cssi/SciSciGPT-SciSciNet huggingface dataset:
HONG KONG OUTPUT DYNAMICS: AN EMPIRICAL ANALYSIS

Advanced statistical techniques are used to analyze Hong Kong output dynamics. Hong Kong, Japan and the US are found to share some common long-term and short-term cyclical variations. While the Hong Kong economy is susceptible to external shocks and Granger-caused by the other two economies, local factors account for a large proportion of output growth variability and uncertainty. On the transmission mechanism, the selected trade and financial variables have incremental explanatory power but do not lessen the ability of lagged output variables to explain Hong Kong growth dynamics. Interestingly, the US does not appear to exert undue influences on Hong Kong.
Daily Data is Bad for Beta: Opacity and Frequency-Dependent Betas

A stock’s market exposure, beta, varies across return frequencies. Sorting stocks on the difference between low- and high-frequency betas (Δβ) yields large systematic mispricings relative to the CAPM at high frequencies, but significantly smaller mispricings at low frequencies. We provide a risk-based explanation for this frequency dependence by introducing uncertainty about the effect of systematic news on firm value (opacity) into a frictionless model. We document a robust relationship between the frequency dependence of betas and proxies for opacity. Our findings suggest that opacity poses significant challenges to using betas estimated from high-frequency returns. While the CAPM may be an appropriate asset pricing model at low frequencies, additional factors, e.g., based on opacity, are necessary at high frequencies.
Let's Get Married: An Essay in Honor of Mari Matsuda

I analyze a number of prominent arguments against gay marriage and find them wanting, particularly the one that asserts that allowing same-sex couples to wed would destroy the institution of marriage.
In Defense of Asbestos Tort Litigation: Rethinking Legal Process Analysis in a World of Uncertainty, Second Bests, and Shared Policy‐Making Responsibility

A central question in American policy making is when should courts address complex policy issues, as opposed to defer to other forums? Legal process analysis offers a standard answer. It holds that judges should act when adjudication offers advantages over other modes of social ordering such as contracts, legislation, or agency rule making. From this vantage, the decision to use common law adjudication to address a sprawling public health crisis was a terrible mistake, as asbestos litigation has come to represent the very worst of mass tort litigation. This article questions this view, arguing that legal process analysis distorts the institutional choices underlying the American policy‐making process. Indeed, once one considers informational and political constraints, as well as how the branches of government can fruitfully share policy‐making functions, the asbestos litigation seems a reasonable and, in some ways, exemplary, use of judicial power.
Optimal portfolio under ambiguous ambiguity

A prominent approach to modelling ambiguity about stock return distribution is to assume that investors have multiple priors about the distribution and these priors are distributed according to a certain second-order distribution. Realistically, investors may also have multiple priors about the second-order distribution, thus allowing for ambiguous ambiguity. Despite a long history of debates about this idea (Reichenbach [1949], Savage [1954]), there seems to be no formal analysis of investment behavior in the presence of this feature. We develop a tractable portfolio choice framework incorporating ambiguous ambiguity, characterize analytically the optimal portfolio, and examine its properties.
Fast Spatial Autocorrelation

Physical or geographic location proves to be an important feature in many data science models, because many diverse natural and social phenomenon have a spatial component. Spatial autocorrelation measures the extent to which locally adjacent observations of the same phenomenon are correlated. Although statistics like Moran's $I$ and Geary's $C$ are widely used to measure spatial autocorrelation, they are slow: all popular methods run in $\Omega(n^{2})$ time, rendering them unusable for large data sets, or long time-courses with moderate numbers of points. We propose a new $S_{A}$ statistic based on the notion that the variance observed when merging pairs of nearby clusters should increase slowly for spatially autocorrelated variables. We give a linear-time algorithm to calculate $S_{A}$ for a variable with an input agglomeration order (available at https://github.com/aamgalan/spatial_autocorrelation). For a typical dataset of $n\approx 63,000$ points, our $S_{A}$ autocorrelation measure can be computed in 1 second, versus 2 hours or more for Moran's $I$ and Geary's $C$ . Through simulation studies, we demonstrate that $S_{A}$ identifies spatial correlations in variables generated with spatially-dependent model half an order of magnitude earlier than either Moran's $I$ or Geary's $C$ . Finally, we prove several theoretical properties of $S_{A}$ : namely that it behaves as a true correlation statistic, and is invariant under addition or multiplication by a constant.
Anisotropic swelling of anisotropic elastic panels

