&R: an end-to-end unsupervised approach to learning point cloud registration from raw RGB-D video. The key idea is to leverage differentiable alignment and rendering to enforce photometric and geometric consistency between frames. We evaluate our approach on indoor scene datasets and find that we out-perform existing traditional approaches with classical and learned descriptors while being competitive with supervised geometric point cloud registration approaches.
Inappropriateness Penalty, Desirability Premium: What Do More Certifications Actually Signal?

Prevailing theory argues that more certifications increase performance. However, emerging empirical evidence implies that obtaining more certifications may actually decrease performance. How do we ...
Dynamical Processes in Globular Clusters

Globular clusters are among the most congested stellar systems in the Universe. Internal dynamical evolution drives them toward states of high central density, while simultaneously concentrating the most massive stars and binary systems in their cores. As a result, these clusters are expected to be sites of frequent close encounters and physical collisions between stars and binaries, making them efficient factories for the production of interesting and observable astrophysical exotica. I describe some elements of the competition among stellar dynamics, stellar evolution, and other processes that control globular cluster dynamics, with particular emphasis on pathways that may lead to the formation of blue stragglers.
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks

With a growing need to enable intelligence in embedded devices in the Internet of Things (IoT) era, secure hardware implementation of Deep Neural Networks (DNNs) has become imperative. We will focus on how to address adversarial robustness for DNNs through efficiency-driven hardware optimizations. Since memory (specifically, dot-product operations) is a key energy-spending component for DNNs, hardware approaches in the past have focused on optimizing the memory. One such approach is approximate digital CMOS memories with hybrid 6T-8T SRAM cells that enable supply voltage (Vdd) scaling yielding low-power operation, without significantly affecting the performance due to read/write failures incurred in the 6T cells. In this paper, we show how the bit-errors in the 6T cells of hybrid 6T-8T memories minimize the adversarial perturbations in a DNN. Essentially, we find that for different configurations of 8T-6T ratios and scaled Vdd operation, noise incurred in the hybrid memory architectures is bound within specific limits. This hardware noise can potentially interfere in the creation of adversarial attacks in DNNs yielding robustness. Another memory optimization approach involves using analog memristive crossbars that perform Matrix- Vector-Multiplications (MVMs) efficiently with low energy and area requirements. However, crossbars generally suffer from intrinsic non-idealities that cause errors in performing MVMs, leading to degradation in the accuracy of the DNNs. We will show how the intrinsic hardware variations manifested through crossbar non-idealities yield adversarial robustness to the mapped DNNs without any additional optimization.
Frontiers in Scalable Distributed Control: SLS, MPC, and Beyond

The System Level Synthesis (SLS) approach facilitates distributed control of large cyberphysical networks in an easy-to-understand, computationally scalable way. We present an overview of the SLS approach and its associated extensions in nonlinear control, MPC, adaptive control, and learning for control. To illustrate the effectiveness of SLS-based methods, we present a case study motivated by the power grid, with communication constraints, actuator saturation, disturbances, and changing setpoints. This simple but challenging case study necessitates the use of model predictive control (MPC); however, standard MPC techniques often scales poorly to large systems and incurs heavy computational burden. To address this challenge, we combine two SLS-based controllers to form a layered MPC-like controller. Our controller has constant computational complexity with respect to the system size, gives a 20-fold reduction in online computation requirements, and still achieves performance that is within 3% of the centralized MPC controller.
A model-free method for learning flexibility capacity of loads providing grid support

Flexible loads are a resource for the Balancing Authority (BA) of the future to aid in the balance of power supply and demand. In order to be used as a resource, the BA must know the capacity of the flexible loads to vary their power demand over a baseline without violating consumers' quality of service (QoS). Existing work on capacity characterization is model-based: They need models relating power consumption to variables that dictate QoS, such as temperature in the case of an air conditioning system. However, in many cases the model parameters are not known or are difficult to obtain. In this work, we pose a data driven capacity characterization method that does not require model information, it only needs access to a simulator. The capacity is characterized as the set of feasible spectral densities (SDs) of the demand deviation. The proposed method is an extension of our recent work on SD-based capacity characterization that was limited to the case where the loads dynamic model is completely known. Numerical evaluation of the method is provided, which compares our approach to the model-based solution of our past work.
Efficient MPI-based Communication for GPU-Accelerated Dask Applications

