cssi/SciSciGPT-SciSciNet huggingface dataset:
Who, If Anyone, Should be Liable for Injuries from Generic Drugs

Two recent Supreme Court decisions (PLIVA, Inc. v. Mensing (2011), and Mutual Pharmaceutical v. Bartlett (2013)), have essentially removed the threat of liability from generic drug manufacturers. In this paper, we consider four possible liability regimes in a simple model of drug market competition and safety research. Specifically, we compare a baseline regime of No Liability (NL) with the Everyone Liable (EL) regime (generics face the same liability as branded manufacturers), the No Liability for Generics (NLG) regime, resulting from PLIVA and Mutual (generics face no liability), and a novel Brand Fully Liable (BFL) regime (branded developer faces liability from injuries caused by a generic version of a drug it has developed). We find that the BFL regime typically provides the most efficient incentives to identify side effects and develop an efficient warning. However, the BFL regime can lead to overconsumption of the generic drug by patients who should not take the drug at all. For this reason, the EL regime may be preferable for a drug where the danger of side effects may outweigh the clinical value, as was alleged in Mutual Pharmaceutical v. Bartlett . We find that the NLG regime that resulted from the recent Supreme Court decisions is unlikely to be optimal, because it is dominated by BFL when the consumption decision is not important, and inferior to EL when it is very important.
REC and NdFe Magnetic Moment Irreversibility from Temperature Cycling

Presented are the results of thermal cycling tests carried out on REC and NdFe samples, to determine the irreversible losses in room temperature open circuit magnetic moment. A stabilization prescription was developed for a REC alloy that will allow two 4day/175°C temperature cycles, which simulate two UHV bakeouts, with only a 0.35% average loss and a 0.65% loss variation in the room temperature open circuit magnetic moment after stabilization.
The Reputational Penalty for Aggressive Accounting: Earnings Restatements and Management Turnover

In this paper we investigate the reputational penalties to managers of firms announcing earnings restatements. More specifically, we examine management turnover and the subsequent employment of displaced managers at firms announcing earnings restatements during 1997 or 1998. In contrast to prior research (Beneish 1999; Agrawal et al. 1999), which does not find increased turnover following GAAP violations or revelation of corporate fraud, we find that 60 percent of restating firms experience a turnover of at least one top manager within 24 months of the restatement compared to 35 percent among age‐, size‐, and industry‐matched firms. Moreover, the subsequent employment prospects of the displaced managers of restatement firms are poorer than those of the displaced managers of control firms. Our results hold after controlling for firm performance, bankruptcy, and other determinants of management turnover, and suggest that both corporate boards and the external labor market impose significant penalties on man...
Repairing a Mortgage Crisis: HOLC Lending and Its Impact on Local Housing Markets

Between 1933 and 1936 the Home Owners’ Loan Corporation purchased more than a million delinquent mortgages from private lenders and refinanced those loans for the borrowers. Its primary goal was to break the cycle of foreclosure, forced property sales and decreases in home values that was affecting local housing markets throughout the nation. We find that the volume of HOLC lending was related to measures of distress in local (county-level) housing markets and that these interventions increased 1940 median home values and homeownership rates, but not new home building.“[A] tremendous surge of residential building in the [last] decade… was matched by an ever-increasing supply of homes sold on easy terms [and only]… a small decline in prices was necessary to wipe out this equity. Unfortunately, deflationary processes are never satisfied with small declines in values. They feed upon themselves and produce results out of all proportion to their causes… In the field of real estate finance, particularly, we have depended so much upon credit that our whole value structure can be thrown out of balance by relatively slight shocks. When such a delicate structure is once disorganized, it is a tremendous task to get it into a position where it can again function normally.”1Henry Hoagland, 1935
Large Cash Transfers to the Elderly in South Africa

We examine the social pension in South Africa, where large cash sums-about twice the median per capita income of African households-are paid to people qualified by age but irrespective of previous contributions. We present the history of the scheme and use a 1993 nationally representative survey to investigate the redistributive consequences of the transfers, documenting who receive the pensions, their levels of living, and those of their families. We also look at behavioural effects, particularly the effects of the cash receipts on the allocation of income to food, schooling, transfers, and savings. In South Africa, a large 'social pension'-about twice the median per capita income of African households-is paid in cash to people qualified by age irrespective of previous contributions. We present the history of the scheme and explain how such large transfers could come about in an economy in which the recipients were not only politically weak, but without any political representation whatsoever. We then use a 1993 nationally representative survey to investigate the redistributive consequences of the transfers, documenting who receive the pensions, their levels of living, and those of their families. We also look at behavioural effects, particularly the effects of the cash receipts on the allocation of income to food, schooling, transfers, and savings. The pressing policy issue for South Africans is whether it makes sense to target seven billion rand (nearly $2 billion) of social expenditure through the current pension schemes. Our analysis contributes to the discussion by documenting the redistributive and behavioural effects of the transfers. We find that, at least as far as immediate incidence is concerned, and without allowance for behavioural effects, the social pension is an effective tool of redistribution, and that the households it reaches are predominantly poor. Because so many of the elderly among South Africa's African population live with children, the social pension is also effective in putting money into house
Complexity of parabolic systems

