cssi/SciSciGPT-SciSciNet huggingface dataset:
Reducing and Redistributing Unpaid Work: Stronger Policies to Support Gender Equality

Unpaid work, such as caring for children, the elderly, and household chores represents a significant share of economic activity but is not counted as part of GDP. Women disproportionately shoulder the burden of unpaid work: on average, women do two more hours of unpaid work per day than men, with large differences across countries. While much unpaid care work is done entirely by choice, constraints imposed by cultural norms, labor market features or lack of public services, infrastructure, and family-friendly policies matter. This undermines female labor force participation and lowers economy-wide productivity. In this paper, we examine recent trends in unpaid work around the world using aggregate and individual-level data, explore potential drivers, and identify policies that can help reduce and redistribute unpaid work across genders. Conservative model-based estimates suggest that the gains from these policies could amount to up to 4 percent of GDP.
Can Shallow Neural Networks Beat the Curse of Dimensionality? A Mean Field Training Perspective

We prove that the gradient descent training of a two-layer neural network on empirical or population risk may not decrease population risk at an order faster than $t^{-4/(d-2)}$ under mean field scaling. The loss functional is mean squared error with a Lipschitz-continuous target function and data distributed uniformly on the $d$ -dimensional unit cube. Thus gradient descent training for fitting reasonably smooth, but truly high-dimensional data may be subject to the curse of dimensionality. We present numerical evidence that gradient descent training with general Lipschitz target functions becomes slower and slower as the dimension increases, but converges at approximately the same rate in all dimensions when the target function lies in the natural function space for two-layer ReLU networks. Impact Statement –Artificial neural networks perform well in many real life applications, but may suffer from the curse of dimensionality on certain problems. We provide theoretical and numerical evidence that this may be related to whether a target function lies in the hypothesis class described by infinitely wide networks. The training dynamics are considered in the fully non-linear regime and not reduced to neural tangent kernels. We believe that it will be essential to study these hypothesis classes in detail to choose an appropriate machine learning models for a given problem. The goal of the article is to illustrate this in a mathematically sound and numerically convincing fashion.
The effects of two intramuscular sedation protocols on echocardiographic variables in cats following sedation and blood donation.

OBJECTIVE To compare effects of 2 IM sedation protocols, alfaxalone-butorphanol (AB) versus dexmedetomidine-butorphanol (DB), on echocardiographic (ECHO) variables in cats following sedation and blood donation. DESIGN Experimental randomized, blinded crossover study. SETTING University teaching hospital. ANIMALS Eleven client-owned healthy cats. INTERVENTIONS Cats received a baseline ECHO without sedation prior to their first donation. Cats were sedated intramuscularly with AB (alfaxalone, 2 mg/kg, and butorphanol, 0.2 mg/kg) for 1 donation and DB (dexmedetomidine, 10 μg/kg, and butorphanol 0.2, mg/kg) for another, with a minimum 6 weeks between donations. A post-sedation, post-donation ECHO was performed after each blood donation. MEASUREMENTS AND MAIN RESULTS Eight cats completed the study. Compared to baseline, DB combined with blood donation decreased heart rate (-84/min; P < 0.0001), fractional shortening (-16.5%; P < 0.0001), ejection fraction (-21.0%; P = 0.0002), and cardiac output (-292 mL/min, P = 0.0001); AB combined with blood donation increased heart rate (+45/min; P = 0.0003) and decreased left ventricular end diastolic volume (-1.57 mL; P < 0.0001). Compared to AB, DB decreased heart rate (-129/min; P < 0.0001) and fractional shortening (-21.6%; P < 0.0001) and increased left ventricular end-systolic (+1.14 mL; P = 0.0004) and diastolic volumes (+1.93 mL; P < 0.0002). Cats administered DB had a significant increase in regurgitant flow across mitral, aortic, and pulmonic valves following blood donation (P < 0.05). One cat administered DB developed spontaneous echo contrast in the left ventricle following donation. CONCLUSIONS AND CLINICAL RELEVANCE Compared to AB, DB had more pronounced effects on ECHO variables in cats following IM sedation and blood donation. Due to its minimal impact on ECHO variables, AB may be a more desirable sedation protocol in this population of cats.
Presidential Popular Constitutionalism