Dask is a popular parallel and distributed computing framework, which rivals Apache Spark to enable task-based scalable processing of big data. The Dask Distributed library forms the basis of this computing engine and provides support for adding new communication devices. It currently has two communication devices: one for TCP and the other for high-speed networks using UCX-Py—a Cython wrapper to UCX. This paper presents the design and implementation of a new communication backend for Dask—called MPI4Dask—that is targeted for modern HPC clusters built with GPUs. MPI4Dask exploits mpi4py over MVAPICH2-GDR, which is a GPU-aware implementation of the Message Passing Interface (MPI) standard. MPI4Dask provides point-to-point asynchronous I/O communication coroutines, which are non-blocking concurrent operations defined using the async/await keywords from the Python’s asyncio framework. Our latency and throughput comparisons suggest that MPI4Dask outperforms UCX by 6× for 1 Byte message and 4× for large messages (2 MBytes and beyond) respectively. We also conduct comparative performance evaluation of MPI4Dask with UCX using two benchmark applications: 1) sum of cuPy array with its transpose, and 2) cuDF merge. MPI4Dask speeds up the overall execution time of the two applications by an average of 3.47× and 3.11× respectively on an in-house cluster built with NVIDIA Tesla V100 GPUs for 1 – 6 Dask workers. We also perform scalability analysis of MPI4Dask against UCX for these applications on TACC’s Frontera (GPU) system with upto 32 Dask workers on 32 NVIDIA Quadro RTX 5000 GPUs and 256 CPU cores. MPI4Dask speeds up the execution time for cuPy and cuDF applications by an average of 1.71× and 2.91× respectively for 1 − 32 Dask workers on the Frontera (GPU) system.
Overview of the NASA TROPICS CubeSat Constellation Mission

Recent technology advances in miniature microwave radiometers that can be hosted on very small satellites has made possible a new class of affordable constellation missions that provide very high revisit rates of tropical cyclones and other severe weather. The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission was selected by NASA as part of the Earth Venture–Instrument (EVI-3) program and is now in development with planned launch readiness in late 2019. The overarching goal for TROPICS is to provide nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones (TCs). TROPICS will provide rapid-refresh microwave measurements (median refresh rate better than 60 minutes for the baseline mission) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. TROPICS will comprise a constellation of at least six CubeSats in three low-Earth orbital planes. Each CubeSat will host a high performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles and low-level moisture. This observing system offers an unprecedented combination of horizontal and temporal resolution in the microwave spectrum to measure environmental and inner-core conditions for TCs on a nearly global scale and is a major leap forward in the temporal resolution of several key parameters needed for assimilation into advanced data assimilation systems capable of utilizing rapid-update radiance or retrieval data. Here, we provide an overview of the mission and an update on current status, with a focus on unique characteristics of the Cubesat system, recent performance simulations on a range of observables to be provided by the constellation, and a summary of science applications.
A Homological Theory of Functions

In computational complexity, a complexity class is given by a set of problems or functions, and a basic challenge is to show separations of complexity classes $A \not= B$ especially when $A$ is known to be a subset of $B$. In this paper we introduce a homological theory of functions that can be used to establish complexity separations, while also providing other interesting consequences. We propose to associate a topological space $S_A$ to each class of functions $A$, such that, to separate complexity classes $A \subseteq B'$, it suffices to observe a change in "the number of holes", i.e. homology, in $S_A$ as a subclass $B$ of $B'$ is added to $A$. In other words, if the homologies of $S_A$ and $S_{A \cup B}$ are different, then $A \not= B'$. We develop the underlying theory of functions based on combinatorial and homological commutative algebra and Stanley-Reisner theory, and recover Minsky and Papert's 1969 result that parity cannot be computed by nonmaximal degree polynomial threshold functions. In the process, we derive a "maximal principle" for polynomial threshold functions that is used to extend this result further to arbitrary symmetric functions. A surprising coincidence is demonstrated, where the maximal dimension of "holes" in $S_A$ upper bounds the VC dimension of $A$, with equality for common computational cases such as the class of polynomial threshold functions or the class of linear functionals in $\mathbb F_2$, or common algebraic cases such as when the Stanley-Reisner ring of $S_A$ is Cohen-Macaulay. As another interesting application of our theory, we prove a result that a priori has nothing to do with complexity separation: it characterizes when a vector subspace intersects the positive cone, in terms of homological conditions. By analogy to Farkas' result doing the same with *linear conditions*, we call our theorem the Homological Farkas Lemma.
Self-employment and mental health