We first bound the codimension of an ancient mean curvature flow by the entropy. As a consequence, all blowups lie in a Euclidean subspace whose dimension is bounded by the entropy and dimension of the evolving submanifolds. This drastically reduces the complexity of the system. We use this in a major application of our new methods to give the first general bounds on generic singularities of surfaces in arbitrary codimension. We also show sharp bounds for codimension in arguably some of the most important situations of general ancient flows. Namely, we prove that in any dimension and codimension any ancient flow that is cylindrical at $-\infty $ must be a flow of hypersurfaces in a Euclidean subspace. This extends well-known classification results to higher codimension. The bound on the codimension in terms of the entropy is a special case of sharp bounds for spectral counting functions for shrinkers and, more generally, ancient flows. Shrinkers are solutions that evolve by scaling and are the singularity models for the flow. We show rigidity of cylinders as shrinkers in all dimension and all codimension in a very strong sense: Any shrinker, even in a large dimensional space, that is sufficiently close to a cylinder on a large enough, but compact, set is itself a cylinder. This is an important tool in the theory and is key for regularity; cf. (Colding and Minicozzi II in preprint, 2020).
Perfect tree forcings for singular cardinals

Abstract We investigate forcing properties of perfect tree forcings defined by Prikry to answer a question of Solovay in the late 1960's regarding first failures of distributivity. Given a strictly increasing sequence of regular cardinals 〈 κ n : n ω 〉 , Prikry defined the forcing P of all perfect subtrees of ∏ n ω κ n , and proved that for κ = sup n ω ⁡ κ n , assuming the necessary cardinal arithmetic, the Boolean completion B of P is ( ω , μ ) -distributive for all μ κ but ( ω , κ , δ ) -distributivity fails for all δ κ , implying failure of the ( ω , κ ) -d.l. These hitherto unpublished results are included, setting the stage for the following recent results. P satisfies a Sacks-type property, implying that B is ( ω , ∞ , κ ) -distributive. The ( h , 2 ) -d.l. and the ( d , ∞ , κ ) -d.l. fail in B . P ( ω ) / fin completely embeds into B . Also, B collapses κ ω to h . We further prove that if κ is a limit of countably many measurable cardinals, then B adds a minimal degree of constructibility for new ω-sequences. Some of these results generalize to cardinals κ with uncountable cofinality.
Langevin Equations in the Small-Mass Limit: Higher-Order Approximations

We study the small-mass (overdamped) limit of Langevin equations for a particle in a potential and/or magnetic field with matrix-valued and state-dependent drift and diffusion. We utilize a bootstrapping argument to derive a hierarchy of approximate equations for the position degrees of freedom that are able to achieve accuracy of order $$m^{\ell /2}$$ over compact time intervals for any $$\ell \in \mathbb {Z}^+$$. This generalizes prior derivations of the homogenized equation for the position degrees of freedom in the $$m\rightarrow 0$$ limit, which result in order $$m^{1/2}$$ approximations. Our results cover bounded forces, for which we prove convergence in $$L^p$$ norms and unbounded forces, in which case we prove convergence in probability.
Who Did the Ethanol Tax Credit Benefit? An Event Analysis of Subsidy Incidence

Abstract At the end of 2011, the Volumetric Ethanol Excise Tax Credit (VEETC), which had subsidized the blending of ethanol in gasoline, was allowed to expire. During its tenure, the subsidy was the subject of intense scrutiny concerning who benefited from its existence. Using commodity price data, we estimate the subsidy incidence accruing to corn farmers, ethanol producers, gasoline blenders, and gasoline consumers around the time of expiration. Our empirical approach contributes methodologically to the event studies literature by analyzing futures contract prices (as opposed to spot prices) when possible. Ultimately, we find compelling evidence that, at the date of VEETC expiration, ethanol producers captured about 25¢ of the 45¢ subsidy per gallon of ethanol blended. We find suggestive, albeit inconclusive, evidence that a portion of this benefit (about 5¢ per gallon) was passed further upstream from ethanol producers to corn farmers. Most of the remainder seems most likely to have been captured by the blenders themselves. On the petroleum side, we find no evidence that oil refiners captured any part of the subsidy. We also find no evidence that the subsidy was passed downstream to gasoline consumers in the form of lower gasoline prices.
Estimating 3D Camera Pose from 2D Pedestrian Trajectories