While isotropic in-plane swelling problems for thin elastic sheets have been studied extensively in recent years, many shape-programmable materials, including nematic solids and 3D-printed structures, are anisotropic, as are most industrial sheet materials. In this theoretical work, we consider central swelling and shrinkage of plates of aspect ratio and material properties relevant to the manufacture of engineered wood composite panels in which both in-plane swelling and material stiffness are highly orthotropic, leading to multiple separations in energy scales. With transverse swelling in the soft direction, and gradients in the stiff direction, the warped plates adopt two distinct types of configurations, axisymmetric and twisted, which we illustrate with toy models. We employ a two-parameter family of isometries to embed the metric programmed by the swelling, thus reducing the problem to one of minimizing bending energy alone. A simple argument is seen to closely predict averaged axisymmetric curvatures. While purely cylindrical shapes are unobtainable by pure in-plane swelling, they can be closely approximated in a highly anisotropic system. However, anisotropy can favor twisting, and breaks a degenerate soft deformation mode associated with minimal surfaces in isotropic materials. Bifurcations from axisymmetric to twisted shapes can be induced by anisotropy or by certain attributes of a central shrinkage profile. Finally, we note how our findings indicate practical limitations on the diagnosis of moisture inhomogeneities in manufactured panels by observation of warped conformations, due to the sensitivity of the qualitative response to specifics of the profile.
Dynamic Legged Manipulation of a Ball Through Multi-Contact Optimization

The feet of robots are typically used to design locomotion strategies, such as balancing, walking, and running. However, they also have great potential to perform manipulation tasks. In this paper, we propose a model predictive control (MPC) framework for a quadrupedal robot to dynamically balance on a ball and simultaneously manipulate it to follow various trajectories such as straight lines, sinusoids, circles and in-place turning. We numerically validate our controller on the Mini Cheetah robot using different gaits including trotting, bounding, and pronking on the ball.
Fully Convolutional Geometric Features for Category-level Object Alignment

This paper focuses on pose registration of different object instances from the same category. This is required in online object mapping because object instances detected at test time usually differ from the training instances. Our approach transforms instances of the same category to a normalized canonical coordinate frame and uses metric learning to train fully convolutional geometric features. The resulting model is able to generate pairs of matching points between the instances, allowing category-level registration. Evaluation on both synthetic and real-world data shows that our method provides robust features, leading to accurate alignment of instances with different shapes.
Botnet Detection Using Recurrent Variational Autoencoder

Botnet detection is an active research topic as botnets are a source of many malicious activities, including distributed denial-of-service (DDoS), click-fraud, spamming, and crypto-mining attacks. However, it is getting more complicated to identify botnets due to the continuous evolution of botnet software and families that harness new types of devices and attack vectors. Recent studies employing machine learning (ML) showed improved performance to detect botnets to some extent, but they are still limited and ineffective with the lack of sequential pattern analysis, which is a key to detect various classes of botnets. In this paper, we propose a novel botnet detection method, built upon Recurrent Variational Autoencoder (RVAE), that effectively captures sequential characteristics of botnet anomalies. We validate the feasibility of the proposed method with the CTU-13 dataset that have been widely employed for botnet detection studies, and show that our method is at least comparable to existing techniques in terms of detection accuracy. In addition, our experimental results show that the proposed method can detect previously unseen botnets by utilizing sequential patterns of network traffic. We will also show how our method can detect botnets in the streaming mode, which is the essential requirement to perform real-time, on-line detection.
Deferred Acceptance with Compensation Chains

In a foundational paper, Gale and Shapley (1962) introduced the deferred acceptance algorithm that achieves a stable outcome in a two-sided matching market by letting one side of the market make pr...
A collision-resilient aerial vehicle with icosahedron tensegrity structure

Aerial vehicles with collision resilience can operate with more confidence in environments with obstacles that are hard to detect and avoid. This paper presents the methodology used to design a collision resilient aerial vehicle with icosahedron tensegrity structure. A simplified stress analysis of the tensegrity frame under impact forces is performed to guide the selection of its components. In addition, an autonomous controller is presented to reorient the vehicle from an arbitrary orientation on the ground to help it take off. Experiments show that the vehicle can successfully reorient itself after landing upside-down and can survive collisions with speed up to 6.5m/s.
The Catenary Robot: Design and Control of a Cable Propelled by Two Quadrotors

Transporting objects using aerial robots has been widely studied in the literature. Still, those approaches always assume that the connection between the quadrotor and the load is made in a previous stage. However, that previous stage usually requires human intervention, and autonomous procedures to locate and attach the object are not considered. Additionally, most of the approaches assume cables as rigid links, but manipulating cables requires considering the state when the cables are hanging. In this work, we design and control a catenary robot . Our robot is able to transport hook-shaped objects in the environment. The robotic system is composed of two quadrotors attached to the two ends of a cable. By defining the catenary curve with five degrees of freedom, position in 3-D, orientation in z-axis, and span, we can drive the two quadrotors to track a given trajectory. We validate our approach with simulations and real robots. We present four different scenarios of experiments. Our numerical solution is computationally fast and can be executed in real-time.
Learning lenient parsing & typing via indirect supervision

