cssi/SciSciGPT-SciSciNet huggingface dataset:
The Case Against Free Speech

Free societies employ a variety of institutions in which speech is heavily regulated on the basis of its content in order to promote other desirable ends, including discovery of the truth. I illustrate this with the case of courts and rules of evidence. Of course, three differences between courts and the polity at large might seem to counsel against extending that approach more widely. First, the courtroom has an official and somewhat reliable (as well as reviewable) arbiter of the epistemic merits, while the polity may not. Second, no other non-epistemic values of speech are at stake in the courtroom, whereas they are in the polity. Third, the courtroom's jurisdiction is temporally limited in a way the polity's may not be. I argue that only the first of these - the 'Problem of the Epistemic Arbiter' as I call it - poses a serious worry about speech regulation outside select institutions like courts. I also argue for viewing 'freedom of speech' like 'freedom of action': speech, like everything else human beings do, can be benign or harmful, constructive or pernicious. Thus, the central question in free speech jurisprudence should really be how to regulate speech effectively - to minimise its very real harms, without undue cost to its positive values. In particular, I argue against autonomy-based defences of a robust free speech principle. I conclude that the central issue in free speech jurisprudence is not about speech, but about institutional competence. I offer some reasons - from the Marxist 'left' and the public choice 'right' - for being sceptical that capitalist democracies have the requisite competence and make some suggestive remarks about how these defects might be remedied.
Do the GSEs Expand the Supply of Mortgage Credit? New Evidence of Crowd Out in the Secondary Mortgage Market

Abstract The dramatic government takeover of Fannie Mae and Freddie Mac in September, 2008 was motivated in part by a desire to ensure a continued flow of credit to the mortgage market. This study examines a closely related issue: the extent to which GSE activity crowds out mortgage purchases by private secondary market intermediaries. Evidence of substantial crowd out suggests that government support for the GSEs may be less warranted, whereas the absence of crowd out implies that GSE loan purchases enhance liquidity. Using 1994–2008 HMDA data for conventional, conforming sized loans, three distinct periods with regard to GSE crowd out are apparent. From 1994 to 2003, the share of loans sold to the secondary market increased from 60 to over 90%, private sector and GSE market shares of loan purchases were roughly similar for most market segments, and IV estimates indicate relatively little GSE crowd out of private secondary market purchases. From 2004 to 2006, private loan purchases boomed and dominated those of the GSEs, while IV estimates indicate crowd out jumped to 50% at the peak of the boom. This is especially true in the market for home purchase as opposed to refinance loans. With the crash in housing and mortgage markets in 2007, private sector intermediaries pulled back, the GSEs regained market share, and evidence of GSE crowd out disappeared in both the home purchase loan and refinance markets. These patterns suggest that the degree of GSE crowd out varies with market conditions and that the federal takeover of Fannie Mae and Freddie Mac likely served to enhance liquidity to the mortgage market during the 2007–2009 financial crisis.
Learning Assisted Side Channel Delay Test for Detection of Recycled ICs

With the outsourcing of design flow, ensuring the security and trustworthiness of integrated circuits has become more challenging. Among the security threats, IC counterfeiting and recycled ICs have received a lot of attention due to their inferior quality, and in turn, their negative impact on the reliability and security of the underlying devices. Detecting recycled ICs is challenging due to the effect of process variations and process drift occurring during the chip fabrication. Moreover, relying on a golden chip as a basis for comparison is not always feasible. Accordingly, this paper presents a recycled IC detection scheme based on delay side-channel testing. The proposed method relies on the features extracted during the design flow and the sample delays extracted from the target chip to build a Neural Network model using which the target chip can be truly identified as new or recycled. The proposed method classifies the timing paths of the target chip into two groups based on their vulnerability to aging using the information collected from the design and detects the recycled ICs based on the deviation of the delay of these two sets from each other.
Self-Supervised Learning of Point Clouds via Orientation Estimation