We consider the task of re-calibrating the 3D pose of a static surveillance camera, whose pose may change due to external forces, such as birds, wind, falling objects or earthquakes. Conventionally, camera pose estimation can be solved with a PnP (Perspective-n-Point) method using 2D-to-3D feature correspondences, when 3D points are known. However, 3D point annotations are not always available or practical to obtain in real-world applications. We propose an alternative strategy for extracting 3D information to solve for camera pose by using pedestrian trajectories. We observe that 2D pedestrian trajectories indirectly contain useful 3D information that can be used for inferring camera pose. To leverage this information, we propose a data-driven approach by training a neural network (NN) regressor to model a direct mapping from 2D pedestrian trajectories projected on the image plane to 3D camera pose. We demonstrate that our regressor trained only on synthetic data can be directly applied to real data, thus eliminating the need to label any real data. We evaluate our method across six different scenes from the Town Centre Street and DUKEMTMC datasets. Our method achieves an improvement of ~ 50% on both position and orientation prediction accuracy when compared to other SOTA methods.
Necessity and the COVID-19 Pandemic

As the global economic downturn from the coronavirus worsens, many sovereign debtors will have to choose between paying creditors and fighting the virus As of
Transient Intestinal Colonization by a Live-Attenuated Oral Cholera Vaccine Induces Protective Immune Responses in Streptomycin-Treated Mice.

Current mouse models for evaluating the efficacy of live oral cholera vaccines (OCVs) have important limitations. Conventionally raised adult mice are resistant to intestinal colonization by Vibrio cholerae, but germ free mice can be colonized and have been used to study OCV immunogenicity. However, germ free animals have impaired immune systems and intestinal physiology; also, live OCVs colonize germ free mice for many months, which does not mimic the clearance kinetics of live OCVs in humans. Here we leverage antibiotic-treated, conventionally raised adult mice to study the effects of transient intestinal colonization by a live OCV V. cholerae strain. In a single dose vaccination regimen, we found that HaitiV, a live-attenuated OCV candidate, was cleared by streptomycin treated adult mice within two weeks after oral inoculation. This transient colonization elicited far stronger adaptive immune correlates of protection against cholera than did inactivated whole-cell HaitiV. Infant mice from HaitiV vaccinated dams were also significantly more protected from choleric disease than pups from inactivated-HaitiV dams. Our findings establish the benefits of antibiotic treated mice for live OCV studies as well as its limitations and underscore the immunogenicity of HaitiV. Importance Oral cholera vaccines (OCVs) are being deployed to combat cholera but current killed OCVs require multiple doses and show little efficacy in young children. Live OCVs have the potential to overcome these limitations but small animal models for testing OCVs have shortcomings. We used an antibiotic treatment protocol for conventional adult mice to study the effects of short-term colonization by a single dose of HaitiV, a live OCV candidate. Vaccinated mice developed vibriocidal antibodies against V. cholerae and delivered pups that were resistant to cholera, whereas mice vaccinated with inactivated-HaitiV did not. These findings demonstrate HaitiV9s immunogenicity and suggest that this antibiotic treatment protocol will be useful for evaluating the efficacy of live OCVs.
On the degeneracy of integral points and entire curves in the complement of nef effective divisors

Abstract As a consequence of the divisorial case of our recently established generalization of Schmidt's subspace theorem, we prove a degeneracy theorem for integral points on the complement of a union of nef effective divisors. A novel aspect of our result is the attainment of a strong degeneracy conclusion (arithmetic quasi-hyperbolicity) under weak positivity assumptions on the divisors. The proof hinges on applying our recent theorem with a well-situated ample divisor realizing a certain lexicographical minimax. We also explore the connections with earlier work by other authors and make a conjecture regarding bounds for the numbers of divisors necessary, including consideration of the question of arithmetic hyperbolicity. Under the standard correspondence between statements in Diophantine approximation and Nevanlinna theory, one obtains analogous degeneration statements for entire curves.
Estimation and validation of individualized dynamic brain models with resting state fMRI.

Abstract A key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1–3 ​min per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.
Algorithmic versatility of SPF-regularization methods

Sparsity promoting functions (SPFs) are commonly used in optimization problems to find solutions which are sparse in some basis. For example, the l1-regularized wavelet model and the Rudin–Osher–Fa...
Differences in consumer-benefiting misconduct by nonprofit, for-profit, and public organizations

Abstract We examine how organizations of different types – nonprofit, for-profit, and public – engage in consumer-benefiting misconduct (CBM) by examining which patients benefit from hospitals of the three types gaming the market for liver transplants. Consistent with our theory, we find that public firms are the least likely of the three organization types to engage in CBM. We find that for-profit firms engage in CBM for any paying consumers, nonprofit firms engage in CBM for their mission-driven beneficiaries, and public organizations engage in CBM for the set of consumers that their voting constituents direct them to serve. We also examine how the three organization types respond to others’ engagement in CBM, and explore heterogeneity in CBM within nonprofit and public organizations. As the first paper to theoretically predict and empirically examine differences in CBM across nonprofit, public, and for-profit firms, this paper has important implications for our understanding of how distinct governance structures influence misconduct.
Enhanced flight performance in non-uniformly flexible wings