This paper analyzes the role of mental health in self-employment decisions. We find evidence of a relationship between psychological distress and self-employment for men that depends on type of self-employment and severity of psychological distress. Specifically, there is suggestive evidence of a causal link from moderate psychological distress to self-employment in an unincorporated business as a main job for men. Additionally, we find evidence that long term mental illness can significantly increase the probability of self-employment in an unincorporated business for both men and women. Our results suggest that individual difficulty in wage-and-salary employment is the likely mechanism for this connection.
Existence of the first magic angle for the chiral model of bilayer graphene

We consider the chiral model of twisted bilayer graphene introduced by Tarnopolsky, Kruchkov, and Vishwanath (TKV). TKV proved that for inverse twist angles α such that the effective Fermi velocity at the moire K point vanishes, the chiral model has a perfectly flat band at zero energy over the whole Brillouin zone. By a formal expansion, TKV found that the Fermi velocity vanishes at α ≈ 0.586. In this work, we give a proof that the Fermi velocity vanishes for at least one α between 0.57 and 0.61 by rigorously justifying TKV’s formal expansion of the Fermi velocity over a sufficiently large interval of α values. The idea of the proof is to project the TKV Hamiltonian onto a finite-dimensional subspace and then expand the Fermi velocity in terms of explicitly computable linear combinations of modes in the subspace while controlling the error. The proof relies on two propositions whose proofs are computer-assisted, i.e., numerical computation together with worst-case estimates on the accumulation of round-off error, which show that round-off error cannot possibly change the conclusion of computation. The propositions give a bound below on the spectral gap of the projected Hamiltonian, an Hermitian 80 × 80 matrix whose spectrum is symmetric about 0, and verify that two real 18-th order polynomials, which approximate the numerator of the Fermi velocity, take values with a definite sign when evaluated at specific values of α. Together with TKV’s work, our result proves the existence of at least one perfectly flat band of the chiral model.
WiFresh: Age-of-Information from Theory to Implementation

Emerging applications, such as smart factories and fleets of drones, increasingly rely on sharing time-sensitive information for monitoring and control. In such application domains, it is essential to keep information fresh, as outdated information loses its value and can lead to system failures and safety risks. The Age-of-Information is a performance metric that captures how fresh the information is from the perspective of the destination.In this paper, we show that as the congestion in the wireless network increases, the Age-of-Information degrades sharply, leading to outdated information at the destination. Leveraging years of theoretical research, we propose WiFresh: an unconventional architecture that achieves near optimal information freshness in wireless networks of any size, even when the network is overloaded. Our experimental results show that WiFresh can improve information freshness by two orders of magnitude when compared to an equivalent standard WiFi network. We propose and realize two strategies for implementing WiFresh: one at the MAC layer using hardware-level programming and another at the Application layer using Python.
Visual Communication of Object Concepts at Different Levels of Abstraction

Author(s): Yang, Justin; Fan, Judith E. | Abstract: People can produce drawings of specific entities (e.g., Garfield), as well as general categories (e.g., ``cat''). What explains this ability to produce such varied drawings of even highly familiar object concepts? We hypothesized that drawing objects at different levels of abstraction depends on both sensory information and representational goals, such that drawings intended to portray a recently seen object preserve more detail than those intended to represent a category. Participants drew objects cued either with a photo or a category label. For each cue type, half the participants aimed to draw a specific exemplar; the other half aimed to draw the category. We found that label-cued category drawings were the most recognizable at the basic level, whereas photo-cued exemplar drawings were the least recognizable. Together, these findings highlight the importance of task context for explaining how people use drawings to communicate visual concepts in different ways.
Open-access full-duplex wireless in the ORBIT and COSMOS testbeds