Point clouds provide a compact and efficient representation of 3D shapes. While deep neural networks have achieved impressive results on point cloud learning tasks, they require massive amounts of manually labeled data, which can be costly and time-consuming to collect. In this paper, we leverage 3D self-supervision for learning downstream tasks on point clouds with fewer labels. A point cloud can be rotated in infinitely many ways, which provides a rich label-free source for self-supervision. We consider the auxiliary task of predicting rotations that in turn leads to useful features for other tasks such as shape classification and 3D keypoint prediction. Using experiments on ShapeNet and ModelNet, we demonstrate that our approach outperforms the state-of-the-art. Moreover, features learned by our model are complementary to other self-supervised methods and combining them leads to further performance improvement.
Scaling Symbolic Methods using Gradients for Neural Model Explanation

Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers have been proposed for analyzing and verifying neural network properties, but their usage has been fairly limited owing to their poor scalability with larger networks. In this work, we propose a technique for combining gradient-based methods with symbolic techniques to scale such analyses and demonstrate its application for model explanation. In particular, we apply this technique to identify minimal regions in an input that are most relevant for a neural network's prediction. Our approach uses gradient information (based on Integrated Gradients) to focus on a subset of neurons in the first layer, which allows our technique to scale to large networks. The corresponding SMT constraints encode the minimal input mask discovery problem such that after masking the input, the activations of the selected neurons are still above a threshold. After solving for the minimal masks, our approach scores the mask regions to generate a relative ordering of the features within the mask. This produces a saliency map which explains" where a model is looking" when making a prediction. We evaluate our technique on three datasets-MNIST, ImageNet, and Beer Reviews, and demonstrate both quantitatively and qualitatively that the regions generated by our approach are sparser and achieve higher saliency scores compared to the gradient-based methods alone.
Quantum Unique Ergodicity for Eisenstein Series in the Level Aspect

We prove a variety of quantum unique ergodicity (QUE) results for Eisenstein series in the level aspect. A new feature of this variant of QUE is that the main term involves the logarithmic derivative of a Dirichlet L-function on the 1-line. A zero of this L-function near the 1-line can thus have a distorting effect on the main term. We obtain quantitative control on the test function and thereby prove an asymptotic formula in the level aspect version of the problem with test functions of shrinking support. Surprisingly, this asymptotic formula shows some obstruction to equidistribution that may retrospectively be interpreted as being caused by the growth of Eisenstein series in the cusps. We also make some coarse descriptions on the unevenness of the mass distribution of level N Eisenstein series on the fibers of the canonical projection map from $$Y_0(N)$$ to $$Y_0(1)$$ .
Cavitating flow behind a backward facing step

Abstract Flow topology and unsteady cavitation dynamics in the wake of a backward facing step was investigated using high speed videography and time-resolved X-ray densitometry, along with static and dynamic pressure measurements. The measurements are used to inform the understanding of underlying mechanisms of observed flow dynamics as they are related to shock wave induced instabilities. The differences in cavity topology and behavior at different cavitation numbers are examined to highlight flow features such as the pair of cavitating spanwise vortices in the shear layer and propagating bubbly shock front. The shock speeds at different cavitation conditions are estimated based on void-fraction measurements using the X-T diagram and compared to those computed using the one-dimensional Rankine Hugoniot jump condition. The two speeds(computed and measured) are found to be comparable. The effect of compressibility of the bubbly mixture is determined by estimating the local speed of sound using homogeneous ‘frozen model’ and homogeneous ‘equilibrium model’. The current estimation of sound speed suggests that expression used to determine the local speed of sound is closer to the classic ‘frozen model’ than the ‘equilibrium model’ of speed of sound.
Dynamical and arithmetic degrees for random iterations of maps on projective space

We show that the dynamical degree of an (i.i.d) random sequence of dominant, rational self-maps on projective space is almost surely constant. We then apply this result to height growth and height counting problems in random orbits.
The Division of Labor in Communication: Speakers Help Listeners Account for Asymmetries in Visual Perspective