ion, from the rights that had secured it in the free-labor scheme to the conditions the rights were meant to maintain: autonomy and freedom from domination. In a changed world, Roosevelt argued, those conditions might require legal instruments, the opposite of those that had preserved it in an earlier time. At one time, firm property rights had helped to free men and women from monarchical tyranny, feudal privilege, and enslavement, the defining enemies of free labor.76 Now, however, a complex economy, built from those same property rights, created not security, but insecurity, 71. See supra text accompanying note 70. 72. Franklin Delano Roosevelt, Address to the Commonwealth Club of San Francisco (Sept. 23, 1932). 73. Id. 74. Id. 75. Id. 76. Id. PURDY FOR BP 3/2/2009 7:02:06 AM 1854 FORDHAM LAW REVIEW [Vol. 77 the vulnerability to “great social and economic forces” that Wilson had evoked.77 The solution was to revise and abridge those rights to create new sources of economic security and opportunity.78 According to Roosevelt, this was not an anti-individualist position: it was only a way of doing for industrial self-reliance what simpler economic arrangements had done in the agrarian age. Twentieth-century Americans needed “a more permanently safe order of things. . . . not to hamper individualism, but to protect it.”79 What was required was creating the institutional conditions in which individuals could freely flourish, the aim that this Article calls “ecological enablement.” Lyndon Johnson later extended Roosevelt’s idea in a description of his “Great Society,” now often remembered as a half-failed war on poverty, but intended as a new vision of democratic life in an affluent age. He portrayed that society as one of ceaseless self-discovery and self-creation, “not . . . the ordered, changeless, and sterile battalion of the ants. . . . [but] the excitement of becoming—always becoming, trying, probing, falling, resting, and trying again—but always trying and always gaining.”80 Roosevelt had described the founding spirit of the United States as the power to begin the world anew in one’s own life, and had called that spirit the engine of democracy throughout history.81 Johnson rendered this ideal as an individual goal made possible by an ecology of laws and institutions, forming a humanist paradise where leisure is a welcome chance to build and reflect, not a feared cause of boredom and restlessness. . . . where the city of man serves not only the needs of the body and demands of commerce but the desire for beauty and the hunger for community. . . .
Composition of terrestrial exoplanet atmospheres from meteorite outgassing experiments

Terrestrial exoplanets likely form initial atmospheres through outgassing during and after accretion, although there is currently no first-principles understanding of how to connect a planet’s bulk composition to its early atmospheric properties. Important insights into this connection can be gained by assaying meteorites, which are representative samples of planetary building blocks. We perform laboratory outgassing experiments that use a mass spectrometer to measure the abundances of volatiles released when meteorite samples are heated to 1,200 °C. We find that outgassing from three carbonaceous chondrite samples consistently produce H2O-rich (average of ~66%) atmospheres but with substantial amounts of CO (~18%) and CO2 (~15%) as well as smaller quantities of H2 and H2S (up to 1%). These results provide experimental constraints on the initial chemical composition in theoretical models of terrestrial planet atmospheres, and supply abundances for principal gas species as a function of temperature. Laboratory experiments where meteorites are heated up to 1,200 °C are performed in order to inform on the outgassing from exoplanetary interiors. Differing considerably from assumptions commonly used by models, the experiments indicate that terrestrial planets would form water-rich steam atmospheres with substantial amounts of CO2 and CO.
Ferroportin 3 is a dual-targeted mitochondrial/chloroplast iron exporter necessary for iron homeostasis in Arabidopsis.

Mitochondria and chloroplasts are organelles with high iron demand that are particularly susceptible to iron-induced oxidative stress. Despite the necessity of strict iron regulation in these organelles, much remains unknown about mitochondrial and chloroplast iron transport in plants. Here, we propose that Arabidopsis ferroportin 3 (FPN3) is an iron exporter that is dual-targeted to mitochondria and chloroplasts. FPN3 is expressed in shoots, regardless of iron conditions, but its transcripts accumulate under iron deficiency in roots. fpn3 mutants cannot grow as well as the wild type under iron-deficient conditions and their shoot iron levels are lower compared with the wild type. Analyses of iron homeostasis gene expression in fpn3 mutants and inductively coupled plasma mass spectrometry (ICP-MS) measurements show that iron levels in the mitochondria and chloroplasts are increased relative to the wild type, consistent with the proposed role of FPN3 as a mitochondrial/plastid iron exporter. In iron-deficient fpn3 mutants, abnormal mitochondrial ultrastructure was observed, whereas chloroplast ultrastructure was not affected, implying that FPN3 plays a critical role in the mitochondria. Overall, our study suggests that FPN3 is essential for optimal iron homeostasis.
A Generalization of a Theorem of Carnot

Theorem of Carnot: Let M be a point on the diagonal AC of an arbitrary quadrilateral ABCD. Through M one draws a line which intersects AB in α and BC in β.
Stabbing Convex Bodies with Lines and Flats

We study the problem of constructing weak e-nets where the stabbing elements are lines or k-flats instead of points. We study this problem in the simplest setting where it is still interesting - namely, the uniform measure of volume over the hypercube [0,1]^d. Specifically, a (k,e)-net is a set of k-flats, such that any convex body in [0,1]^d of volume larger than e is stabbed by one of these k-flats. We show that for k ≥ 1, one can construct (k,e)-nets of size O(1/e^{1-k/d}). We also prove that any such net must have size at least Ω(1/e^{1-k/d}). As a concrete example, in three dimensions all e-heavy bodies in [0,1]³ can be stabbed by Θ(1/e^{2/3}) lines. Note, that these bounds are sublinear in 1/e, and are thus somewhat surprising.
Bifurcation for a free boundary problem modeling a small arterial plaque