Abstract In order to support experimentation with full-duplex (FD) wireless, we integrated two generations of FD radios in the open-access ORBIT and COSMOS testbeds. In the indoor ORBIT testbed, we integrated a customized 1st generation (Gen-1) narrowband FD radio. In the city-scale PAWR COSMOS testbed, we integrated four 2nd generation (Gen-2) wideband FD radios, as well as static and mobile Gen-1 FD radios. Each integrated FD radio consists of an antenna, a customized RF self-interference (SI) canceller box, a USRP software-defined radio (SDR), and a remotely accessible compute node. The Gen-1/Gen-2 RF SI canceller box includes an RF canceller printed circuit board (PCB) which emulates a customized integrated circuit (IC) RF canceller implementation. The amplitude- and phase-based Gen-1 narrowband RF canceller achieves 45 dB RF SIC across 5 MHz bandwidth. The Gen-2 wideband canceller is based on the technique of frequency-domain equalization (FDE) and achieves 50 dB RF SI cancellation (SIC) across 20 MHz bandwidth. In this paper, we present the design of the two generations of FD radios, and their integration in various forms in the ORBIT and COSMOS testbeds. We then present example experiments that can be remotely run and modified by experimenters. Finally, we discuss future improvements and potential FD wireless experiments that can be supported by these open-access FD radios integrated in the COSMOS testbed.
The M-dwarf Ultraviolet Spectroscopic Sample. I. Determining Stellar Parameters for Field Stars

Accurate stellar properties are essential for precise stellar astrophysics and exoplanetary science. In the M-dwarf regime, much effort has gone into defining empirical relations that can use readily accessible observables to assess physical stellar properties. Often, these relations for the quantity of interest are cast as a non-linear function of available data; however, in Bayesian modeling the reverse is needed. In this article, we introduce a new Bayesian framework to self-consistently and simultaneously apply multiple empirical calibrations to fully characterize the mass, luminosity, radius, and effective temperature of a field age M-dwarf. This framework includes a new M-dwarf mass-radius relation with a scatter of 3.1% at fixed mass. We further introduce the M-dwarf Ultraviolet Spectroscopic Sample (MUSS), and apply our methodology to provide consistent stellar parameters for these nearby low-mass stars, selected as having available spectroscopic data in the ultraviolet. These targets are of interest largely as either exoplanet hosts or benchmarks in multi-wavelength stellar activity. We use the field MUSS stars to define a low-mass main sequence in the solar neighborhood through Gaussian Process (GP) regression. These results enable us to empirically measure a feature in the GP derivative at $\mathcal{M}_{b} = 0.337 \pm ^{0.013}_{0.026}$ $M_{\odot}$ that indicates where the M-dwarf Ultraviolet Spectroscopic Sample transitions from fully to partly convective interiors.
Changes in the distribution of fitness effects and adaptive mutational spectra following a single first step towards adaptation

Historical contingency and diminishing returns epistasis have been typically studied for relatively divergent genotypes and/or over long evolutionary timescales. Here, we use Saccharomyces cerevisiae to study the extent of diminishing returns and the changes in the adaptive mutational spectra following a single first adaptive mutational step. We further evolve three clones that arose under identical conditions from a common ancestor. We follow their evolutionary dynamics by lineage tracking and determine adaptive outcomes using fitness assays and whole genome sequencing. We find that diminishing returns manifests as smaller fitness gains during the 2nd step of adaptation compared to the 1st step, mainly due to a compressed distribution of fitness effects. We also find that the beneficial mutational spectra for the 2nd adaptive step are contingent on the 1st step, as we see both shared and diverging adaptive strategies. Finally, we find that adaptive loss-of-function mutations, such as nonsense and frameshift mutations, are less common in the second step of adaptation than in the first step. Analyses of both natural and experimental evolution suggest that adaptation depends on the evolutionary past and adaptive potential decreases over time. Here, by tracking yeast adaptation with DNA barcoding, the authors show that such evolutionary phenomena can be observed even after a single adaptive step.
Young adults recruit similar motor modules across walking, turning, and chair transfers