Both professional coders and teachers frequently deal with imperfect (fragmentary, incomplete, ill-formed) code. Such fragments are common in StackOverflow; students also frequently produce ill-formed code, for which instructors, TAs (or students themselves) must find repairs. In either case, the developer experience could be greatly improved if such code could somehow be parsed & typed; this makes such code more amenable to use within IDEs and allows early detection and repair of potential errors. We introduce a lenient parser, which can parse & type fragments, even ones with simple errors. Training a machine learner to leniently parse and type imperfect code requires a large training set including many pairs of imperfect code and its repair (and/or type information); such training sets are limited by human effort and curation. In this paper, we present a novel, indirectly supervised, approach to train a lenient parser, without access to such human-curated training data. We leverage the huge corpus of mostly correct code available on Github, and the massive, efficient learning capacity of Transformer-based NN architectures. Using GitHub data, we first create a large dataset of fragments of code and corresponding tree fragments and type annotations; we then randomly corrupt the input fragments (while requiring correct output) by seeding errors that mimic corruptions found in StackOverflow and student data. Using this data, we train high-capacity transformer models to overcome both fragmentation and corruption. With this novel approach, we can achieve reasonable performance on parsing & typing StackOverflow fragments; we also demonstrate that our approach performs well on shorter student error program and achieves best-in-class performance on longer programs that have more than 400 tokens. We also show that by blending DeepFix and our tool, we could achieve 77% accuracy, which outperforms all previously reported student error correction tools.
LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud Segmentation

3D point cloud segmentation is an important function that helps robots understand the layout of their surrounding environment and perform tasks such as grasping objects, avoiding obstacles, and finding landmarks. Current segmentation methods are mostly class-specific, many of which are tuned to work with specific object categories and may not be generalizable to different types of scenes. This research proposes a learnable region growing method for class-agnostic point cloud segmentation, specifically for the task of instance label prediction. The proposed method is able to segment any class of objects using a single deep neural network without any assumptions about their shapes and sizes. The deep neural network is trained to predict how to add or remove points from a point cloud region to morph it into incrementally more complete regions of an object instance. Segmentation results on the S3DIS and ScanNet datasets show that the proposed method outperforms competing methods by 1%-9% on 6 different evaluation metrics.
A genome-wide copper-sensitized screen identifies novel regulators of mitochondrial cytochrome c oxidase activity

Copper is essential for the activity and stability of cytochrome c oxidase (CcO), the terminal enzyme of the mitochondrial respiratory chain. Loss-of-function mutations in genes required for copper transport to CcO result in fatal human disorders. Despite the fundamental importance of copper in mitochondrial and organismal physiology, systematic identification of genes that regulate mitochondrial copper homeostasis is lacking. To discover these genes, we performed a genome-wide screen using a library of DNA-barcoded yeast deletion mutants grown in copper-supplemented media. Our screen recovered a number of genes known to be involved in cellular copper homeostasis as well as genes previously not linked to mitochondrial copper biology. These newly identified genes include the subunits of the adaptor protein 3 complex (AP-3) and components of the cellular pH-sensing pathway Rim20 and Rim21, both of which are known to affect vacuolar function. We find that AP-3 and Rim mutants exhibit decreased vacuolar acidity, which in turn perturbs mitochondrial copper homeostasis and CcO function. CcO activity of these mutants could be rescued by either restoring vacuolar pH or supplementing growth media with additional copper. Consistent with these genetic data, pharmacological inhibition of the vacuolar proton pump leads to decreased mitochondrial copper content and a concomitant decrease in CcO abundance and activity. Taken together, our study uncovered novel genetic regulators of mitochondrial copper homeostasis and provided a mechanism by which vacuolar pH impacts mitochondrial respiration through copper homeostasis.
Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise.

A fundamental step in many data-analysis techniques is the construction of an affinity matrix describing similarities between data points. When the data points reside in Euclidean space, a widespre...
The parity operator: Applications in quantum metrology

In this paper, the authors review the use of parity as a detection observable in quantum metrology and introduce some original findings with regard to measurement resolution in Ramsey spectroscopy and quantum nondemolition measures of atomic parity. Parity was first introduced in the context of Ramsey spectroscopy as an alternative to atomic state detection. It was later adapted for use in quantum optical interferometry where it has been shown to be the optimal detection observable saturating the quantum Cramer–Rao bound for path symmetric states. The authors include a brief review on the basics of phase estimation and the connection between parity-based detection and the quantum Fisher information as it applies to quantum optical interferometry. The authors also discuss the efforts made in experimental methods of measuring photon-number parity and close the paper with a discussion on the use of parity, leading to enhanced measurement resolution in multi-atom spectroscopy. The authors show how this may be of use in the construction of high-precision multi-atom atomic clocks.
Berezinskii-Kosterlitz-Thouless phase transition from lattice sine-Gordon model