Abstract Atherosclerosis, hardening of the arteries, originates from small plaques in the arteries; it is a major cause of disability and premature death in the United States and worldwide. In this paper, we study the bifurcation of a highly nonlinear and highly coupled PDE model describing the growth of an arterial plaque in the early stage of atherosclerosis. The model involves LDL and HDL cholesterols, macrophage cells, and foam cells, with the interface separating the plaque and blood flow regions being a free boundary. We establish finite branches of symmetry-breaking stationary solutions which bifurcate from the radially symmetric solution. Since plaques in reality are unlikely to be strictly radially symmetric, our result would be useful to explain the asymmetric shapes of plaques.
Assessing microsoft from a distance

Practices are deemed exclusionary; they are prohibited; competitive conditions improve. What does this sequence say about the true charac ter of the practices and the merits of the case? One possibility is that the back-end conditions confirm the accuracy of the front-end determina tion?and potentially the wisdom of launching the litigation. The argu ment is that if competition increases when practices stop, those practices must have been anticompetitive, and the legal challenge is vindicated. Another possibility, though, is that the practices were efficient. Their prohibition would rob the defendant of its cost advantage, and its mar ket share would predictably shrink. If competition is measured on the basis of market concentration, the remedy would indeed have increased competition, but the litigation will have reduced economic welfare and probably consumer welfare. The aftermath of the litigation will hardly vindicate the challenge.
Variational Autoencoder with Learned Latent Structure

The manifold hypothesis states that high-dimensional data can be modeled as lying on or near a low-dimensional, nonlinear manifold. Variational Autoencoders (VAEs) approximate this manifold by learning mappings from low-dimensional latent vectors to high-dimensional data while encouraging a global structure in the latent space through the use of a specified prior distribution. When this prior does not match the structure of the true data manifold, it can lead to a less accurate model of the data. To resolve this mismatch, we introduce the Variational Autoencoder with Learned Latent Structure (VAELLS) which incorporates a learnable manifold model into the latent space of a VAE. This enables us to learn the nonlinear manifold structure from the data and use that structure to define a prior in the latent space. The integration of a latent manifold model not only ensures that our prior is well-matched to the data, but also allows us to define generative transformation paths in the latent space and describe class manifolds by transformations stemming from examples of each class. We validate our model on examples with known latent structure and also demonstrate its capabilities on a real-world dataset.
Quantum photonics in triangular-cross-section nanodevices in silicon carbide

Silicon carbide is evolving as a prominent solid-state platform for the realization of quantum information processing hardware. Angle-etched nanodevices are emerging as a solution to photonic integration in bulk substrates where color centers are best defined. We model triangular cross-section waveguides and photonic crystal cavities using Finite-Difference Time-Domain and Finite-Difference Eigensolver approaches. We analyze optimal color center positioning within the modes of these devices and provide estimates on achievable Purcell enhancement in nanocavities with applications in quantum communications. Using open quantum system modeling, we explore emitter-cavity interactions of multiple non-identical color centers coupled to both a single cavity and a photonic crystal molecule in SiC. We observe polariton and subradiant state formation in the cavity-protected regime of cavity quantum electrodynamics applicable in quantum simulation.
Capital Market Financing and Firm Growth

Abstract This paper studies whether there is a connection between finance and growth at the firm level. It employs a new dataset of 150,165 equity and bond issuances around the world, matched with income and balance sheet data for 62,653 listed firms in 65 countries over 1990–2016. Three main patterns emerge from the analyses. First, firms that choose to issue in capital markets use the funds raised to grow by enhancing their productive capabilities, increasing their tangible and intangible capital and the number of employees. Growth accelerates as firms raise funds. Second, the faster growth is more pronounced among firms that are more likely to face tighter financing constraints, namely, small, young, and high-R&D firms. Third, capital market issuances are associated with faster growth among firms located in countries with more developed capital markets relative to banks. Capital markets are also comparatively effective at allowing financially constrained firms to raise capital.
S.-S. Chern's study of almost-complex structures on the six-sphere

In April 2003, Chern began a study of almost-complex structures on the six-sphere, with the idea of exploiting the special properties of its well-known almost-complex structure invariant under the ...
On Optimal Designs Using Topology Optimization for Flow Through Porous Media Applications