The flexibility of biological propulsors such as wings and fins is believed to contribute to the higher performance of flying and swimming animals compared with their engineered peers. Flexibility ...
Extrapolating the profile of a finite population

We study a prototypical problem in empirical Bayes. Namely, consider a population consisting of $k$ individuals each belonging to one of $k$ types (some types can be empty). Without any structural restrictions, it is impossible to learn the composition of the full population having observed only a small (random) subsample of size $m = o(k)$. Nevertheless, we show that in the sublinear regime of $m =\omega(k/\log k)$, it is possible to consistently estimate in total variation the \emph{profile} of the population, defined as the empirical distribution of the sizes of each type, which determines many symmetric properties of the population. We also prove that in the linear regime of $m=c k$ for any constant $c$ the optimal rate is $\Theta(1/\log k)$. Our estimator is based on Wolfowitz's minimum distance method, which entails solving a linear program (LP) of size $k$. We show that there is a single infinite-dimensional LP whose value simultaneously characterizes the risk of the minimum distance estimator and certifies its minimax optimality. The sharp convergence rate is obtained by evaluating this LP using complex-analytic techniques.
Safety of Same and Next Day Discharge Following Revision Hip and Knee Arthroplasty Using Modern Perioperative Protocols.

Abstract Background Advances in perioperative care have enabled early discharge and outpatient primary total joint arthroplasty (TJA). However, the safety of early discharge after revision TJA (rTJA) remains unknown and the COVID-19 pandemic will force decreased hospitalization. This study compared 90-day outcomes in patients undergoing aseptic rTJA discharged the same or next day (early) to those discharged 2 or 3 days postoperatively (later). Methods In total, 530 aseptic rTJAs performed at a single tertiary care referral center (December 5, 2011 to December 30, 2019) were identified. Early and later discharge patients were matched as closely as possible on procedure type, sex, American Society of Anesthesiologists physical status classification, age, and body mass index. All patients were optimized using modern perioperative protocols. The rate of 90-day emergency department (ED) visits and hospital admissions was compared between groups. Results In total, 183 early discharge rTJAs (54 hips, 129 knees) in 178 patients were matched to 183 later discharge rTJAs (71 hips, 112 knees) in 165 patients. Sixty-two percent of the sample was female, with an overall average age and body mass index of 63 ± 9.9 (range: 18-92) years and 32 ± 6.9 (range: 18-58) kg/m2. There was no statistical difference in 90-day ED visit rates between early (6/178, 3.4%) and later (11/165, 6.7%) discharge patients (P = .214). Ninety-day hospital admission rates for early (7/178, 3.9%) and later (4/165, 2.4%) discharges did not differ (P = .545). Conclusion Using modern perioperative protocols with appropriate patient selection, early discharge following aseptic rTJA does not increase 90-day readmissions or ED visits. As hospital inpatient capacity remains limited due to COVID-19, select rTJA patients may safely discharge home the same or next day to preserve hospital beds and resources for more critical illness.
Deciphering the protein motion of S1 subunit in SARS-CoV-2 spike glycoprotein through integrated computational methods.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major worldwide public health emergency that has infected over 8 million people. Spike glycoprotein, especially the parti...
Semiclassical model for calculating exciton and polaron pair energetics at interfaces

Abstract Exciton and polaron pair dissociation is a key functional aspect of photovoltaic devices. To improve upon the current state of interfacial transport models, we augment the existing classical models of dielectric interfaces by incorporating results from ab initio calculations, allowing us to calculate exciton and polaron binding energies more accurately. We demonstrate the predictive capabilities of this new model using two interfaces: (i) the boron subphthalocyanine chloride (SubPc) and C60 interface, which is an archetype for many organic photovoltaic devices; and (ii) pentacene and silicon (1 0 0), which represents a hybrid between organic and inorganic semiconductors. Our calculations predict that the insertion of molecular dipoles at interfaces can be used for improving polaron pair dissociation and that sharp transitions in dielectric permittivity can have a stronger effect on the polaron pair dissociation than even the electron-hole Coulomb interaction.
A Primer on Laplacian Dynamics in Directed Graphs

We analyze the asymptotic behavior of general first order Laplacian processes on digraphs. The most important ones of these are diffusion and consensus with both continuous and discrete time. We treat diffusion and consensus as dual processes. This is the first complete exposition of this material in a single work.
On Training Robust {PDF} Malware Classifiers