Moving about in the world during daily life requires executing and successfully shifting between a variety of functional tasks, such as rising from a chair or bed, walking, turning, and navigating stairs. Moreover, moving about during daily life requires not only navigating between different functional tasks, but also performing these tasks in the presence of mental distractions. However, little is known about underlying neuromuscular control for executing and shifting between these different tasks. In this study, we investigated muscle coordination across walking, turning, and chair transfers by applying motor module (a.k.a. muscle synergy) analysis to the Timed-Up-and-Go (TUG) test with and without a secondary cognitive dual task. We found that healthy young adults recruit a small set of common motor modules across the subtasks of the TUG test and that their composition is robust to cognitive distraction. Instead, cognitive distraction impacted motor module activation timings such that they became more consistent. This work is the first to demonstrate motor module generalization across multiple tasks that are both functionally different and crucial for healthy mobility. Overall, our results suggest that the central nervous system may draw from a "library" of modular control strategies to navigate the variety of movements and cognitive demands required of daily life.
The outer spectral radius and dynamics of completely positive maps

We examine a special case of an approximation of the joint spectral radius given by Blondel and Nesterov, which we call the outer spectral radius. The outer spectral radius is given by the square root of the ordinary spectral radius of the n2 by n2 matrix $$\sum \overline {{X_i}} \otimes {X_i}$$ . We give an analogue of the spectral radius formula for the outer spectral radius which can be used to quickly obtain the error bounds in methods based on the work of Blondel and Nesterov. The outer spectral radius is used to analyze the iterates of a completely positive map, including the special case of quantum channels. The average of the iterates of a completely positive map approach to a completely positive map where the Kraus operators span an ideal in the algebra generated by the Kraus operators of the original completely positive map. We also give an elementary treatment of Popescu’s theorems on similarity to row contractions in the matrix case, describe connections to the Parrilo-Jadbabaie relaxation, and give a detailed analysis of the maximal spectrum of a completely positive map.
A method of immersed layers on Cartesian grids, with application to incompressible flows

Abstract The immersed boundary method (IBM) of Peskin (J. Comput. Phys., 1977), and derived forms such as the projection method of Taira and Colonius (J. Comput. Phys., 2007), have been useful for simulating flow physics in problems with moving interfaces on stationary grids. However, in their interface treatment, these methods do not distinguish one side from the other, but rather, apply the motion constraint to both sides, and the associated interface force is an inseparable mix of contributions from each side. In this work, we define a discrete Heaviside function, a natural companion to the familiar discrete Dirac delta function (DDF), to define a masked version of each field on the grid which, to within the error of the DDF, takes the intended value of the field on the respective sides of the interface. From this foundation we develop discrete operators and identities that are uniformly applicable to any surface geometry. We use these to develop extended forms of prototypical partial differential equations, including Poisson, convection-diffusion, and incompressible Navier-Stokes, that govern the discrete masked fields. These equations contain the familiar forcing term of the IBM, but also additional terms that regularize the jumps in field quantities onto the grid and enable us to individually specify the constraints on field behavior on each side of the interface. Drawing the connection between these terms and the layer potentials in elliptic problems, we refer to them generically as immersed layers. We demonstrate the application of the method to several representative problems, including two-dimensional incompressible flows inside a rotating cylinder and external to a rotating square.
Detecting Anomalous Swarming Agents With Graph Signal Processing

Collective motion among biological organisms such as insects, fish, and birds has motivated considerable interest not only in biology but also in distributed robotic systems. In a robotic or biological swarm, anomalous agents (whether malfunctioning or nefarious) behave differently than the normal agents and attempt to hide in the “chaos” of the swarm. By defining a graph structure between agents in a swarm, we can treat the agents’ properties as a graph signal and use tools from the field of graph signal processing to understand local and global swarm properties. Here, we leverage this idea to show that anomalous agents can be effectively detected using their impacts on the graph Fourier structure of the swarm.
CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy Model