Topology optimization (TopOpt) is a mathematical-driven design procedure to realize optimal material architectures. This procedure is often used to automate the design of devices involving flow through porous media, such as micro-fluidic devices. TopOpt offers material layouts that control the flow of fluids through porous materials, providing desired functionalities. Many prior studies in this application area have used Darcy equations for primal analysis and the minimum power theorem (MPT) to drive the optimization problem. But both these choices (Darcy equations and MPT) are restrictive and not valid for general working conditions of modern devices. Being simple and linear, Darcy equations are often used to model flow of fluids through porous media. However, two inherent assumptions of the Darcy model are: the viscosity of a fluid is a constant, and inertial effects are negligible. There is irrefutable experimental evidence that viscosity of a fluid, especially organic liquids, depends on the pressure. Given the typical small pore-sizes, inertial effects are dominant in micro-fluidic devices. Next, MPT is not a general principle and is not valid for (nonlinear) models that relax the assumptions of the Darcy model. This paper aims to overcome the mentioned deficiencies by presenting a general strategy for using TopOpt. First, we will consider nonlinear models that take into account the pressure-dependent viscosity and inertial effects, and study the effect of these nonlinearities on the optimal material layouts under TopOpt. Second, we will explore the rate of mechanical dissipation, valid even for nonlinear models, as an alternative for the objective function. Third, we will present analytical solutions of optimal designs for canonical problems; these solutions not only possess research and pedagogical values, but also facilitate verification of computer implementations. We have considered a pressure-driven problem with axisymmetry and got optimal material layouts using topology optimization by maximizing the total rate of dissipation. The left figure shows unphysical finger-like design patterns when the primal analysis does not enforce explicitly the underlying radial symmetry. The right figure shows that one can avoid such numerical pathologies if the primal analysis invokes axisymmetry conditions.
Summing over geometries in string theory

We examine the question how string theory achieves a sum over bulk geometries with fixed asymptotic boundary conditions. We discuss this problem with the help of the tensionless string on $$ {\mathrm{\mathcal{M}}}_3\times {\mathrm{S}}^3\times {\mathbbm{T}}^4 $$ (with one unit of NS-NS flux) that was recently understood to be dual to the symmetric orbifold SymN ( $$ {\mathbbm{T}}^4 $$ ). We strengthen the analysis of [1] and show that the perturbative string partition function around a fixed bulk background already includes a sum over semi-classical geometries and large stringy corrections can be interpreted as various semi-classical geometries. We argue in particular that the string partition function on a Euclidean wormhole geometry factorizes completely into factors associated to the two boundaries of spacetime. Central to this is the remarkable property of the moduli space integral of string theory to localize on covering spaces of the conformal boundary of ℳ3. We also emphasize the fact that string perturbation theory computes the grand canonical partition function of the family of theories ⊕N SymN ( $$ {\mathbbm{T}}^4 $$ ). The boundary partition function is naturally expressed as a sum over winding worldsheets, each of which we interpret as a ‘stringy geometry’. We argue that the semi-classical bulk geometry can be understood as a condensate of such stringy geometries. We also briefly discuss the effect of ensemble averaging over the Narain moduli space of $$ {\mathbbm{T}}^4 $$ and of deforming away from the orbifold by the marginal deformation.
Preventing post-surgical cardiac adhesions with a catechol-functionalized oxime hydrogel.

Post-surgical cardiac adhesions represent a significant problem during routine cardiothoracic procedures. This fibrous tissue can impair heart function and inhibit surgical access in reoperation procedures. Here, we propose a hydrogel barrier composed of oxime crosslinked poly(ethylene glycol) (PEG) with the inclusion of a catechol (Cat) group to improve retention on the heart for pericardial adhesion prevention. This three component system is comprised of aldehyde (Ald), aminooxy (AO), and Cat functionalized PEG mixed to form the final gel (Ald-AO-Cat). Ald-AO-Cat has favorable mechanical properties, degradation kinetics, and minimal swelling, as well as superior tissue retention compared to an initial Ald-AO gel formulation. We show that the material is cytocompatible, resists cell adhesion, and led to a reduction in the severity of adhesions in an in vivo rat model. We further show feasibility in a pilot porcine study. The Ald-AO-Cat hydrogel barrier may therefore serve as a promising solution for preventing post-surgical cardiac adhesions. Postsurgical adhesions are a problem during routine cardiothoracic procedures. Here, the authors report on a catechol functionalised hydrogel as an anti-adhesion material with improved retention on the heart which is biocompatible and biodegradable with minimal swelling, demonstrating application in vivo.
Information theoretic limits of cardinality estimation: Fisher meets Shannon

Estimating the cardinality (number of distinct elements) of a large multiset is a classic problem in streaming and sketching, dating back to Flajolet and Martin’s classic Probabilistic Counting (PCSA) algorithm from 1983. In this paper we study the intrinsic tradeoff between the space complexity of the sketch and its estimation error in the random oracle model. We define a new measure of efficiency for cardinality estimators called the Fisher-Shannon (Fish) number H/I. It captures the tension between the limiting Shannon entropy (H) of the sketch and its normalized Fisher information (I), which characterizes the variance of a statistically efficient, asymptotically unbiased estimator. Our results are as follows. (i) We prove that all base-q variants of Flajolet and Martin’s PCSA sketch have Fish-number H0/I0 ≈ 1.98016 and that every base-q variant of (Hyper)LogLog has Fish-number worse than H0/I0, but that they tend to H0/I0 in the limit as q→ ∞. Here H0,I0 are precisely defined constants. (ii) We describe a sketch called Fishmonger that is based on a smoothed, entropy-compressed variant of PCSA with a different estimator function. It is proved that with high probability, Fishmonger processes a multiset of [U] such that at all times, its space is O(log2logU) + (1+o(1))(H0/I0)b ≈ 1.98b bits and its standard error is 1/√b. (iii) We give circumstantial evidence that H0/I0 is the optimum Fish-number of mergeable sketches for Cardinality Estimation. We define a class of linearizable sketches and prove that no member of this class can beat H0/I0. The popular mergeable sketches are, in fact, also linearizable.
SMG: A Shuffling Gradient-Based Method with Momentum