Although state-of-the-art PDF malware classifiers can be trained with almost perfect test accuracy (99%) and extremely low false positive rate (under 0.1%), it has been shown that even a simple adversary can evade them. A practically useful malware classifier must be robust against evasion attacks. However, achieving such robustness is an extremely challenging task. In this paper, we take the first steps towards training robust PDF malware classifiers with verifiable robustness properties. For instance, a robustness property can enforce that no matter how many pages from benign documents are inserted into a PDF malware, the classifier must still classify it as malicious. We demonstrate how the worst-case behavior of a malware classifier with respect to specific robustness properties can be formally verified. Furthermore, we find that training classifiers that satisfy formally verified robustness properties can increase the evasion cost of unbounded (i.e., not bounded by the robustness properties) attackers by eliminating simple evasion attacks. Specifically, we propose a new distance metric that operates on the PDF tree structure and specify two classes of robustness properties including subtree insertions and deletions. We utilize state-of-the-art verifiably robust training method to build robust PDF malware classifiers. Our results show that, we can achieve 92.27% average verified robust accuracy over three properties, while maintaining 99.74% accuracy and 0.56% false positive rate. With simple robustness properties, our robust model maintains 7% higher robust accuracy than all the baseline models against unrestricted whitebox attacks. Moreover, the state-of-the-art and new adaptive evolutionary attackers need up to 10 times larger $L_0$ feature distance and 21 times more PDF basic mutations (e.g., inserting and deleting objects) to evade our robust model than the baselines.
Analog Subspace Coding: A New Approach to Coding for Non-Coherent Wireless Networks

We provide a precise framework to study subspace codes for non-coherent communications in wireless networks. To this end, an analog operator channel is defined with inputs and outputs being subspaces of ${\mathbb{C}^n}$. Then a certain distance is defined to capture the performance of subspace codes in terms of their capability to recover from interference and rank-deficiency of the network. We also study the robustness of the proposed model with respect to additive noise. Furthermore, we propose a new approach to construct subspace codes in the analog domain, also regarded as Grassmann codes, by leveraging polynomial evaluations over finite fields together with characters associated to finite fields that map their elements to the unit circle in the complex plane. The constructed codes, referred to as character-polynomial (CP) codes, are shown to perform better compared to other existing constructions of Grassmann codes in terms of the trade-off between the rate and the normalized minimum distance, for a wide range of values for n.
Spectral Algebra Models of Unstable \(v_n\)-Periodic Homotopy Theory

We give a survey of a generalization of Quillen–Sullivan rational homotopy theory which gives spectral algebra models of unstable \(v_n\)-periodic homotopy types. In addition to describing and contextualizing our original approach, we sketch two other recent approaches which are of a more conceptual nature, due to Arone-Ching and Heuts. In the process, we also survey many relevant concepts which arise in the study of spectral algebra over operads, including topological Andre-Quillen cohomology, Koszul duality, and Goodwillie calculus.
Extended Second-Order Multireference Algebraic Diagrammatic Construction Theory for Charged Excitations.

We report a new implementation of multireference algebraic diagrammatic construction theory (MR-ADC) for simulations of electron attachment and ionization in strongly correlated molecular systems (EA/IP-MR-ADC). Following our recent work on IP-MR-ADC [J. Chem. Theory Comput.2019,15, 5908], we present the first implementation of the second-order MR-ADC method for electron attachment (EA-MR-ADC(2)) and two extended second-order approximations (EA- and IP-MR-ADC(2)-X) that incorporate a partial treatment of third-order electron correlation effects. Introducing a small approximation for the second-order amplitudes of the effective Hamiltonian, our implementation of EA- and IP-MR-ADC(2)-X has a low O(M5) computational scaling with the basis set size M. Additionally, we describe an efficient algorithm for solving the first-order amplitude equations in MR-ADC and partially contracted second-order N-electron valence perturbation theory (NEVPT2) which completely avoids computation of the four-particle reduced density matrices without introducing any approximations or imaginary-time propagation. For a benchmark set of eight small molecules, a carbon dimer, and a twisted ethylene, we demonstrate that EA- and IP-MR-ADC(2)-X achieve an accuracy similar to that of strongly contracted NEVPT2 while having a lower computational scaling with the active space size and providing efficient access to transition properties.
Deep-learning approaches to identify critically Ill patients at emergency department triage using limited information.

Study objective Triage quickly identifies critically ill patients, facilitating timely interventions. Many emergency departments (EDs) use emergency severity index (ESI) or abnormal vital sign triggers to guide triage. However, both use fixed thresholds, and false activations are costly. Prior approaches using machinelearning have relied on information that is often unavailable during the triage process. We examined whether deep-learning approaches could identify critically ill patients only using data immediately available at triage. Methods We conducted a retrospective, cross-sectional study at an urban tertiary care center, from January 1, 2012-January 1, 2020. De-identified triage information included structured (age, sex, initial vital signs) and textual (chief complaint) data, with critical illness (mortality or ICU admission within 24 hours) as the outcome. Four progressively complex deep-learning models were trained and applied to triage information from all patients. We compared the accuracy of the models against ESI as the standard diagnostic test, using area under the receiver-operator curve (AUC). Results A total of 445,925 patients were included, with 60,901 (13.7%) critically ill. Vital sign thresholds identified critically ill patients with AUC 0.521 (95% confidence interval [CI] = 0.519-0.522), and ESI <3 demonstrated AUC 0.672 (95% CI = 0.671-0.674), logistic regression classified patients with AUC 0.803 (95% CI = 0.802-0.804), 2-layer neural network with structured data with AUC 0.811 (95% CI = 0.807-0.815), gradient tree boosting with AUC 0.820 (95% CI = 0.818-0.821), and the neural network model with textual data with AUC 0.851 (95% CI = 0.849-0.852). All successive increases in AUC were statistically significant. Conclusion Deep-learning techniques represent a promising method of augmenting triage, even with limited information. Further research is needed to determine if improved predictions yield clinical and operational benefits.
The methyltransferase DOT1L limits activation and the Th1 program in CD4+ T cells during infection and inflammation