Recent debates over adults' theory of mind use have been fueled by surprising failures of perspective-taking in communication, suggesting that perspective-taking may be relatively effortful. Yet adults routinely engage in effortful processes when needed. How, then, should speakers and listeners allocate their resources to achieve successful communication? We begin with the observation that the shared goal of communication induces a natural division of labor: The resources one agent chooses to allocate toward perspective-taking should depend on their expectations about the other's allocation. We formalize this idea in a resource-rational model augmenting recent probabilistic weighting accounts with a mechanism for (costly) control over the degree of perspective-taking. In a series of simulations, we first derive an intermediate degree of perspective weighting as an optimal trade-off between expected costs and benefits of perspective-taking. We then present two behavioral experiments testing novel predictions of our model. In Experiment 1, we manipulated the presence or absence of occlusions in a director-matcher task. We found that speakers spontaneously modulated the informativeness of their descriptions to account for "known unknowns" in their partner's private view, reflecting a higher degree of speaker perspective-taking than previously acknowledged. In Experiment 2, we then compared the scripted utterances used by confederates in prior work with those produced in interactions with unscripted directors. We found that confederates were systematically less informative than listeners would initially expect given the presence of occlusions, but listeners used violations to adaptively make fewer errors over time. Taken together, our work suggests that people are not simply "mindblind"; they use contextually appropriate expectations to navigate the division of labor with their partner. We discuss how a resource-rational framework may provide a more deeply explanatory foundation for understanding flexible perspective-taking under processing constraints.
Abdication and federalism

States abdicate many of their federal responsibilities to local governments. They do not monitor local compliance with those laws; they disclaim responsibility for the actions of their local governments; and they deny state officials the legal capacity to bring local governments into compliance. When sued for noncompliance with these federal laws, states attempt to evade responsibility by arguing that local governments — and not the state — are responsible. These arguments create serious and unexplored barriers to enforcing federal law. They present thorny issues of federalism and liability, and courts struggle with them. Because neither courts resolving these conflicts nor advocates litigating them are aware that abdication occurs regularly across a number of policy areas, courts have failed to develop a consistent methodology for addressing it. This Article argues that courts should reject these state arguments in most cases and outlines the contours of a “nonabdication doctrine” that would be less solicitous and accommodating of existing state laws and more attentive to the language of federal laws.This Article is the first to uncover these state arguments and mark them as a pattern across a surprisingly diverse set of states and federal policies: indigent defense, election law, public assistance, conditions of incarceration, and others. It uses state filings — including archived documents — as well as interviews with numerous advocates and state officials, to explore the concept of state abdication. It posits that abdication is a consequence of superimposing federal responsibilities onto the diverse legal and political relationships between states and their local governments. It suggests that abdication provides a new lens through which to reassess previous thinking on localism, federalism, and decentralization. Because abdication permits states to shelter noncompliance with federal law at the local level and mutes productive local dissent, it reveals a cost to decentralizing federal policy that federalism scholars currently overlook.
The Silent Pandemic: Emergent Antibiotic Resistances Following the Global Response to SARS-CoV-2.

Summary The ongoing SARS-CoV-2 pandemic has highlighted the importance of the rapid development of vaccines and antivirals. However, the potential for the emergence of antibiotic resistances due to the increased use of antibacterial cleaning products and therapeutics presents an additional, underreported threat. Most antibacterial cleaners contain simple quaternary ammonium compounds (QACs); however, these compounds are steadily becoming less effective as antibacterial agents. QACs are extensively used in SARS-CoV-2-related sanitization in clinical and household settings. Similarly, due to the danger of secondary infections, antibiotic therapeutics are increasingly used as a component of COVID-19 treatment regimens, even in the absence of a bacterial infection diagnosis. The increased use of antibacterial agents as cleaners and therapeutics is anticipated to lead to novel resistances in the coming years.
Libertarian Paternalism Is Not an Oxymoron

The idea of libertarian paternalism might seem to be an oxymoron, but it is both possible and legitimate for private and public institutions to affect behavior while also respecting freedom of choice. Often people's preferences are ill-formed, and their choices will inevitably be influenced by default rules, framing effects, and starting points. In these circumstances, a form of paternalism cannot be avoided. Equipped with an understanding of behavioral findings of bounded rationality and bounded self-control, libertarian paternalists should attempt to steer people's choices in welfare-promoting directions without eliminating freedom of choice. It is also possible to show how a libertarian paternalist might select among the possible options and to assess how much choice to offer. Examples are given from many areas, including savings behavior, labor law, and consumer protection.
Legislating for profit and optimal Eighth‐Amendment review

We compare a net‐revenue maximizing legislator's criminalization and punishment decisions to the optimal choices. The legislator over‐criminalizes and over‐punishes all criminalized acts when the degree to which it internalizes harms from crimes increases proportionally with the harm. An analysis of Eighth‐Amendment review, in the form of upper‐bounds on fines, reveals that it can both reduce fines to their optimal levels and remove the legislator's incentives to inefficiently criminalize low‐harm acts in the first place. These results provide a rationale for asymmetric judicial review wherein upper‐bounds are imposed on punishment, but lower‐bounds are not.
The Topology of Statistical Verifiability