We combine two advanced ideas widely used in optimization for machine learning: shuffling strategy and momentum technique to develop a novel shuffling gradient-based method with momentum, coined Shuffling Momentum Gradient (SMG), for non-convex finite-sum optimization problems. While our method is inspired by momentum techniques, its update is fundamentally different from existing momentum-based methods. We establish state-of-the-art convergence rates of SMG for any shuffling strategy using either constant or diminishing learning rate under standard assumptions (i.e.$L$-smoothness and bounded variance). When the shuffling strategy is fixed, we develop another new algorithm that is similar to existing momentum methods, and prove the same convergence rates for this algorithm under the $L$-smoothness and bounded gradient assumptions. We demonstrate our algorithms via numerical simulations on standard datasets and compare them with existing shuffling methods. Our tests have shown encouraging performance of the new algorithms.
Time-Stamped Language Model: Teaching Language Models to Understand The Flow of Events

Tracking entities throughout a procedure described in a text is challenging due to the dynamic nature of the world described in the process. Firstly, we propose to formulate this task as a question answering problem. This enables us to use pre-trained transformer-based language models on other QA benchmarks by adapting those to the procedural text understanding. Secondly, since the transformer-based language models cannot encode the flow of events by themselves, we propose a Time-Stamped Language Model (TSLM) to encode event information in LMs architecture by introducing the timestamp encoding. Our model evaluated on the Propara dataset shows improvements on the published state-of-the-art results with a 3.1% increase in F1 score. Moreover, our model yields better results on the location prediction task on the NPN-Cooking dataset. This result indicates that our approach is effective for procedural text understanding in general.
Design and fabrication requirements for low-noise supersonic/hypersonic wind tunnels

A schematic diagram of the new proposed Supersonic Low Disturbance Tunnel (SLDT) is shown. Large width two dimensional rapid expansion nozzles guarantee wide quiet test cores that are well suited for testing models at large angle of attack and for swept wings. Hence, this type of nozzle will be operated first in the new proposed large scale SLDT. Test results indicate that the surface finish of pilot nozzles is critical. The local roughness Reynolds number criteria R sub k is approx. = 10 will be used to specify allowable roughness on new pilot nozzles and the new proposed tunnel. Experimental data and calculations for M = 3.0, 3.5, and 5.0 nozzles give N-factors from 6 to 10 for transition caused by Goertler vortices. The use of N is approx. = 9.0 for the Goertler instability predicts quiet test cores in the new M = 3.5 and M = 6.0 axisymmetric long pilot nozzles that are 3 to 4 times longer than observed in the test nozzles to date. The new nozzles utilize a region of radial flow which moves the inflection point far downstream and delays the onset and amplification of the Goertler vortices.
Single-cavity bi-color laser enabled by optical anti-parity-time symmetry

The exploration of quantum inspired symmetries in optical systems has spawned promising physics and provided fertile ground for developing devices exhibiting exotic functionalities. Founded on the anti-parity–time (APT) symmetry that is enabled by both spatial and temporal interplay between gain and loss, we demonstrate theoretically and numerically bi-color lasing in a single micro-ring resonator with spatiotemporal modulation along its azimuthal direction. In contrast to conventional multi-mode lasers that have mixed-frequency output, our laser exhibits stable, demultiplexed, tunable bi-color emission at different output ports. Our APT-symmetry-based laser may point out a new route for realizing compact on-chip coherent multi-color light sources.
Pivoting in the pandemic: a qualitative study of child and adolescent psychiatrists in the times of COVID-19

We examined the personal and professional impacts of the COVID-19 pandemic on the development, practice, and shifting values of child and adolescent psychiatrists (CAP), in order to inform how the field may move forward post-pandemic. We conducted individual semi-structured interviews of child and adolescent psychiatrists (n = 24) practicing in the United States. Participants were selected as a diverse purposive sample of active members of the American Academy of Child and Adolescent Psychiatry (AACAP). We analyzed anonymized transcripts through iterative coding using thematic analysis aided by NVivo software. We identified three main thematic domains within participants’ response to the pandemic, which have engendered a reevaluation of and a recommitment to the aims of each clinician and the field of CAP more broadly. These domains, paired with representative questions, include: (1) Unsettling, or “who have we been?” (identifying discontents such as daily inefficiencies and intraprofessional loss of trust); (2) Adaptation, or “who are we now?” (exploring affordances and limitations of virtual work, and the evolution of personal and professional identity); and (3) Reimagination, or “who will we become?” (renewing a commitment to psychiatry as advocacy). Even as we identified a collective agreement toward the need for implementing change, just what needs to change, and how that change will be realized, remain contested. These three thematic domains, augmented by a national confrontation with race and equity, have engendered a field-wide reckoning with known inequities. They have reinvigorated collective responses and calls to action. The divergent mindsets to change and leadership have provided an aperture for what values and practices the field might instill in its next generation of practitioners.
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting.

