cssi/SciSciGPT-SciSciNet huggingface dataset:
Unlocking clients: The importance of relationships in the financial advisory industry

Abstract We investigate the importance of client relationships in the financial advisory industry. We exploit firm-level variation in adoption of the Broker Protocol, which enabled clients to follow their advisers to member firms without fear of litigation. We show that advisers’ ability to maintain client relationships is a significant predictor of their employment decisions; that about 40% of client assets follow advisers when they move; and that once clients are “unlocked,” firms become less willing to fire advisers for misconduct. Firms that unlock their clients subsequently experience higher levels of misconduct and increase their fees, calling into question whether clients are better off.
Consumer Protection for Financial Inclusion in Low- and Middle-Income Countries: Bridging Regulator and Academic Perspectives

Markets for consumer financial services are growing rapidly in low- and middle-income countries and are being transformed by digital technologies and platforms. With growth and change come concerns...
Super B factories

Heavy-flavor physics, in particular B and r physics results from the B factories, currently provides strong constraints on models of physics beyond the Standard Model. A new generation of colliders, Super B Factories, with 50 to 100 times the luminosity of existing colliders, can, in a dialog with LHC and ILC, provide unique clarification of new physics phenomena seen at those machines.
Asymptotic windings of the block determinants of a unitary Brownian motion and related diffusions

We study several matrix diffusion processes constructed from a unitary Brownian motion. In particular, we use the Stiefel fibration to lift the Brownian motion of the complex Grassmannian to the complex Stiefel manifold and deduce a skew-product decomposition of the Stiefel Brownian motion. As an application, we prove asymptotic laws for the determinants of the block entries of the unitary Brownian motion.
Hadamard-Free Circuits Expose the Structure of the Clifford Group

The Clifford group plays a central role in quantum randomized benchmarking, quantum tomography, and error correction protocols. Here we study the structural properties of this group. We show that any Clifford operator can be uniquely written in the canonical form $F_{1}HSF_{2}$ , where $H$ is a layer of Hadamard gates, $S$ is a permutation of qubits, and $F_{i}$ are parameterized Hadamard-free circuits chosen from suitable subgroups of the Clifford group. Our canonical form provides a one-to-one correspondence between Clifford operators and layered quantum circuits. We report a polynomial-time algorithm for computing the canonical form. We employ this canonical form to generate a random uniformly distributed $n$ -qubit Clifford operator in runtime $O(n^{2})$ . The number of random bits consumed by the algorithm matches the information-theoretic lower bound. A surprising connection is highlighted between random uniform Clifford operators and the Mallows distribution on the symmetric group. The variants of the canonical form, one with a short Hadamard-free part and one allowing a circuit depth $9n$ implementation of arbitrary Clifford unitaries in the Linear Nearest Neighbor architecture are also discussed. Finally, we study computational quantum advantage where a classical reversible linear circuit can be implemented more efficiently using Clifford gates, and show an explicit example where such an advantage takes place.
Entropy rigidity for finite volume strictly convex projective manifolds

We prove entropy rigidity for finite volume strictly convex projective manifolds in dimensions $$\ge 3$$ , generalizing the work of [1] to the finite volume setting. The rigidity theorem uses the techniques of Besson, Courtois, and Gallot’s entropy rigidity theorem. It implies uniform lower bounds on the volume of any finite volume strictly convex projective manifold in dimensions $$\ge 3$$ .
The Impact of Different Parameterizations on the Interpretation of CP Violation in Neutrino Oscillations

CP violation in the lepton mass matrix will be probed with good precision in upcoming experiments. The amount of CP violation present in oscillations can be quantified in numerous ways and is typically parameterized by the complex phase δPDG in the standard PDG definition of the lepton mixing matrix. There are additional parameterizations of the lepton mixing matrix as well. Through various examples, we explore how, given the current data, different parameterizations can lead to different conclusions when working with parameterization dependent variables, such as δ. We demonstrate how the smallness of |Ue3| governs the scale of these results. We then demonstrate how δ can be misleading and argue that the Jarlskog is the cleanest means of presenting the amount of CP violation in the lepton sector. We also confirm that, among the different parameterizations considered, the standard PDG parameterization has a number of convenient features.
Fixed Parameter Approximation Scheme for Min-Max k-Cut

We consider the graph k-partitioning problem under the min-max objective, termed as \(\textsc {Minmax}\;k\text {-}\textsc {cut}\). The input here is a graph \(G=(V,E)\) with non-negative edge weights \(w:E\rightarrow \mathbb {R}_+\) and an integer \(k\ge 2\) and the goal is to partition the vertices into k non-empty parts \(V_1, \ldots , V_k\) so as to minimize \(\max _{i=1}^k w(\delta (V_i))\). Although minimizing the sum objective \(\sum _{i=1}^k w(\delta (V_i))\), termed as \(\textsc {Minsum}\;k\text {-}\textsc {cut}\), has been studied extensively in the literature, very little is known about minimizing the max objective. We initiate the study of \(\textsc {Minmax}\;k\text {-}\textsc {cut}\) by showing that it is NP-hard and W[1]-hard when parameterized by k, and design a parameterized approximation scheme when parameterized by k. The main ingredient of our parameterized approximation scheme is an exact algorithm for \(\textsc {Minmax}\;k\text {-}\textsc {cut}\) that runs in time \((\lambda k)^{O(k^2)}n^{O(1)}\), where \(\lambda \) is the value of the optimum and n is the number of vertices. Our algorithmic technique builds on the technique of Lokshtanov, Saurabh, and Surianarayanan (FOCS, 2020) who showed a similar result for \(\textsc {Minsum}\;k\text {-}\textsc {cut}\). Our algorithmic techniques are more general and can be used to obtain parameterized approximation schemes for minimizing \(\ell _p\)-norm measures of k-partitioning for every \(p\ge 1\).
Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression

Machine learning offers an intriguing alternative to first-principle analysis for discovering new physics from experimental data. However, to date, purely data-driven methods have only proven successful in uncovering physical laws describing simple, low-dimensional systems with low levels of noise. Here we demonstrate that combining a data-driven methodology with some general physical principles enables discovery of a quantitatively accurate model of a non-equilibrium spatially extended system from high-dimensional data that is both noisy and incomplete. We illustrate this using an experimental weakly turbulent fluid flow where only the velocity field is accessible. We also show that this hybrid approach allows reconstruction of the inaccessible variables - the pressure and forcing field driving the flow.
The acceleration of charged particles and formation of power-law energy spectra in nonrelativistic magnetic reconnection

Magnetic reconnection is a primary driver of particle acceleration processes in space and astrophysical plasmas. Understanding how particles are accelerated and the resulting particle energy spectra are among the central topics in reconnection studies. We review recent advances in addressing this problem in nonrelativistic reconnection that is relevant to space and solar plasmas and beyond. We focus on particle acceleration mechanisms, particle transport due to 3D reconnection physics, and their roles in forming power-law particle energy spectra. We conclude by pointing out the challenges in studying particle acceleration and transport in a large-scale reconnection layer and the relevant issues to be addressed in the future.
The Barron Space and the Flow-Induced Function Spaces for Neural Network Models

One of the key issues in the analysis of machine learning models is to identify the appropriate function space and norm for the model. This is the set of functions endowed with a quantity which can control the approximation and estimation errors by a particular machine learning model. In this paper, we address this issue for two representative neural network models: the two-layer networks and the residual neural networks. We define the Barron space and show that it is the right space for two-layer neural network models in the sense that optimal direct and inverse approximation theorems hold for functions in the Barron space. For residual neural network models, we construct the so-called flow-induced function space and prove direct and inverse approximation theorems for this space. In addition, we show that the Rademacher complexity for bounded sets under these norms has the optimal upper bounds.
RNase L promotes the formation of unique ribonucleoprotein granules distinct from stress granules

Stress granules (SGs) are ribonucleoprotein (RNP) assemblies that form in eukaryotic cells as a result of limited translation in response to stress. SGs form during viral infection and are thought to promote the antiviral response because many viruses encode inhibitors of SG assembly. However, the antiviral endoribonuclease RNase L also alters SG formation, whereby only small punctate SG-like bodies that we term RNase L–dependent bodies (RLBs) form during RNase L activation. How RLBs relate to SGs and their mode of biogenesis is unknown. Herein, using immunofluorescence, live-cell imaging, and MS-based analyses, we demonstrate that RLBs represent a unique RNP granule with a protein and RNA composition distinct from that of SGs in response to dsRNA lipofection in human cells. We found that RLBs are also generated independently of SGs and the canonical dsRNA-induced SG biogenesis pathway, because RLBs did not require protein kinase R, phosphorylation of eukaryotic translation initiation factor 2 subunit 1 (eIF2α), the SG assembly G3BP paralogs, or release of mRNAs from ribosomes via translation elongation. Unlike the transient interactions between SGs and P-bodies, RLBs and P-bodies extensively and stably interacted. However, despite both RLBs and P-bodies exhibiting liquid-like properties, they remained distinct condensates. Taken together, these observations reveal that RNase L promotes the formation of a unique RNP complex that may have roles during the RNase L–mediated antiviral response.
The legacy of representation in medieval Europe for incomes and institutions today

Why can some governments credibly commit to the rule of law and protection of property rights while others cannot? A potential answer involves deep historical traditions of institutions that constrain rulers. We explore whether experiences with representative assemblies in medieval/early modern Europe have left their mark on incomes and institutions today. We employ Stasavage’s (2010) data on representative assembly activity in 30 medieval/early modern European polities and the Putterman and Weil (2010) data on descendancy shares from circa 1500 populations to construct country-level measures of historical assembly experience. In a cross-country analysis, we find that assembly experience is positively and significantly correlated with current incomes, a measure of the rule of law and property rights, and the Polity IV index that emphasizes executive constraint. Once the latter two variables are controlled for, the estimated effect of assembly experience on current incomes is insignificant.
Contributions of implicit and explicit memories to sensorimotor adaptation of movement extent during goal-directed reaching