Energy management is a critical aspect of risk assessment for Uncrewed Aerial Vehicle (UAV) flights, as a depleted battery during a flight brings almost guaranteed vehicle damage and a high risk of human injuries or property damage. Predicting the amount of energy a flight will consume is challenging as routing, weather, obstacles, and other factors affect the overall consumption. We develop a deep energy model for a UAV that uses Temporal Convolutional Networks to capture the time varying features while incorporating static contextual information. Our energy model is trained on a real world dataset and does not require segregating flights into regimes. We illustrate an improvement in power predictions by 29% on test flights when compared to a state-of-the-art analytical method. Using the energy model, we can predict the energy usage for a given trajectory and evaluate the risk of running out of battery during flight. We propose using Conditional Value-at-Risk (CVaR) as a metric for quantifying this risk. We show that CVaR captures the risk associated with worst-case energy consumption on a nominal path by transforming the output distribution of Monte Carlo forward simulations into a risk space. Computing the CVaR on the risk-space distribution provides a metric that can evaluate the overall risk of a flight before take-off. Our energy model and risk evaluation method can improve flight safety and evaluate the coverage area from a proposed takeoff location. The video and codebase are available at: [Video]a | [Code]b
Neko: a Library for Exploring Neuromorphic Learning Rules

The field of neuromorphic computing is in a period of active exploration. While many tools have been developed to simulate neuronal dynamics or convert deep networks to spiking models, general software libraries for learning rules remain underexplored. This is partly due to the diverse, challenging nature of efforts to design new learning rules, which range from encoding methods to gradient approximations, from population approaches that mimic the Bayesian brain to constrained learning algorithms deployed on memristor crossbars. To address this gap, we present Neko, a modular, extensible library with a focus on aiding the design of new learning algorithms. We demonstrate the utility of Neko in three exemplar cases: online local learning, probabilistic learning, and analog on-device learning. Our results show that Neko can replicate the state-of-the-art algorithms and, in one case, lead to significant outperformance in accuracy and speed. Further, it offers tools including gradient comparison that can help develop new algorithmic variants. Neko is an open source Python library that supports PyTorch and TensorFlow backends.
Weak-Field Hall Resistivity and Spin-Valley Flavor Symmetry Breaking in Magic-Angle Twisted Bilayer Graphene.

Near a magic twist angle, the lowest energy conduction and valence bands of bilayer graphene moir\'e superlattices become extremely narrow. The band dispersion that remains is sensitive to the moir\'e's strain pattern, nonlocal tunneling between layers, and filling-factor-dependent Hartree and exchange band renormalizations. In this Letter, we analyze the influence of these band-structure details on the pattern of flavor symmetry breaking observed in this narrow band system and on the associated pattern of Fermi surface reconstructions revealed by weak-field Hall and Shubnikov--de Haas magnetotransport measurements.
Software and Data Provenance as a Basis for eScience Workflow

While tracking data and software provenance is necessary in eScience, and often implemented in scientific workflow management tools, such tools generally don’t provide graphical UIs that use provenance to guide workflow. Graphitti Workbench was built to center workflow around its PROV-DM based ProVis software and data provenance visualizer, making provenance more readily accessible to scientists. Using ProVis as the system desktop highlights the importance of provenance to inform future experiments and improves provenance understandability. Our usability study indicates that this solution is highly acceptable and that it provides easy deployment of experiments using provenance-based creation.
Delays in Banks’ Loan Loss Provisioning and Economic Downturns: Evidence from the U.S. Housing Market

I study whether banks’ loan loss provisioning contributed to economic downturns by examining the U.S. housing market. Specifically, I examine the aggregate effects of banks’ delayed loan loss recognition (DLR) on house prices during the Great Recession and the channels through which these potential effects arose. I construct ZIP-code-level exposure to banks’ DLR before the crisis and compare high- and low-exposure ZIP codes during the crisis to examine the aggregate effects of banks’ DLR on the housing market. I find that high-exposure ZIP codes experienced larger decreases in mortgage supply, larger increases in distressed sales, and larger decreases in house prices during the crisis. In addition, I conduct individual bank-level analyses and find that high-DLR banks reduced their mortgage supply more than low-DLR banks, and mortgages issued by high-DLR banks were more likely to become distressed during the crisis. These findings suggest that the aggregate effects on house prices were likely driven by both the credit-crunch and distressed-sales channels.
Probabilistic model for dynamic galaxy decomposition