Probabilistic forecasting of high dimensional multivariate time series is a notoriously challenging task, both in terms of computational burden and distribution modeling. Most previous work either makes simple distribution assumptions or abandons modeling cross-series correlations. A promising line of work exploits scalable matrix factorization for latent-space forecasting, but is limited to linear embeddings, unable to model distributions, and not trainable end-to-end when using deep learning forecasting. We introduce a novel temporal latent auto-encoder method which enables nonlinear factorization of multivariate time series, learned end-to-end with a temporal deep learning latent space forecast model. By imposing a probabilistic latent space model, complex distributions of the input series are modeled via the decoder. Extensive experiments demonstrate that our model achieves state-of-the-art performance on many popular multivariate datasets, with gains sometimes as high as 50% for several standard metrics.
Photovoltaics for high capacity space power systems

Abstract The anticipated energy requirements of future space missions will grow by factors approaching 100 or more, particularly as we establish a permanent manned presence in space. The advances that can be expected in solar array performance and lifetime, when coupled with advanced, high energy density storage batteries and/or fuel cells, will continue to make photovoltaic energy conversion a viable power generating option for the large systems of the future. The specific technologies required to satisfy any particular set of power requirements will vary from mission to mission. Nonetheless, in almost all cases the technology push will be toward lighter weight and higher efficiency, whether of solar arrays or storage devices. This paper will describe the content and direction of the current NASA program in space photovoltaic technology. The paper will also discuss projected system level capabilities of photovoltaic power systems in the context of some of the new mission opportunities under study by NASA, such as a manned lunar base, and a manned visit to Mars.
Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently

Cascade models are central to understanding, predicting, and controlling epidemic spreading and information propagation. Related optimization, including influence maximization, model parameter inference, or the development of vaccination strategies, relies heavily on sampling from a model. This is either inefficient or inaccurate. As alternative, we present an efficient message passing algorithm that computes the probability distribution of the cascade size for the Independent Cascade Model on weighted directed networks and generalizations. Our approach is exact on trees but can be applied to any network topology. It approximates locally tree-like networks well, scales to large networks, and can lead to surprisingly good performance on more dense networks, as we also exemplify on real world data.
Large-Scale Discrete Fourier Transform on TPUs

In this work, we present two parallel algorithms for the large-scale discrete Fourier transform (DFT) on Tensor Processing Unit (TPU) clusters. The two parallel algorithms are associated with two DFT formulations: one formulation, denoted as KDFT, is based on the Kronecker product; the other is based on the famous Cooley-Tukey algorithm and phase adjustment, denoted as FFT. Both KDFT and FFT formulations take full advantage of TPU’s strength in matrix multiplications. The KDFT formulation allows direct use of nonuniform inputs without additional step. In the two parallel algorithms, the same strategy of data decomposition is applied to the input data. Through the data decomposition, the dense matrix multiplications in KDFT and FFT are kept local within TPU cores, which can be performed completely in parallel. The communication among TPU cores is achieved through the one-shuffle scheme in both parallel algorithms, with which sending and receiving data takes place simultaneously between two neighboring cores and along the same direction on the interconnect network. The one-shuffle scheme is designed for the interconnect topology of TPU clusters, minimizing the time required by the communication among TPU cores. Both KDFT and FFT are implemented in TensorFlow. The three-dimensional complex DFT is performed on an example of dimension $8192 \times 8192 \times 8192$ with a full TPU Pod: the run time of KDFT is 12.66 seconds and that of FFT is 8.3 seconds. Scaling analysis is provided to demonstrate the high parallel efficiency of the two DFT implementations on TPUs.
Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds

We study an unsupervised domain adaptation problem for the semantic labeling of 3D point clouds, with a particular focus on domain discrepancies induced by different LiDAR sensors. Based on the observation that sparse 3D point clouds are sampled from 3D surfaces, we take a Complete and Label approach to recover the underlying surfaces before passing them to a segmentation network. Specifically, we design a Sparse Voxel Completion Network (SVCN) to complete the 3D surfaces of a sparse point cloud. Unlike semantic labels, to obtain training pairs for SVCN requires no manual labeling. We also introduce local adversarial learning to model the surface prior. The recovered 3D surfaces serve as a canonical domain, from which semantic labels can transfer across different LiDAR sensors. Experiments and ablation studies with our new benchmark for cross-domain semantic labeling of LiDAR data show that the proposed approach provides 6.3-37.6% better performance than previous domain adaptation methods.
Aggregative Efficiency of Bayesian Learning in Networks