We obtain nonperturbative results on the sine-Gordon model using the lattice field technique. In particular, we employ the Fourier accelerated hybrid Monte Carlo algorithm for our studies. We find the critical temperature of the theory based autocorrelation time, as well as the finite size scaling of the “thickness” observable used in an earlier lattice study by Hasenbusch et al.
A Predictive Reaction-Diffusion Based Model of E.coli Colony Growth Control

Bacterial colony formations exhibit diverse morphologies and dynamics. A mechanistic understanding of this process has broad implications to ecology and medicine. However, many control factors and their impacts on colony formation remain underexplored. Here we propose a reaction-diffusion based dynamic model to quantitatively describe cell division and colony expansion, where control factors of colony spreading take the form of nonlinear density-dependent function and the intercellular impacts take the form of density-dependent hill function. We validate the model using experimental E. coli colony growth data and our results show that the model is capable of predicting the whole colony expansion process in both time and space under different conditions. Furthermore, the nonlinear control factors can predict colony morphology at both center and edge of the colony.
AI-powered effective lens position prediction improves the accuracy of existing lens formulas

AIMS To assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves the refraction prediction performance of existing intraocular lens (IOL) calculation formulas. METHODS A dataset of 4806 patients with cataract was gathered at the Kellogg Eye Center, University of Michigan, and split into a training set (80% of patients, 5761 eyes) and a testing set (20% of patients, 961 eyes). A previously developed ML-based method was used to predict the postoperative ACD based on preoperative biometry. This ML-based postoperative ACD was integrated into new effective lens position (ELP) predictions using regression models to rescale the ML output for each of four existing formulas (Haigis, Hoffer Q, Holladay and SRK/T). The performance of the formulas with ML-modified ELP was compared using a testing dataset. Performance was measured by the mean absolute error (MAE) in refraction prediction. RESULTS When the ELP was replaced with a linear combination of the original ELP and the ML-predicted ELP, the MAEs±SD (in Diopters) in the testing set were: 0.356±0.329 for Haigis, 0.352±0.319 for Hoffer Q, 0.371±0.336 for Holladay, and 0.361±0.331 for SRK/T which were significantly lower (p<0.05) than those of the original formulas: 0.373±0.328 for Haigis, 0.408±0.337 for Hoffer Q, 0.384±0.341 for Holladay and 0.394±0.351 for SRK/T. CONCLUSION Using a more accurately predicted postoperative ACD significantly improves the prediction accuracy of four existing IOL power formulas.
Report on the Progress in Stellar Evolution to 1950

At a joint meeting of the American Astronomical Society and the American Physical Society held in June of 1940, the University of Michigan astronomer Dean McLaughlin (1940) gave a review of the current understanding of stellar evolution. At the end he somewhat facetiously remarked that, “For several years I have told students that I knew all about stellar evolution in 1923, less is 1925, and nothing at all since 1930.” I would like to suggest that those dates were not chosen randomly, and in the first part of my own survey of stellar evolution to 1950, I would like to explain the significance of those dates.
Optimal Transport Based Filtering with Nonlinear State Equality Constraints

Abstract In this work we propose a framework to address the issue of state dependent nonlinear equality-constrained state estimation using Bayesian filtering. This framework is constructed specifically for a linear approximation of Bayesian filtering that uses the theory of Optimal Transport. As a part of this framework, we present three traditionally-used nonlinear equality constraint-preserving algorithms coupled with the Optimal Transport based filter: the equality-constrained Optimal Transport filter, the projected Optimal Transport filter, and the measurement-augmented Optimal Transport filter. In cases where the nonlinear equality-constraints represent an arbitrary convex manifold, we show that the re-sampling step of Optimal Transport filter, can generate initial samples for filtering, from any probability distribution function defined on this manifold. We show numerical results using our proposed framework.
Stock Return Extrapolation, Option Prices, and Variance Risk Premium

This paper presents a tractable dynamic equilibrium model of stock return extrapolation in the presence of stochastic volatility. In the model, consistent with survey evidence, following positive (negative) stock returns, investors expect future returns to be higher (lower) but also less (more) volatile. The biased volatility expectation introduces a new channel through which past returns and investor sentiment affect derivative prices. In particular, through this novel channel, the model reconciles the otherwise puzzling evidence of past returns affecting option prices and the evidence of variance risk premium predicting future stock market returns even after controlling for the realized variance.
A Verified Messaging System

We present a concurrent-read exclusive-write buffer system with strong correctness and security properties. Our motivating application for this system is the distribution of sensor values in a multicomponent vehicle-control system, where some components are unverified and possibly malicious, and other components are vehicle-control-critical and must be verified. Valid participants are guaranteed correct communication (i.e., the writer is always able to write to an unused buffer, and readers always read the most recently published value), while invalid readers or writers cannot compromise the correctness or liveness of valid participants. There is only one writer, all operations are wait-free, and there is no extra process or thread mediating communication. We prove the correctness of the system with valid participants by formally verifying a C implementation of the system in Coq, using the Verified Software Toolchain extended with an atomic exchange operation. The result is the first C-level mechanized verification of a nonblocking communication protocol.
The overfitted brain: Dreams evolved to assist generalization