In the era of precision cosmology and ever-improving cosmological simulations, a better understanding of different galaxy components such as bulges and discs will give us new insight into galactic formation and evolution. Based on the fact that the stellar populations of the constituent components of galaxies differ by their dynamical properties, we develop two simple models for galaxy decomposition using the IllustrisTNG cosmological hydrodynamical simulation. The first model uses a single dynamical parameter and can distinguish 4 components: thin disc, thick disc, counter-rotating disc and bulge. The second model uses one more dynamical parameter, was defined in a probabilistic manner, and distinguishes two components: bulge and disc. The number fraction of disc-dominated galaxies at a given stellar mass obtained by our models agrees well with observations for masses exceeding $ \log_{10}(M_*/M_\odot)=10$. Sersic indices and half-mass radii for the bulge components agree well with those for real galaxies. The mode of the distribution of Sersic indices for the disc components is at the expected value of $n=1$. However, disc half-mass radii are smaller than those for real galaxies, in accordance with previous findings that the IllustrisTNG simulation produces undersized discs.
From Noise to Bias: Overconfidence in New Product Forecasting

We study decision behavior in the selection, forecasting, and production for a new product. In a stylized behavioral model and five experiments, we generate new insight into when and why this combi...
Strategic Behavior is Bliss: Iterative Voting Improves Social Welfare

Recent work in iterative voting has defined the difference in social welfare between the truthful winner and worst-case equilibrium winner, due to repeated strategic manipulations, known as the additive dynamic price of anarchy (ADPoA). While all iterative plurality winners have been shown to differ from truth by at most one initial vote, it is less understood how agents' welfare changes in equilibrium. To this end, we differentiate agents' utility from their iteration mechanism and determine iterative plurality's ADPoA in the worst- and average-case. We first negatively demonstrate that the worst-case ADPoA is linear in the number of agents. In expectation, rather, equilibrium winners have a constant order welfare advantage over the truthful winner. Our positive results illustrate the prospect for social welfare to increase due to strategic manipulation.
Determinants of Ethnic Differences in School Modality Choices During the COVID-19 Crisis

A growing body of research and popular reporting shows racial differences in school modality choices during the COVID-19 crisis, with White students more likely to attend school in person in the fa...
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning

Gradient inversion attack (or input recovery from gradient) is an emerging threat to the security and privacy preservation of Federated learning, whereby malicious eavesdroppers or participants in the protocol can recover (partially) the clients' private data. This paper evaluates existing attacks and defenses. We find that some attacks make strong assumptions about the setup. Relaxing such assumptions can substantially weaken these attacks. We then evaluate the benefits of three proposed defense mechanisms against gradient inversion attacks. We show the trade-offs of privacy leakage and data utility of these defense methods, and find that combining them in an appropriate manner makes the attack less effective, even under the original strong assumptions. We also estimate the computation cost of end-to-end recovery of a single image under each evaluated defense. Our findings suggest that the state-of-the-art attacks can currently be defended against with minor data utility loss, as summarized in a list of potential strategies. Our code is available at: https://github.com/Princeton-SysML/GradAttack.
ER-localized phosphatidylethanolamine synthase plays a conserved role in lipid droplet formation.

The asymmetric distribution of phospholipids in membranes is a fundamental principle of cellular compartmentalization and organization. Phosphatidylethanolamine (PE), a nonbilayer phospholipid that contributes to organelle shape and function, is synthesized at several subcellular localizations via semi-redundant pathways. Previously, we demonstrated in budding yeast that the PE synthase Psd1, which primarily operates on the mitochondrial inner membrane, is additionally targeted to the ER. While ER-localized Psd1 is required to support cellular growth in the absence of redundant pathways, its physiological function is unclear. We now demonstrate that ER-localized Psd1 sub-localizes on the ER to lipid droplet (LD) attachment sites and show it is specifically required for normal LD formation. We also find that the role of phosphatidylserine decarboxylase (PSD) enzymes in LD formation is conserved in other organisms. Thus, we have identified PSD enzymes as novel regulators of LDs and demonstrate that both mitochondria and LDs in yeast are organized and shaped by the spatial positioning of a single PE synthesis enzyme.
A study of asphalt aging using Indirect Tensile Strength test