Abstract CD4+ T helper (Th) cell differentiation is controlled by lineage-specific expression of transcription factors and effector proteins, as well as silencing of lineage-promiscuous genes. Lysine methyltransferases (KMTs) comprise a major class of epigenetic enzymes that are emerging as important regulators of Th cell biology. Here, we show that the KMT DOT1L regulates Th cell function and lineage integrity. DOT1L-dependent dimethylation of lysine 79 of histone H3 (H3K79me2) is associated with lineage-specific gene expression. However, DOT1L-deficient Th cells overproduce IFN-γ under lineage-specific and lineage-promiscuous conditions. Consistent with the increased IFN-γ response, mice with a T cell-specific deletion of DOT1L are susceptible to infection with the helminth parasite Trichuris muris and resistant to the development of allergic lung inflammation. These results identify a central role for DOT1L in Th cell lineage commitment and stability, and suggest that inhibition of DOT1L may provide a novel therapeutic strategy to limit type 2 immune responses.
Lorentzian inversion and anomalous dimensions in Mellin space

In this note, we derive a Mellin space version of the Lorentzian inversion formula for CFTs by explicitly integrating over the cross-ratios in d = 2 and d = 4 spacetime dimensions. We use the simplicity of the Mellin representation of Witten diagrams and the double discontinuity to find the OPE coefficients and anomalous dimensions of double- trace primaries in large N CFTs to order $$ \frac{1}{N^4} $$ . We find that our results match analytically at order $$ \frac{1}{N^2} $$ , and numerically at order $$ \frac{1}{N^4} $$ with existing literature.
Efficient counter-factual type error debugging

Abstract Providing effective error messages in response to type errors continues to be a challenge in functional programming. Type error messages often point to bogus error locations or lack sufficient information for removing the type error, making error debugging ineffective. Counter-factual typing (CFT) addressed this problem by generating comprehensive error messages with each message includes a rich set of information. However, this comes with a cost of huge computations, making it too slow for interactive use. In particular, our recent study shows that programmers usually have to go through multiple iterations of updating and recompiling programs to remove a type error. Interestingly, our study also finds that program updates are minor in each iteration during type error debugging. We exploit this fact and develop eCFT, an efficient version of CFT, which doesn't recompute all error fixes from scratch for each updated program but only recomputes error fixes that are changed in response to the update. Our key observation is that minor program changes lead to minor error suggestion changes. eCFT is based on principal typing, a typing scheme more amenable to reuse previous typing results. We have evaluated our approach and found it is about 12.4× faster than CFT in updating error fixes.
A Unified, Stable and Accurate Meshfree Framework for Peridynamic Correspondence Modeling—Part I: Core Methods

The overarching goal of this work is to develop an accurate, robust, and stable methodology for finite deformation modeling using strong-form peridynamics (PD) and the correspondence modeling framework. We adopt recently developed methods that make use of higher-order corrections to improve the computation of integrals in the correspondence formulation. A unified approach is presented that incorporates the reproducing kernel (RK) and generalized moving least square (GMLS) approximations in PD to obtain non-local gradients of higher-order accuracy. We show, however, that the improved quadrature rule does not suffice to handle instability issues that have proven problematic for the correspondence model-based PD. In Part I of this paper, a bond-associative, higher-order core formulation is developed that naturally provides stability without introducing artificial stabilization parameters. Numerical examples are provided to study the convergence of RK-PD, GMLS-PD, and their bond-associated versions to a local counterpart, as the degree of non-locality (i.e., the horizon) approaches zero. Problems from linear elastostatics are utilized to verify the accuracy and stability of our approach. It is shown that the bond-associative approach improves the robustness of RK-PD and GMLS-PD formulations, which is essential for practical applications. The higher-order, bond-associated model can obtain second-order convergence for smooth problems and first-order convergence for problems involving field discontinuities, such as curvilinear free surfaces. In Part II of this paper we use our unified PD framework to (a) study wave propagation phenomena, which have proven problematic for the state-based correspondence PD framework; (b) propose a new methodology to enforce natural boundary conditions in correspondence PD formulations, which should be particularly appealing to coupled problems. Our results indicate that bond-associative methods accompanied by higher-order gradient corrections provide the key ingredients to obtain the necessary accuracy, stability, and robustness characteristics needed for engineering-scale simulations.
Tight Bounds on Sensitivity and Block Sensitivity of Some Classes of Transitive Functions