Summary Understanding of the evolved biological function of sleep has advanced considerably in the past decade. However, no equivalent understanding of dreams has emerged. Contemporary neuroscientific theories often view dreams as epiphenomena, and many of the proposals for their biological function are contradicted by the phenomenology of dreams themselves. Now, the recent advent of deep neural networks (DNNs) has finally provided the novel conceptual framework within which to understand the evolved function of dreams. Notably, all DNNs face the issue of overfitting as they learn, which is when performance on one dataset increases but the network's performance fails to generalize (often measured by the divergence of performance on training versus testing datasets). This ubiquitous problem in DNNs is often solved by modelers via "noise injections" in the form of noisy or corrupted inputs. The goal of this paper is to argue that the brain faces a similar challenge of overfitting and that nightly dreams evolved to combat the brain's overfitting during its daily learning. That is, dreams are a biological mechanism for increasing generalizability via the creation of corrupted sensory inputs from stochastic activity across the hierarchy of neural structures. Sleep loss, specifically dream loss, leads to an overfitted brain that can still memorize and learn but fails to generalize appropriately. Herein this "overfitted brain hypothesis" is explicitly developed and then compared and contrasted with existing contemporary neuroscientific theories of dreams. Existing evidence for the hypothesis is surveyed within both neuroscience and deep learning, and a set of testable predictions is put forward that can be pursued both in vivo and in silico.
AutoDS: Towards Human-Centered Automation of Data Science

Data science (DS) projects often follow a lifecycle that consists of laborious tasks for data scientists and domain experts (e.g., data exploration, model training, etc.). Only till recently, machine learning(ML) researchers have developed promising automation techniques to aid data workers in these tasks. This paper introduces AutoDS, an automated machine learning (AutoML) system that aims to leverage the latest ML automation techniques to support data science projects. Data workers only need to upload their dataset, then the system can automatically suggest ML configurations, preprocess data, select algorithm, and train the model. These suggestions are presented to the user via a web-based graphical user interface and a notebook-based programming user interface. Our goal is to offer a systematic investigation of user interaction and perceptions of using an AutoDS system in solving a data science task. We studied AutoDS with 30 professional data scientists, where one group used AutoDS, and the other did not, to complete a data science project. As expected, AutoDS improves productivity; Yet surprisingly, we find that the models produced by the AutoDS group have higher quality and less errors, but lower human confidence scores. We reflect on the findings by presenting design implications for incorporating automation techniques into human work in the data science lifecycle.
On Large $N$ Limit of Symmetric Traceless Tensor Models

For some theories where the degrees of freedom are tensors of rank 3 or higher, there exist solvable large N limits dominated by the melonic diagrams. Simple examples are provided by models containing one rank 3 tensor in the tri-fundamental representation of the O(N)3 symmetry group. When the quartic interaction is assumed to have a special tetrahedral index structure, the coupling constant g must be scaled as N −3/2 in the melonic large N limit. In this paper we consider the combinatorics of a large N theory of one fully symmetric and traceless rank-3 tensor with the tetrahedral quartic interaction; this model has a single O(N ) symmetry group. We explicitly calculate all the vacuum diagrams up to order g 8, as well as some diagrams of higher order, and find that in the large N limit where g 2 N 3 is held fixed only the melonic diagrams survive. While some non-melonic diagrams are enhanced in the O(N ) symmetric theory compared to the O(N )3 one, we have not found any diagrams where this enhancement is strong enough to make them comparable with the melonic ones. Motivated by these results, we conjecture that the model of a real rank-3 symmetric traceless tensor possesses a smooth large N limit where g 2 N 3 is held fixed and all the contributing diagrams are melonic. A feature of the symmetric traceless tensor models is that some vacuum diagrams containing odd numbers of vertices are suppressed only by N −1/2 relative to the melonic graphs.
Scalable Graph Neural Network Training: The Case for Sampling

Graph Neural Networks (GNNs) are a new and increasingly popular family of deep neural network architectures to perform learning on graphs. Training them efficiently is challenging due to the irregular nature of graph data. The problem becomes even more challenging when scaling to large graphs that exceed the capacity of single devices. Standard approaches to distributed DNN training, like data and model parallelism, do not directly apply to GNNs. Instead, two different approaches have emerged in the literature: whole-graph and sample-based training. In this paper, we review and compare the two approaches. Scalability is challenging with both approaches, but we make a case that research should focus on sample-based training since it is a more promising approach. Finally, we review recent systems supporting sample-based training.
Induction of Ventral Spinal V0 Interneurons from Mouse Embryonic Stem Cells.