Topological models of empirical and formal inquiry are increasingly prevalent. They have emerged in such diverse fields as domain theory [1, 16], formal learning theory [18], epistemology and philosophy of science [10, 15, 8, 9, 2], statistics [6, 7] and modal logic [17, 4]. In those applications, open sets are typically interpreted as hypotheses deductively verifiable by true propositional information that rules out relevant possibilities. However, in statistical data analysis, one routinely receives random samples logically compatible with every statistical hypothesis. We bridge the gap between propositional and statistical data by solving for the unique topology on probability measures in which the open sets are exactly the statistically verifiable hypotheses. Furthermore, we extend that result to a topological characterization of learnability in the limit from statistical data.
Rate of Prefix-free Codes in LQG Control Systems with Side Information

In this work, we study an LQG control system where one of two feedback channels is discrete and incurs a communication cost. We assume that a decoder (co-located with the controller) can make noiseless measurements of a subset of the state vector (referred to as side information) meanwhile a remote encoder (co-located with a sensor) can make arbitrary measurements of the entire state vector, but must convey its measurements to the decoder over a noiseless binary channel. Use of the channel incurs a communication cost, quantified as the time-averaged expected length of prefix-free binary codeword. We study the tradeoff between the communication cost and control performance. The formulation motivates a constrained directed information minimization problem, which can be solved via convex optimization. Using the optimization, we propose a quantizer design and a subsequent achievability result.
The Contribution of Astrocyte and Neuronal Panx1 to Seizures Is Model and Brain Region Dependent.

Pannexin1 (Panx1) is an ATP release channel expressed in neurons and astrocytes that plays important roles in CNS physiology and pathology. Evidence for the involvement of Panx1 in seizures include...
Community Service Component of an Alternative Bar Exam

As part of a Symposium, the author was asked to comment on a community service component of an alternative bar exam. More specifically, within the article, she addressed the following questions: assuming that a community service component were made a component of an alternative bar exam, (1) whom should such a component serve, (2) how would it fit into existing models of providing service to underserved communities, and (3) how could such an approach be funded.
Data-driven modeling of rotating detonation waves

A rotating detonation engine displays complex nonlinear shock front propagation dynamics which impede effective dimensionality reduction. A novel optimization is used to discover separate low-rank modes and interpretable dynamics for each front's propagation. Koopman autoencoders similarly enable separation and forecasting of shock wave interactions.
Semi-Supervised Learning for Eye Image Segmentation

Recent advances in appearance-based models have shown improved eye tracking performance in difficult scenarios like occlusion due to eyelashes, eyelids or camera placement, and environmental reflections on the cornea and glasses. The key reason for the improvement is the accurate and robust identification of eye parts (pupil, iris, and sclera regions). The improved accuracy often comes at the cost of labeling an enormous dataset, which is complex and time-consuming. This work presents two semi-supervised learning frameworks to identify eye-parts by taking advantage of unlabeled images where labeled datasets are scarce. With these frameworks, leveraging the domain-specific augmentation and novel spatially varying transformations for image segmentation, we show improved performance on various test cases with limited labeled samples. For instance, for a model trained on just 4 and 48 labeled images, these frameworks improved by at least 4.7% and 0.4% respectively, in segmentation performance over the baseline model, which is trained only with the labeled dataset.
Internal wave energy flux from density perturbations in nonlinear stratifications

Internal gravity wave energy contributes significantly to the energy budget of the oceans, affecting mixing and the thermohaline circulation. Hence it is important to determine the internal wave energy flux for the different cases agree well with results from direct numerical simulations of the Navier–Stokes equations. Our computational method can be applied to any density perturbation data using the MATLAB graphical user interface ‘EnergyFlux’.
Lyapunov-Regularized Reinforcement Learning for Power System Transient Stability