Abstract While dynamic modulus, diametrical resilient modulus, and loss of ductility are the most common parameters to study aging, this study determines laboratory equivalence of field aging using an Indirect Tensile Strength (ITS) test for its simplicity and wide usages in Asphalt Concrete (AC) performance evaluation. Cylindrical samples were compacted in the laboratory, aged in the laboratory and field, and then loaded diametrically to determine ITS value and flow number. The ITS tests were conducted on six sets of compacted samples after subjecting them to 1, 5, 10, 15, 20, and 25 days of oven aging at 85 °C in the laboratory and on 11 sets of compacted samples after subjecting them to 1, 2, 3, and up to 12-months of field aging. The ITS tests were also performed on a third set of samples whose loose mixes were subjected to 8, 16, 32, 48, 72 and 100 h oven aging at 135 °C. As expected, ITS of the laboratory (both compacted and loose) and field aged samples increase and flow number decrease with the aging period. It is found in this study that one-day laboratory aging is close to approximately one-year of field aging measured in terms of ITS value. Results from loose mix aging show that the ITS value increases with the conditioning period, reaches a peak and then decreases with the conditioning period. Overall, the flow number decreases as aging intensity increases, that is, the brittleness increases with aging.
Dielectric characterization and molecular interaction behaviour in binary mixture of 1,2-diaminopropane with 2-aminoethanol

The dielectric dispersionand dielectric loss �� of binary mixture of 1,2-diaminopropane - 2-aminoethanol were measured by employing the Time Domain Reflectometry technique over a frequency range 10 MHz to 20 GHz at 15, 25, 35 and 45 ◦ C. To evaluate various dielectric parameters, the frequency dependents complex permittivity ( *( ω )= � -j �� ) data such as static permittivity (s), relaxation time (τ) and permittivity at high frequency (∞) were fitted by the non-linear least-squares fit method to Debye expression. Temperature dependent s, τ, Kirkwood correlation factor (g), free energy of activation (� G) and enthalpy of activation (� H) have been determined and discussed in terms of the effect of -NH2 and -CH3 side-group on molecular dynamics and intermolecular hydrogen bonds. It is appropriate to report the present work on the dielectric behavior of 1,2-diaminopropane and 2-aminoethanol liquid molecules, and also their conformations, because the dielectric behavior and molecular dynamics of the system under study can be explored more clearly only by comparing the dielectric data of the mixture system with the dielectric data of the individual molecules and their dynamics.
Education Matters: Beyond the Fetishization of the Banking Model

One of the most formidable challenges of the twenty-first century is the recommitment to the principle that education matters—an education that, according to Zygmunt Bauman, ceases to promote, “as binding rules of [its] own discourse, the inadmissibility of ethical problematic in any other form but that of a communallysustained ideology and thus heterogeneous to sociological scientific, rational discourse” — a discourse that is overly concerned with the positivistic overemphasis on the socalled scientific methods and absolute objectivity that informs the current reform in education. The current move to blindly promote “research-based” instructional methods and the general tendency to accept only “scientifically proven” methods epitomizes the rigidity of the educational reform discourse during the past two decades or so. The attempts of educators to adopt “hard science” modes of analysis as part of their research in social sciences and education have given rise to a form of “scientism” rather than science. By “scientism” I refer to a process whereby educators engage in the mechanization of the intellectual work which often leads to the fragmentation of knowledge—a process through which “[p]hrases like the ‘sanctity of human life’ or ‘moral duty’ sound as alien in a sociology seminar as they do in the smoke-free, sanitized rooms of a bureaucratic office.” Hence, education that fragments bodies of knowledge is not only education that domesticates but it also preponderantly markets literacy for stupidification under the rubric of democracy and the refusal to admit that a claim of objectivity is, in fact, an ideological act—an act that attempts to relegate citizens as bystanders and spectators in the farcical elections increasingly controlled by corporations-turned-people whose freedom of expression is protected by the Supreme Court as they funnel obscene amounts of money to highjack the electoral process and buy politicians who will ultimately serve the interests of the moneyed minority and work against the interests of the ever-increasing dispossessed majority. The stupidification process is, in turn, legitimized through the vulgarization of democracy by the dominant ideology as witnessed by the remarks of the Secretary of State, John Kerry, who exalted