In social-learning settings where individuals receive private signals and observe network neighbors' actions, the network structure often obstructs information aggregation. We consider sequential social learning with rational agents and Gaussian signals and ask how the efficiency of signal aggregation changes with the network. Rational actions in our model are a log-linear function of observations and admit a signal-counting interpretation of accuracy. This leads to a detailed ranking of networks for social learning based on their aggregative efficiency index. Networks where agents observe multiple neighbors but not their common predecessors confound information, and we show confounding can make learning very inefficient. In a class of networks where agents move in generations and observe the previous generation, aggregative efficiency is a simple function of network parameters: increasing in observations and decreasing in confounding. Generations after the first contribute very little additional information due to confounding, even when generations are arbitrarily large. The full paper can be found at https://kevinhe.net/papers/aggregative.pdf
A note on boundary conditions in Euclidean gravity

We review what is known about boundary conditions in General Relativity on a spacetime of Euclidean signature. The obvious Dirichlet boundary condition, in which one specifies the boundary geometry...
Learning to Denoise Historical Music

We propose an audio-to-audio neural network model that learns to denoise old music recordings. Our model internally converts its input into a time-frequency representation by means of a short-time Fourier transform (STFT), and processes the resulting complex spectrogram using a convolutional neural network. The network is trained with both reconstruction and adversarial objectives on a synthetic noisy music dataset, which is created by mixing clean music with real noise samples extracted from quiet segments of old recordings. We evaluate our method quantitatively on held-out test examples of the synthetic dataset, and qualitatively by human rating on samples of actual historical recordings. Our results show that the proposed method is effective in removing noise, while preserving the quality and details of the original music.
Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare.

In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks. Unfortunately, beneficiaries may gradually disengage from such programs, which is detrimental to their health. A concrete example of gradual disengagement has been observed by an organization that carries out a free automated call-based program for spreading preventive care information among pregnant women. Many women stop picking up calls after being enrolled for a few months. To avoid such disengagements, it is important to provide timely interventions. Such interventions are often expensive and can be provided to only a small fraction of the beneficiaries. We model this scenario as a restless multi-armed bandit (RMAB) problem, where each beneficiary is assumed to transition from one state to another depending on the intervention. Moreover, since the transition probabilities are unknown a priori, we propose a Whittle index based Q-Learning mechanism and show that it converges to the optimal solution. Our method improves over existing learning-based methods for RMABs on multiple benchmarks from literature and also on the maternal healthcare dataset.
State-space aerodynamic model reveals high force control authority and predictability in flapping flight

Flying animals resort to fast, large-degree-of-freedom motion of flapping wings, a key feature that distinguishes them from rotary or fixed-winged robotic fliers with limited motion of aerodynamic ...
Stochastic homogenization of a class of quasiconvex viscous Hamilton-Jacobi equations in one space dimension

Abstract We prove homogenization for a class of viscous Hamilton-Jacobi equations in the stationary & ergodic setting in one space dimension. Our assumptions include most notably the following: the Hamiltonian is of the form G ( p ) + β V ( x , ω ) , the function G is coercive and strictly quasiconvex, min ⁡ G = 0 , β > 0 , the random potential V takes values in [ 0 , 1 ] with full support and it satisfies a hill condition that involves the diffusion coefficient. Our approach is based on showing that, for every direction outside of a bounded interval ( θ 1 ( β ) , θ 2 ( β ) ) , there is a unique sublinear corrector with certain properties. We obtain a formula for the effective Hamiltonian and deduce that it is coercive, identically equal to β on ( θ 1 ( β ) , θ 2 ( β ) ) , and strictly monotone elsewhere.
Magnetoexcitons in transition metal dichalcogenides monolayers, bilayers, and van der Waals heterostructures

We study direct and indirect magnetoexcitons in Rydberg states in monolayers and heterostructures of transition metal dichalcogenices (TMDCs) in an external magnetic field, applied perpendicular to the monolayer or heterostructures in the framework of a Mott-Wannier model of excitons. We calculate binding energies of magnetoexcitons for the Rydberg states $1s, 2s, 3s$, and $4s$ by numerical integration of the Schr\"odinger equation using the Rytova-Keldysh potential for direct magnetoexcitons and both the Rytova-Keldysh and Coulomb potentials for indirect magnetoexcitons. The latter allows understanding the role of screening in TMDCs heterostructures. We report the magnetic field energy contribution to the binding energies and diamagnetic coefficients (DMCs) for direct and indirect magnetoexcitons. The tunability of the energy contribution of direct and indirect magnetoexcitons by the magnetic field is demonstrated. It is shown that binding energies and DMCs of indirect magnetoexcitons can be manipulated by the number of hBN layers. Therefore, our study raises the possibility of controlling the binding energies of direct and indirect magnetoexcitons in TMDC monolayers, bilayers, and van der Waals heterostructures using magnetic field and opens an additional degree of freedom to tailor the binding energies and DMCs for heterostructures by varying the number of hBN sheets between TMDC layers. The calculations of the binding energies and DMCs of indirect magnetoexcitons in TMDC heterostructures can be compared with the experimental results when they are available.
Reinforcement Learning To Adapt Speech Enhancement to Instantaneous Input Signal Quality