Transient stability of power systems is becoming increasingly important because of the growing integration of renewable resources. These resources lead to a reduction in mechanical inertia but also provide increased flexibility in frequency responses. Namely, their power electronic interfaces can implement almost arbitrary control laws. To design these controllers, reinforcement learning (RL) has emerged as a powerful method in searching for optimal non-linear control policy parameterized by neural networks. A key challenge is to enforce that a learned controller must be stabilizing. This letter proposes a Lyapunov regularized RL approach for optimal frequency control for transient stability in lossy networks. Because the lack of an analytical Lyapunov function, we learn a Lyapunov function parameterized by a neural network. The losses are specially designed with respect to the physical power system. The learned neural Lyapunov function is then utilized as a regularization to train the neural network controller by penalizing actions that violate the Lyapunov conditions. Case study shows that introducing the Lyapunov regularization enables the controller to be stabilizing and achieve smaller losses.
Deciding What to Learn: A Rate-Distortion Approach

Agents that learn to select optimal actions represent a prominent focus of the sequential decision-making literature. In the face of a complex environment or constraints on time and resources, however, aiming to synthesize such an optimal policy can become infeasible. These scenarios give rise to an important trade-off between the information an agent must acquire to learn and the sub-optimality of the resulting policy. While an agent designer has a preference for how this trade-off is resolved, existing approaches further require that the designer translate these preferences into a fixed learning target for the agent. In this work, leveraging rate-distortion theory, we automate this process such that the designer need only express their preferences via a single hyperparameter and the agent is endowed with the ability to compute its own learning targets that best achieve the desired trade-off. We establish a general bound on expected discounted regret for an agent that decides what to learn in this manner along with computational experiments that illustrate the expressiveness of designer preferences and even show improvements over Thompson sampling in identifying an optimal policy.
Escaping the Time Pit: Pitfalls and Guidelines for Using Time-Based Git Data

Many software engineering research papers rely on time-based data (e.g., commit timestamps, issue report creation/update/close dates, release dates). Like most real-world data however, time-based data is often dirty. To date, there are no studies that quantify how frequently such data is used by the software engineering research community, or investigate sources of and quantify how often such data is dirty. Depending on the research task and method used, including such dirty data could affect the research results. This paper presents the first survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of the 690 technical track and data papers published in MSR 2004–2020, we saw at least 35% of papers utilized time-based data. We then used the Boa and Software Heritage infrastructures to help identify and quantify several sources of dirty commit timestamp data. Finally we provide guidelines/best practices for researchers utilizing time-based data from Git repositories.
Efficiently Cooled Stellar Wind Bubbles in Turbulent Clouds. I. Fractal Theory and Application to Star-forming Clouds

Winds from massive stars have velocities of 1000 km/s or more, and produce hot, high pressure gas when they shock. We develop a theory for the evolution of bubbles driven by the collective winds from star clusters early in their lifetimes, which involves interaction with the turbulent, dense interstellar medium of the surrounding natal molecular cloud. A key feature is the fractal nature of the hot bubble's surface. The large area of this interface with surrounding denser gas strongly enhances energy losses from the hot interior, enabled by turbulent mixing and subsequent cooling at temperatures T = 10^4-10^5 K where radiation is maximally efficient. Due to the extreme cooling, the bubble radius scales differently (R ~ t^1/2) from the classical Weaver77 solution, and has expansion velocity and momentum lower by factors of 10-10^2 at given R, with pressure lower by factors of 10^2 - 10^3. Our theory explains the weak X-ray emission and low shell expansion velocities of observed sources. We discuss further implications of our theory for observations of the hot bubbles and cooled expanding shells created by stellar winds, and for predictions of feedback-regulated star formation in a range of environments. In a companion paper, we validate our theory with a suite of hydrodynamic simulations.
Concentrated Stopping Set Design for Coded Merkle Tree: Improving Security Against Data Availability Attacks in Blockchain Systems

In certain blockchain systems, light nodes are clients that download only a small portion of the block. Light nodes are vulnerable to data availability (DA) attacks where a malicious node hides an invalid portion of the block from the light nodes. Recently, a technique based on erasure codes called Coded Merkle Tree (CMT) was proposed by Yu et al. that enables light nodes to detect a DA attack with high probability. The CMT is constructed using LDPC codes for fast decoding but can fail to detect a DA attack if a malicious node hides a small stopping set of the code. To combat this, Yu et al. used well-studied techniques to design random LDPC codes with high minimum stopping set size. Although effective, these codes are not necessarily optimal for this application. In this paper, we demonstrate a more specialized LDPC code design to improve the security against DA attacks. We achieve this goal by providing a deterministic LDPC code construction that focuses on concentrating stopping sets to a small group of variable nodes rather than only eliminating stopping sets. We design these codes by modifying the Progressive Edge Growth algorithm into a technique called the entropy-constrained PEG (EC-PEG) algorithm. This new method demonstrates a higher probability of detecting DA attacks and allows for good codes at short lengths.
Reynolds-Stress and Triple-Product Models Applied to Flows with Rotation and Curvature