The ventral spinal population of V0 interneurons (INs) contributes to the coordinated movements directed by spinal central pattern generators (CPGs), including respiratory circuits and left-right a...
FLOP: Federated Learning on Medical Datasets using Partial Networks

The outbreak of COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. To aid and accelerate the diagnosis process, automatic diagnosis of COVID-19 via deep learning models has recently been explored by researchers across the world. While different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19, the data itself is still scarce due to patient privacy concerns. Federated Learning (FL) is a natural solution because it allows different organizations to cooperatively learn an effective deep learning model without sharing raw data. However, recent studies show that FL still lacks privacy protection and may cause data leakage. We investigate this challenging problem by proposing a simple yet effective algorithm, named Federated Learning on Medical Datasets using Partial Networks (FLOP), that shares only a partial model between the server and clients. Extensive experiments on benchmark data and real-world healthcare tasks show that our approach achieves comparable or better performance while reducing the privacy and security risks. Of particular interest, we conduct experiments on the COVID-19 dataset and find that our FLOP algorithm can allow different hospitals to collaboratively and effectively train a partially shared model without sharing local patients' data.
Mode structure of a broadband high gain parametric amplifier

This paper investigates the change of the mode structure of a parametric process as the gain changes and finds that the fields will spread out across many modes and the mode functions are broadened as the gain increases.
Representability and Compactness for Pseudopowers

We prove a compactness theorem for pseudopower operations of the form $${{\,\mathrm{pp}\,}}_{\Gamma (\mu ,\sigma )}(\mu )$$ where $$\aleph _0<\sigma ={{\,\mathrm{cf}\,}}(\sigma )\le {{\,\mathrm{cf}\,}}(\mu )$$ . Our main tool is a result that has Shelah’s cov versus pp Theorem as a consequence. We also show that the failure of compactness in other situations has significant consequences for pcf theory, in particular, implying the existence of a progressive set A of regular cardinals for which $${{\,\mathrm{pcf}\,}}(A)$$ has an inaccessible accumulation point.
Transforming the deep space network into the Interplanetary Network

Abstract Space exploration missions are undergoing a significant transformation as are the expectations of their scientific investigators and the public who participate in these great voyages of exploration. The early reconnaissance missions are giving way to a new data-intensive era of long duration observational outposts, landed vehicles, sample returns, and multi-spacecraft fleets and constellations. Mars exploration has already become a special case of the new operational mode; other destinations will follow. These changes will require orders of magnitude increases in data rates, highly automated and standardized data communications between the remote locations and Earth, more transparent and responsive mission operations procedures, and the ability to engage the public by giving them Internet-based visibility into the missions as they unfold. The new area will demand a new paradigm for the Deep Space Network, with increased emphasis on data networking and the data processing applications that allow users to become more intimately engaged with the conduct of the mission. We call this new paradigm the Interplanetary Network. Its vision is seamless connectivity between scientists and their instruments, new data analysis and visualization tools that will greatly enhance and enable new modes of space exploration, and the involvement of the public via web-based “telepresence.”
MIMO-SAR: A Hierarchical High-Resolution Imaging Algorithm for mmWave FMCW Radar in Autonomous Driving

Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driver-assistance system features. A key shortcoming for present-day vehicular radar imaging is poor azimuth resolution (for side-looking operation) due to the form factor limits on antenna size and placement. In this paper, we propose a solution via a new multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) imaging technique, that applies coherent SAR principles to vehicular MIMO radar to improve the side-view (angular) resolution. The proposed 2-stage hierarchical MIMO-SAR processing workflow drastically reduces the computation load while preserving image resolution. To enable coherent processing over the synthetic aperture, we integrate a radar odometry algorithm that estimates the trajectory of ego-radar. The MIMO-SAR algorithm is validated by both simulations and real experiment data collected by a vehicle-mounted radar platform.
Almost minimizers for the thin obstacle problem

We consider Anzellotti-type almost minimizers for the thin obstacle (or Signorini) problem with zero thin obstacle and establish their $$C^{1,\beta }$$ regularity on the either side of the thin manifold, the optimal growth away from the free boundary, the $$C^{1,\gamma }$$ regularity of the regular part of the free boundary, as well as a structural theorem for the singular set. The analysis of the free boundary is based on a successful adaptation of energy methods such as a one-parameter family of Weiss-type monotonicity formulas, Almgren-type frequency formula, and the epiperimetric and logarithmic epiperimetric inequalities for the solutions of the thin obstacle problem.
Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments

Simulation provides a safe and efficient way to generate useful data for learning complex robotic tasks. However, matching simulation and real-world dynamics can be quite challenging, especially for systems that have a large number of unobserved or unmeasurable parameters, which may lie in the robot dynamics itself or in the environment with which the robot interacts. We introduce a novel approach to tackle such a sim-to-real problem by developing policies capable of adapting to new environments, in a zero-shot manner. Key to our approach is an error-aware policy (EAP) that is explicitly made aware of the effect of unobservable factors during training. An EAP takes as input the predicted future state error in the target environment, which is provided by an error-prediction function, simultaneously trained with the EAP. We validate our approach on an assistive walking device trained to help the human user recover from external pushes. We show that a trained EAP for a hip-torque assistive device can be transferred to different human agents with unseen biomechanical characteristics. In addition, we show that our method can be applied to other standard RL control tasks.
Linkage Arguments For and Against Rights

This essay is about relations of support and conflict within systems of fundamental legal rights—and the arguments for and against rights that those relations make possible. Justificatory linkage arguments defend controversial rights by claiming that they provide very useful support to the realization of well-accepted rights. This essay analyzes such arguments in detail and discusses their structures, uses, and pitfalls. It then shows that linkage arguments can be used not just to defend rights but also to attack them. When rights conflict—severely or weakly, logically or practically—negative linkage arguments attacking them can be based on the trouble they make for other rights. Many examples of conflicts of rights are provided. Negative linkage arguments provide reasons for rejecting, repealing, or trimming the criticized right. Such arguments are already in regular use but their close relation to justificatory linkage arguments has not been recognized.
Remote Sensing of Turbidity Plumes in Lake Ontario

Space imagery and high-altitude photography provide an effective method of monitoring the dynamics of turbidity plumes in Lake Ontario. The Niagara River plume, as much as 200 sq miles (520 km²) in area, is by far the largest turbidity feature in the lake. Plume analyses corroborates the presence of a prevailing eastward flowing longshore current along the entire south shore of the lake. This current is most persistent at the Oswego River outlet but it is quite variable in the Rochester embayment, where rapid shifts in water movement were occasionally detected. The position of the spring thermal bar, a zone of maximum density water corresponding to the 4°C isotherm, was located near the Niagara River outlet in images obtained during April, 1973.
Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noise

We give a computationally-efficient PAC active learning algorithm for $d$-dimensional homogeneous halfspaces that can tolerate Massart noise~\citep{massart2006risk} and Tsybakov~\citep{tsybakov2004optimal} noise. Specialized to the $\eta$-Massart noise setting, our algorithm achieves an information-theoretically near-optimal label complexity of $\tilde{O}\rbr{\frac{d}{(1-2\eta)^2} \polylog(\frac1\epsilon)}$ under a wide range of unlabeled data distributions (specifically, the family of ``structured distributions'' defined in~\citet{diakonikolas2020polynomial}). Under the more challenging Tsybakov noise condition, we identify two subfamilies of noise conditions, under which our algorithm is the first to achieve computational efficiency and provide label complexity guarantees strictly lower than passive learning algorithms.
Adaptivity in Adaptive Submodularity

Adaptive sequential decision making is one of the central challenges in machine learning and artificial intelligence. In such problems, the goal is to design an interactive policy that plans for an action to take, from a finite set of $n$ actions, given some partial observations. It has been shown that in many applications such as active learning, robotics, sequential experimental design, and active detection, the utility function satisfies adaptive submodularity, a notion that generalizes the notion of diminishing returns to policies. In this paper, we revisit the power of adaptivity in maximizing an adaptive monotone submodular function. We propose an efficient semi adaptive policy that with $O(\log n \times\log k)$ adaptive rounds of observations can achieve an almost tight $1-1/e-\eps$ approximation guarantee with respect to an optimal policy that carries out $k$ actions in a fully sequential manner. To complement our results, we also show that it is impossible to achieve a constant factor approximation with $o(\log n)$ adaptive rounds. We also extend our result to the case of adaptive stochastic minimum cost coverage where the goal is to reach a desired utility $Q$ with the cheapest policy. We first prove the long-standing conjecture by Golovin and Krause and show that the greedy policy achieves the asymptotically tight logarithmic approximation guarantee. We then propose a semi adaptive policy that provides the same guarantee in polylogarithmic adaptive rounds through a similar information-parallelism scheme. Our results shrink the adaptivity gap in adaptive submodular maximization by an exponential factor.
Zeros of Jensen polynomials and asymptotics for the Riemann xi function

The classical criterion of Jensen for the Riemann hypothesis is that all of the associated Jensen polynomials have only real zeros. We find a new version of this criterion, using linear combinations of Hermite polynomials, and show that this condition holds in many cases. Detailed asymptotic expansions are given for the required Taylor coefficients of the xi function at 1/2 as well as related quantities. These results build on those in the recent paper of Griffin, Ono, Rolen and Zagier.
Uniform Subspace Correction Preconditioners for Discontinuous Galerkin Methods with hp-Refinement