Today, the optimal performance of existing noise-suppression algorithms, both data-driven and those based on classic statistical methods, is range bound to specific levels of instantaneous input signal-to-noise ratios. In this paper, we present a new approach to improve the adaptivity of such algorithms enabling them to perform robustly across a wide range of input signal and noise types. Our methodology is based on the dynamic control of algorithmic parameters via reinforcement learning. Specifically, we model the noise-suppression module as a black box, requiring no knowledge of the algorithmic mechanics except a simple feedback from the output. We utilize this feedback as the reward signal for a reinforcement-learning agent that learns a policy to adapt the algorithmic parameters for every incoming audio frame (16 ms of data). Our preliminary results show that such a control mechanism can substantially increase the overall performance of the underlying noise-suppression algorithm; 42% and 16% improvements in output SNR and MSE, respectively, when compared to no adaptivity.
Necessary and Sufficient Girth Conditions for Tanner Graphs of Quasi-Cyclic LDPC Codes

This paper revisits the connection between the girth of a protograph-based LDPC code given by a parity-check matrix and the properties of powers of the product between the matrix and its transpose in order to obtain the necessary and sufficient conditions for a code to have given girth between 6 and 12, and to show how these conditions can be incorporated into simple algorithms to construct codes of that girth. To this end, we highlight the role that certain submatrices that appear in these products have in the construction of codes of desired girth. In particular, we show that imposing girth conditions on a parity-check matrix is equivalent to imposing conditions on a square submatrix obtained from it and we show how this equivalence is particularly strong for a protograph based parity-check matrix of variable node degree 2, where the cycles in its Tanner graph correspond one-to-one to the cycles in the Tanner graph of a square submatrix obtained by adding the permutation matrices (or products of these) in the composition of the parity-check matrix. We end the paper with exemplary constructions of codes with various girths and computer simulations. Although, we mostly assume the case of fully connected protographs of variable node degree 2 and 3, the results can be used for any parity-check matrix/protograph-based Tanner graph.
Inferring CAD Modeling Sequences Using Zone Graphs

In computer-aided design (CAD), the ability to "reverse engineer" the modeling steps used to create 3D shapes is a long-sought-after goal. This process can be decomposed into two sub-problems: converting an input mesh or point cloud into a boundary representation (or B-rep), and then inferring modeling operations which construct this B-rep. In this paper, we present a new system for solving the second sub-problem. Central to our approach is a new geometric representation: the zone graph. Zones are the set of solid regions formed by extending all B-Rep faces and partitioning space with them; a zone graph has these zones as its nodes, with edges denoting geometric adjacencies between them. Zone graphs allow us to tractably work with industry-standard CAD operations, unlike prior work using CSG with parametric primitives. We focus on CAD programs consisting of sketch + extrude + Boolean operations, which are common in CAD practice. We phrase our problem as search in the space of such extrusions permitted by the zone graph, and we train a graph neural network to score potential extrusions in order to accelerate the search. We show that our approach outperforms an existing CSG inference baseline in terms of geometric reconstruction accuracy and reconstruction time, while also creating more plausible modeling sequences.
Guaranteeing a physically realizable battery dispatch without charge-discharge complementarity constraints

The non-convex complementarity constraints present a fundamental computational challenge in energy constrained optimization problems. In this work, we present a new, linear, and robust battery optimization formulation that sidesteps the need for battery complementarity constraints and integers and prove analytically that the formulation guarantees that all energy constraints are satisfied which ensures that the optimized battery dispatch is physically realizable. In addition, we bound the worst-case model mismatch and discuss conservativeness. Simulation results further illustrate the effectiveness of this approach.
Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network

Research in deep learning models to forecast traffic intensities has gained great attention in recent years due to their capability to capture the complex spatio-temporal relationships within the traffic data. However, most state-of-the-art approaches have designed spatial-only (e.g. Graph Neural Networks) and temporal-only (e.g. Recurrent Neural Networks) modules to separately extract spatial and temporal features. However, we argue that it is less effective to extract the complex spatiotemporal relationship with such factorized modules. Besides, most existing works predict the traffic intensity of a particular time interval only based on the traffic data of the previous one hour of that day. And thereby ignores the repetitive daily/weekly pattern that may exist in the last hour of data. Therefore, we propose a Unified Spatio-Temporal Graph Convolution Network (USTGCN) for traffic forecasting that performs both spatial and temporal aggregation through direct information propagation across different timestamp nodes with the help of spectral graph convolution on a spatio-temporal graph. Furthermore, it captures historical daily patterns in previous days and current-day patterns in current-day traffic data. Finally, we validate our work's effectiveness through experimental analysis11Code is available at github.com/AmitRoy7781/USTGCN, which shows that our model USTGCN can outperform state-of-the-art performances in three popular benchmark datasets from the Performance Measurement System (PeMS). Moreover, the training time is reduced significantly with our proposed USTGCN model.