Nisan and Szegedy [16] conjectured that block sensitivity is at most polynomial in sensitivity for any Boolean function. Until a recent breakthrough of Huang [14], the conjecture had been wide open in the general case, and was proved only for a few special classes of Boolean functions. Huang’s result [14] implies that block sensitivity is at most the 4th power of sensitivity for any Boolean function. It remains open if a tighter relationship between sensitivity and block sensitivity holds for arbitrary Boolean functions; the largest known gap between these measures is quadratic [3, 8, 9, 11, 18, 21].
Pregnancy success in mice requires appropriate cannabinoid receptor signaling for primary decidua formation.

With implantation, mouse stromal cells begin to transform into epithelial-like cells surrounding the implantation chamber forming an avascular zone called the primary decidual zone (PDZ). In the mouse, the PDZ forms a transient, size-dependent permeable barrier to protect the embryo from maternal circulating harmful agents. The process of decidualization is critical for pregnancy maintenance in mice and humans. Mice deficient in cannabinoid receptors, CB1 and CB2, show compromised PDZ with dysregulated angiogenic factors, resulting in the retention of blood vessels and macrophages. This phenotype is replicated in Cnr1-/- but not in Cnr2-/-mice. In vitro decidualization models suggest that Cnr1 levels substantially increase in mouse and human decidualizing stromal cells, and that neutralization of CB1 signaling suppresses decidualization and misregulates angiogenic factors. Taken together, we propose that implantation quality depends on appropriate angiogenic events driven by the integration of CB2 in endothelial cells and CB1 in decidual cells.
Improving the Usability of Virtual Reality Neuron Tracing with Topological Elements

Researchers in the field of connectomics are working to reconstruct a map of neural connections in the brain in order to understand at a fundamental level how the brain processes information. Constructing this wiring diagram is done by tracing neurons through high-resolution image stacks acquired with fluorescence microscopy imaging techniques. While a large number of automatic tracing algorithms have been proposed, these frequently rely on local features in the data and fail on noisy data or ambiguous cases, requiring time-consuming manual correction. As a result, manual and semi-automatic tracing methods remain the state-of-the-art for creating accurate neuron reconstructions. We propose a new semi-automatic method that uses topological features to guide users in tracing neurons and integrate this method within a virtual reality (VR) framework previously used for manual tracing. Our approach augments both visualization and interaction with topological elements, allowing rapid understanding and tracing of complex morphologies. In our pilot study, neuroscientists demonstrated a strong preference for using our tool over prior approaches, reported less fatigue during tracing, and commended the ability to better understand possible paths and alternatives. Quantitative evaluation of the traces reveals that users' tracing speed increased, while retaining similar accuracy compared to a fully manual approach.
Assistive VR Gym: Interactions with Real People to Improve Virtual Assistive Robots

Versatile robotic caregivers could benefit millions of people worldwide, including older adults and people with disabilities. Recent work has explored how robotic caregivers can learn to interact with people through physics simulations, yet transferring what has been learned to real robots remains challenging. Virtual reality (VR) has the potential to help bridge the gap between simulations and the real world. We present Assistive VR Gym (AVR Gym), which enables real people to interact with virtual assistive robots. We also provide evidence that AVR Gym can help researchers improve the performance of simulation-trained assistive robots with real people. Prior to AVR Gym, we trained robot control policies (Original Policies) solely in simulation for four robotic caregiving tasks (robot-assisted feeding, drinking, itch scratching, and bed bathing) with two simulated robots (PR2 from Willow Garage and Jaco from Kinova). With AVR Gym, we developed Revised Policies based on insights gained from testing the Original policies with real people. Through a formal study with eight participants in AVR Gym, we found that the Original policies performed poorly, the Revised policies performed significantly better, and that improvements to the biomechanical models used to train the Revised policies resulted in simulated people that better match real participants. Notably, participants significantly dis-agreed that the Original policies were successful at assistance, but significantly agreed that the Revised policies were successful at assistance. Overall, our results suggest that VR can be used to improve the performance of simulation-trained control policies with real people without putting people at risk, thereby serving as a valuable stepping stone to real robotic assistance.
Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach.