Predictions for Reynolds-stress and triple product turbulence models are compared for flows with significant rotational effects. Driver spinning cylinder flowfield and Zaets rotating pipe case are to be investigated at a minimum.
On the conformal method for the Einstein constraint equations

In this work, we use the global analysis and degree-theoretic methods introduced by Smale to study the existence and multiplicity of solutions of the vacuum Einstein constraint equations given by the conformal method of Lichnerowicz-Choquet-Bruhat-York. In particular this approach gives a new proof of the existence result of Maxwell and Holst-Nagy-Tsogtgerel. We also relate the method to the limit equation of Dahl-Gicquaud-Humbert and the non-existence result of Nguyen.
Interventions for Improving Professional Networking for Women: Experimental Evidence from the IT Sector

Professional networks are vital for individuals’ career advancement. Research demonstrates, however, that women are often disadvantaged in their access to such networks. Using a randomized field experiment at an IT conference, we found that women had worse networking outcomes than men. Relative to men, women met 42% fewer new contacts, spent 48% less time talking to them, and added 25% fewer LinkedIn connections. We theorized that in fields where women are underrepresented (e.g., IT) two networking barriers—search and social—differentially affect men and women. We designed and experimentally tested interventions for reducing these barriers. The search intervention was designed to facilitate locating diverse contacts and information. The social intervention was designed to facilitate helping behavior and connecting across social boundaries. We find that the search intervention increased the number of new contacts women met by 57%, the time they spent talking with them by 90%, the number of LinkedIn connections they added by 29%, and their odds of changing jobs by a factor of 1.6. The social intervention increased the time women spent talking to new contacts by 66%. The interventions did not improve men’s outcomes. Our results show that simple interventions can help women grow their networks and find jobs.
CrossRoI: cross-camera region of interest optimization for efficient real time video analytics at scale

Video cameras are pervasively deployed in city scale for public good or community safety (i.e. traffic monitoring or suspected person tracking). However, analyzing large scale video feeds in real time is data intensive and poses severe challenges to today's network and computation systems. We present CrossRoI, a resource-efficient system that enables real time video analytics at scale via harnessing the videos content associations and redundancy across a fleet of cameras. CrossRoI exploits the intrinsic physical correlations of cross-camera viewing fields to drastically reduce the communication and computation costs. CrossRoI removes the repentant appearances of same objects in multiple cameras without harming comprehensive coverage of the scene. CrossRoI operates in two phases - an offline phase to establish cross-camera correlations, and an efficient online phase for real time video inference. Experiments on real-world video feeds show that CrossRoI achieves 42% ~ 65% reduction for network overhead and 25% ~ 34% reduction for response delay in real time video analytics applications with more than 99% query accuracy, when compared to baseline methods. If integrated with SotA frame filtering systems, the performance gains of CrossRoI reaches 50% ~ 80% (network overhead) and 33% ~ 61% (end-to-end delay).
NuSTAR Observation of LS 5039

LS 5039 is a high-mass gamma-ray binary hosting a compact object of unknown type. NuSTAR observed LS 5039 during its entire 3.9 day binary period. We performed a periodic signal search up to 1000 Hz which did not produce credible period candidates. We do see the 9.05 s period candidate, originally reported by Yoneda et al. 2020 using the same data, in the Fourier power spectrum, but we find that the statistical significance of this feature is too low to claim it as a real detection. We also did not find significant bursts or quasi-periodic variability. The modulation with the orbital period is clearly seen and remains unchanged over a decade long timescale when compared to the earlier Suzaku light curve. The joint analysis of the NuSTAR and Suzaku XIS data shows that the 0.7-70 keV spectrum can be satisfactory described by a single absorbed power-law model with no evidence of cutoff at higher energies. The slope of the spectrum anti-correlates with the flux during the binary orbit. Therefore, if LS 5039 hosts a young neutron star, its X-ray pulsations appear to be outshined by the intrabinary shock emission. The lack of spectral lines and/or an exponential cutoff at higher energies suggests that the putative neutron star is not actively accreting. Although a black hole scenario still remains a possibility, the lack of variability or Fe K$\alpha$ lines, which typically accompany accretion, makes it less likely.
SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions.