In this paper, we develop subspace correction preconditioners for discontinuous Galerkin (DG) discretizations of elliptic problems with hp-refinement. These preconditioners are based on the decomposition of the DG finite element space into a conforming subspace, and a set of small nonconforming edge spaces. The conforming subspace is preconditioned using a matrix-free low-order refined technique, which in this work, we extend to the hp-refinement context using a variational restriction approach. The condition number of the resulting linear system is independent of the granularity of the mesh h, and the degree of the polynomial approximation p. The method is amenable to use with meshes of any degree of irregularity and arbitrary distribution of polynomial degrees. Numerical examples are shown on several test cases involving adaptively and randomly refined meshes, using both the symmetric interior penalty method and the second method of Bassi and Rebay (BR2).
A Modeled Approach for Online Adversarial Test of Operational Vehicle Safety

The scenario-based testing of operational vehicle safety presents a set of principal other vehicle (POV) trajectories that seek to force the subject vehicle (SV) into a certain safety-critical situation. Current scenarios are mostly (i) statistics-driven: inspired by human driver crash data, (ii) deterministic: POV trajectories are pre-determined and are independent of SV responses, and (iii) overly simplified: defined over a finite set of actions performed at the abstracted motion planning level. Such scenario-based testing (i) lacks severity guarantees, (ii) has predefined maneuvers making it easy for an SV with intelligent driving policies to game the test, and (iii) is inefficient in producing safety-critical instances with limited and expensive testing effort. We propose a model-driven online feedback control policy for multiple POVs which propagates efficient adversarial trajectories while respecting traffic rules and other concerns formulated as an admissible state-action space. The approach is formulated in an anchor-template hierarchy structure, with the template model planning inducing a theoretical SV capturing guarantee under standard assumptions. The planned adversarial trajectory is then tracked by a lower-level controller applied to the full-system or the anchor model. The effectiveness of the methodology is illustrated through various simulated examples with the SV controlled by either parameterized self-driving policies or human drivers.
Measuring Topological Order

The topological order of a $(2+1)\mathrm{D}$ topological phase of matter is characterized by its chiral central charge and a unitary modular tensor category that describes the universal fusion and braiding properties of its anyonic quasiparticles. I discuss the topologically invariant quantities associated with these and identify ones that are useful for determining the topological order. I propose a variety of physical experiments that probe these quantities and detail the relation of the measured data to the topological invariants.
Activation of Proneuronal Transcription Factor Ascl1 in Maternal Liver Ensures a Healthy Pregnancy.

Background & aims Maternal liver exhibits robust adaptations to pregnancy to accommodate the metabolic needs of developing and growing placenta and fetus by largely unknown mechanisms. We found that achaete-scute homolog-like 1 (Ascl1), a gene encoding a basic helix-loop-helix transcription factor essential for neuronal development, is highly activated in maternal hepatocytes during the second half of gestation in mice. Methods To investigate whether and how Ascl1 plays a pregnancy-dependent role, we deleted the Ascl1 gene specifically in maternal hepatocytes from mid-gestation until term. Results As a result, we identified multiple Ascl1-dependent phenotypes. Maternal livers lacking Ascl1 exhibited aberrant hepatocyte structure, increased hepatocyte proliferation, enlarged hepatocyte size, reduced albumin production, and elevated release of liver enzymes, indicating maternal liver dysfunction. Simultaneously, maternal pancreas and spleen and the placenta displayed marked overgrowth; and the maternal ceca microbiome showed alterations in relative abundance of several bacterial subpopulations. Moreover, litters born from maternal hepatic Ascl1-deficient dams experienced abnormal postnatal growth after weaning, implying an adverse pregnancy outcome. Mechanistically, we found that maternal hepatocytes deficient for Ascl1 exhibited robust activation of insulin-like growth factor 2 expression, which may contribute to the Ascl1-dependent phenotypes widespread in maternal and uteroplacental compartments. Conclusions In summary, we demonstrate that maternal liver, via activating Ascl1 expression, modulates the adaptations of maternal organs and the growth of the placenta to maintain a healthy pregnancy. Our studies reveal Ascl1 as a novel and critical regulator of the physiology of pregnancy.
A General Coded Caching Scheme for Scalar Linear Function Retrieval

Coded caching aims to minimize the network's peak-time communication load by leveraging the information pre-stored in the users' local cache. The original single file retrieval setting by Maddah-Ali and Niesen has been recently extended to general Scalar Linear Function Retrieval (SLFR) by Wan et al., who proposed a linear scheme that surprisingly achieves the same optimal load (under the constraint of uncoded cache placement) as in single file retrieval. This paper's goal is to characterize the conditions under which a general SLFR linear scheme is optimal and gain practical insights into why the specific choices made by Wan et al. work. This paper shows that the optimal decoding coefficients are necessarily the product of two terms, one only involving the encoding coefficients and the other only the demands. In addition, the relationships among the encoding coefficients are shown to be captured by the cycles of a certain graph. Thus, a general linear scheme for SLFR can be found by solving a spanning tree problem.