Explanations—a form of post-hoc interpretability—play an instrumental role in making systems accessible as AI continues to proliferate complex and sensitive sociotechnical systems. In this paper, we introduce Human-centered Explainable AI (HCXAI) as an approach that puts the human at the center of technology design. It develops a holistic understanding of “who” the human is by considering the interplay of values, interpersonal dynamics, and the socially situated nature of AI systems. In particular, we advocate for a reflective sociotechnical approach. We illustrate HCXAI through a case study of an explanation system for non-technical end-users that shows how technical advancements and the understanding of human factors co-evolve. Building on the case study, we lay out open research questions pertaining to further refining our understanding of “who” the human is and extending beyond 1-to-1 human-computer interactions. Finally, we propose that a reflective HCXAI paradigm—mediated through the perspective of Critical Technical Practice and supplemented with strategies from HCI, such as value-sensitive design and participatory design—not only helps us understand our intellectual blind spots, but it can also open up new design and research spaces.
Data-Driven Learning and Load Ensemble Control

Abstract Demand response (DR) programs aim to engage distributed small-scale flexible loads, such as thermostatically controllable loads (TCLs), to provide various grid support services. Linearly Solvable Markov Decision Process (LS-MDP), a variant of the traditional MDP, is used to model aggregated TCLs. Then, a model-free reinforcement learning technique called Z-learning is applied to learn the value function and derive the optimal policy for the DR aggregator to control TCLs. The learning process is robust against uncertainty that arises from estimating the passive dynamics of the aggregated TCLs. The efficiency of this data-driven learning is demonstrated through simulations on Heating, Cooling & Ventilation (HVAC) units in a testbed neighborhood of residential houses.
Hyperfiniteness and Borel combinatorics

Author(s): Conley, Clinton; Jackson, Steve; Marks, Andrew; Seward, Brandon; Tucker-Drob, Robin
Extreme Ultraviolet Late-Phase Flares: Before and During the Solar Dynamics Observatory Mission

The solar extreme-ultraviolet (EUV) observations from the Solar Dynamics Observatory (SDO) have revealed interesting characteristics of warm coronal emissions, such as Fe xvi 335 A emission, which peak soon after the hot coronal X-ray emissions peak during a flare and then sometimes peak for a second time hours after the X-ray flare peak. This flare type, with two warm coronal emission peaks but only one X-ray peak, has been named the EUV late phase (Woods et al., Astrophys. J. 739, 59, 2011). These flares have the distinct properties of i) having a complex magnetic-field structure with two initial sets of coronal loops, with one upper set overlaying a lower set, ii) having an eruptive flare initiated in the lower set and disturbing both loop sets, iii) having the hot coronal emissions emitted only from the lower set in conjunction with the X-ray peak, and iv) having the first peak of the warm coronal emissions associated with the lower set and its second peak emitted from the upper set many minutes to hours after the first peak and without a second X-ray enhancement. The disturbance of the coronal loops by the eruption is at about the same time, but the relaxation and cooling down of the heated coronal loops during the post-flare reconnections have different time scales with the longer, upper loops being significantly delayed from the lower loops. The difference in these cooling time scales is related to the difference between the two peak times of the warm coronal emission and is also apparent in the decay profile of the X-ray emissions having two distinct decays, with the first decay slope being steeper (faster) and the delayed decay slope being smaller (slower) during the time of the warm-coronal-emission second peak. The frequency and relationship of the EUV late-phase decay times between the Fe xvi 335 A two flare peaks and X-ray decay slopes are examined using three years of SDO/EUV Variability Experiment (EVE) data, and the X-ray dual-decay character is then exploited to estimate the frequency of EUV late-phase flares during the past four solar cycles. This study indicates that the frequency of EUV late-phase flares peaks before and after each solar-cycle minimum.
Density distribution in the liquid Hg-sapphire interface.

We present the results of a computer simulation study of the liquid density distribution normal to the interface between liquid Hg and the reconstructed (0001) face of sapphire. The simulations are...
Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices

In this paper, we present a new approach to learning cascaded classifiers for use in computing environments that involve networks of heterogeneous and resource-constrained, low-power embedded compute and sensing nodes. We present a generalization of the classical linear detection cascade to the case of tree-structured cascades where different branches of the tree execute on different physical compute nodes in the network. Different nodes have access to different features, as well as access to potentially different computation and energy resources. We concentrate on the problem of jointly learning the parameters for all of the classifiers in the cascade given a fixed cascade architecture and a known set of costs required to carry out the computation at each node. To accomplish the objective of joint learning of all detectors, we propose a novel approach to combining classifier outputs during training that better matches the hard cascade setting in which the learned system will be deployed. This work is motivated by research in the area of mobile health where energy efficient real time detectors integrating information from multiple wireless on-body sensors and a smart phone are needed for real-time monitoring and the delivery of just-in-time adaptive interventions. We evaluate our framework on mobile sensor-based human activity recognition and mobile health detector learning problems.
Tunable Graphene Metasurface Reflectarray for Cloaking, Illusion, and Focusing

Can one size really fit all? The authors demonstrate the versatility of a graphene-based metasurface that is capable of actively controlling anomalous beam steering, focusing, cloaking, and illusion optics. These various functionalities are usually discussed in disparate fashion, but this work shows that they can all be described within a general framework for arbitrary surface morphology. This unified, simplified approach seems quite appealing for device design.