The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.
Learning Product Characteristics and Consumer Preferences from Search Data

A building block of many models in empirical industrial organization is a characteristic space, where products are modeled as a bundle of characteristics over which consumers have preferences. The ability of such models to predict counterfactual outcomes depends on how well this characteristic space representation can capture substitution patterns. A limitation of existing methods is that product characteristics must be observable. In this paper, we extend a machine learning approach (Bayesian Personalized Ranking) that allows us to jointly learn latent product characteristics and consumer preferences from search data. We then show how this can be combined with existing demand estimation approaches to predict demand. Our application is to the hotel market, where we combine two datasets: consumers' web browsing histories, and hotel prices and occupancy rates. Using an event study design, we show that closeness in latent characteristic space predicts competition: hotels that are close to new entrants lose the most market share post-entry. We take a more structural approach to the 2016 merger of Marriott and Starwood, demonstrating that by using latent characteristics and consumer preferences learned from search data, we can substantially improve post-merger predictions of demand relative to standard baselines.
On the entropy of parabolic Allen–Cahn equation

We define a (mean curvature flow) entropy for Radon measures in $\mathbb{R}^n$ or in a compact manifold. Moreover, we prove a monotonicity formula of the entropy of the measures associated with the parabolic Allen-Cahn equations. If the ambient manifold is a compact manifold with non-negative sectional curvature and parallel Ricci curvature, this is a consequence of a new monotonicity formula for the parabolic Allen-Cahn equation. As an application, we show that when the entropy of the initial data is small enough (less than twice of the energy of the one-dimensional standing wave), the limit measure of the parabolic Allen-Cahn equation has unit density for all future time.
Terahertz Light Sources by Electronic-Oscillator-Driven Second Harmonic Generation in Extreme-Confinement Cavities

We propose a coherent THz source by efficient, c ascaded second harmonic generation from electronic oscillators. We introduce hybrid dielectric cavity designs combining extreme field concentration in high-Q resonators with phonon-resonance-enhanced nonlinear materials.
Development of space launch facilities

Space launch facilities, discussing site plan, zoning and development of John F. Kennedy Space Center at Merritt Island
Some conformally invariant gap theorems for Bach-flat 4-manifolds

S.-Y. A. Chang, J. Qing, and P. Yang proved an important gap theorem for Bach-flat metrics with round sphere as model case in 2007. In this article, we generalize this result by establishing conformally invariant gap theorems for Bach-flat 4-manifolds with $$(\mathbb {CP}^2, g_{FS})$$ and $$(S^2 \times S^2,g_{prod})$$ as model cases. An iteration argument plays an important role in the case of $$(\mathbb {CP}^2, g_{FS})$$ and the convergence theory of Bach-flat metrics is of particular importance in the case of $$(S^2 \times S^2,g_{prod})$$ . The latter result provides an interesting way to distinguish $$(S^2 \times S^2,g_{prod})$$ from $$(\mathbb {CP}^2\#\bar{\mathbb {CP}}^2,g_{Page})$$ .
Insulin/IGF-1 signaling and heat stress differentially regulate HSF1 activities in germline development.

Germline development is sensitive to nutrient availability and environmental perturbation. Heat shock transcription factor 1 (HSF1), a key transcription factor driving the cellular heat shock response (HSR), is also involved in gametogenesis. The precise function of HSF1 (HSF-1 in C. elegans) and its regulation in germline development are poorly understood. Using the auxin-inducible degron system in C. elegans, we uncovered a role of HSF-1 in progenitor cell proliferation and early meiosis and identified a compact but important transcriptional program of HSF-1 in germline development. Interestingly, heat stress only induces the canonical HSR in a subset of germ cells but impairs HSF-1 binding at its developmental targets. Conversely, insulin/insulin growth factor 1 (IGF-1) signaling dictates the requirement for HSF-1 in germline development and functions through repressing FOXO/DAF-16 in the soma to activate HSF-1 in germ cells. We propose that this non-cell-autonomous mechanism couples nutrient-sensing insulin/IGF-1 signaling to HSF-1 activation to support homeostasis in rapid germline growth.
Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study

Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a nonconvex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCNs). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder-decoder structures to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. These numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time and yield more accurate subsurface velocity models in comparison with conventional FWI.
Replicating analyses of item response curves using data from the Force and Motion Conceptual Evaluation

Physics educators can better understand student preinstruction thinking by using item response curves rather than just correct versus incorrect responses.
Hessian Aware Quantization of Spiking Neural Networks

To achieve the low latency, high throughput, and energy efficiency benefits of Spiking Neural Networks (SNNs), reducing the memory and compute requirements when running on a neuromorphic hardware is an important step. Neuromorphic architecture allows massively parallel computation with variable and local bit-precisions. However, how different bit-precisions should be allocated to different layers or connections of the network is not trivial. In this work, we demonstrate how a layer-wise Hessian trace analysis can measure the sensitivity of the loss to any perturbation of the layer’s weights, and this can be used to guide the allocation of a layer-specific bit-precision when quantizing an SNN. In addition, current gradient based methods of SNN training use a complex neuron model with multiple state variables, which is not ideal for compute and memory efficiency. To address this challenge, we present a simplified neuron model that reduces the number of state variables by 4-fold while still being compatible with gradient based training. We find that the impact on model accuracy when using a layer-wise bit-precision correlated well with that layer’s Hessian trace. The accuracy of the optimal quantized network only dropped by 0.3%, yet the network size was reduced by 58%. This reduces memory usage and allows fixed-point arithmetic with simpler digital circuits to be used, increasing the overall throughput and energy efficiency.
A survey of current anticoagulation patient education practices and development.

Abstract Introduction Anticoagulants are high-risk medications widely used to prevent and treat thrombotic events, resulting in a need for adequate patient education to minimize harm. While anticoagulant patient education interventions can improve knowledge and surrogate outcomes, they may not represent current practice. Our objective was to determine provider perceptions of anticoagulation patient education at their institution. Methods A quantitative survey was distributed through a national professional organization and across several health systems. Questions included educational delivery methods, perceived effectiveness, evaluation of patient knowledge, development of patient education, and demographics. Results The 61 survey respondents were 79.0% female, 86.2% white, and an average age of 43. Most respondents (95%) complete patient education interventions in one session. Providers reviewed educational topics in 37% of daily patient interactions. 59% of respondents reported reasons for not performing patient knowledge checks included no formal process requiring knowledge checks, lack of time, and forgetting. The majority (93.4%) reported their patient education process was somewhat or very effective. The main reason underlying perceived less effective patient education was too much information in one session. Thirty-four respondents had some knowledge of patient education development at their institution. Most of these (82.9%) indicated their educational process’s development relied on expert opinion. In comparison, 22.9% indicated utilizing user-based design, and 10 (28.9%) respondents used learning theories. Conclusion While most providers felt patient education was effective, they reported reviewing education with patients daily. The lack of formal knowledge checks and best practices in developing patient education tools are significant gaps to address.
Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks

Rearranging and manipulating deformable objects such as cables, fabrics, and bags is a long-standing challenge in robotic manipulation. The complex dynamics and high-dimensional configuration spaces of deformables, compared to rigid objects, make manipulation difficult not only for multi-step planning, but even for goal specification. Goals cannot be as easily specified as rigid object poses, and may involve complex relative spatial relations such as "place the item inside the bag". In this work, we develop a suite of simulated benchmarks with 1D, 2D, and 3D deformable structures, including tasks that involve image-based goal-conditioning and multi-step deformable manipulation. We propose embedding goal-conditioning into Transporter Networks, a recently proposed model architecture for learning robotic manipulation that rearranges deep features to infer displacements that can represent pick and place actions. We demonstrate that goal-conditioned Transporter Networks enable agents to manipulate deformable structures into flexibly specified configurations without test-time visual anchors for target locations. We also significantly extend prior results using Transporter Networks for manipulating deformable objects by testing on tasks with 2D and 3D deformables. Supplementary material is available at https://berkeleyautomation.github.io/bags/.