We examined how implicit and explicit memories contribute to sensorimotor adaptation of movement extent during goal-directed reaching. Twenty subjects grasped the handle of a horizontal planar robot that rendered spring-like resistance to movement. Subjects made rapid "out-and-back" reaches to capture a remembered visual target at the point of maximal reach extent. The robot's resistance changed unpredictably between reaches, inducing target capture errors that subjects attempted to correct from one trial to the next. Each subject performed over 400 goal-directed reaching trials. Some trials were performed without concurrent visual cursor feedback of hand motion. Some trials required self-assessment of performance between trials, whereby subjects reported peak reach extent on the most recent trial. This was done by either moving a cursor on a horizontal display (visual self-assessment), or by moving the robot's handle back to the recalled location (proprioceptive self-assessment). Control condition trials performed either without or with concurrent visual cursor feedback of hand motion did not require self-assessments. We used step-wise linear regression analyses to quantify the extent to which prior reach errors and explicit memories of reach extent contribute to subsequent reach performance. Consistent with prior reports, providing concurrent visual feedback of hand motion increased reach accuracy and reduced the impact of past performance errors on future performance, relative to the corresponding no-vision control condition. By contrast, we found no impact of interposed self-assessment on subsequent reach performance or on how prior target capture errors influence subsequent reach performance. Self-assessments were biased toward the remembered target location and they spanned a compressed range of values relative to actual reach extents, demonstrating that declarative memories of reach performance systematically differed from actual performances. We found that multilinear regression could best account for observed data variability when the regression model included only implicit memories of prior reach performance; including explicit memories (self-assessments) in the model did not improve its predictive accuracy. We conclude therefore that explicit memories of prior reach performance do not contribute to implicit sensorimotor adaptation of movement extent during goal-directed reaching under conditions of environmental uncertainty.
Life cycle of streaks in the buffer layer of wall-bounded turbulence

Streaks are considered to be of major importance in near-wall turbulence for their role in the regeneration of turbulent energy. The life cycle of streaks in the buffer layer of wall-bounded turbulence is identified by tracking the time evolution of individual streaks. The results show that streaks are born in the buffer layer, coalescing with each other to create larger streaks that are still attached to the wall. Once the streak becomes large enough, the tall-attached streak eventually splits into wall-attached and wall-detached components, disintegrating further until they dissipate into the turbulent background.
Upper Bounds on Communication in Terms of Approximate Rank.

We show that any Boolean function with approximate rank r can be computed by bounded error quantum protocols without prior entanglement of complexity \(O( \sqrt{r} \log r)\). In addition, we show that any Boolean function with approximate rank r and discrepancy \(\delta \) can be computed by deterministic protocols of complexity O(r), and private coin bounded error randomized protocols of complexity \(O((\frac{1}{\delta })^2 + \log r)\). Our deterministic upper bound in terms of approximate rank is tight up to constant factors, and the dependence on discrepancy in our randomized upper bound is tight up to taking square-roots. Our results can be used to obtain lower bounds on approximate rank. We also obtain a strengthening of Newman’s theorem with respect to approximate rank.
Borrowing from supplemental sources to estimate causal effects from a primary data source

The increasing multiplicity of data sources offers exciting possibilities in estimating the effects of a treatment, intervention, or exposure, particularly if observational and experimental sources could be used simultaneously. Borrowing between sources can potentially result in more efficient estimators, but it must be done in a principled manner to mitigate increased bias and Type I error. Furthermore, when the effect of treatment is confounded, as in observational sources or in clinical trials with noncompliance, causal effect estimators are needed to simultaneously adjust for confounding and to estimate effects across data sources. We consider the problem of estimating causal effects from a primary source and borrowing from any number of supplemental sources. We propose using regression-based estimators that borrow based on assuming exchangeability of the regression coefficients and parameters between data sources. Borrowing is accomplished with multisource exchangeability models and Bayesian model averaging. We show via simulation that a Bayesian linear model and Bayesian additive regression trees both have desirable properties and borrow under appropriate circumstances. We apply the estimators to recently completed trials of very low nicotine content cigarettes investigating their impact on smoking behavior.
Design, Characterization, and Control of a Size Adaptable In-pipe Robot for Water Distribution Systems

Leak detection and water quality monitoring are requirements and challenging tasks in Water Distribution Systems (WDS). In-line robots are designed for this aim. In our previous work, we designed an in-pipe robot [1]. In this research, we present the design of the central processor, characterize and control the robot based on the condition of operation in highly pressurized environment of pipelines with the presence of high-speed flow. To this aim, an extreme operation condition is simulated with computational fluid dynamics (CFD) and the spring mechanism is characterized to ensure sufficient stabilizing force during operation based on the extreme operating condition. Also, an end-to-end method is suggested for power considerations for our robot that calculates minimum battery capacity and operation duration in the extreme operating condition. Finally, we design a novel LQR-PID based controller based on the system’s auxiliary matrices that retains the robot’s stability inside pipeline against disturbances and uncertainties during operation. The ADAMS-MATLAB co-simulation of the robot-controller shows rotational velocity with -4°/sec and +3°/sec margin around x, y, and z axes while the system tracks different desired velocities in pipelines (i.e. 0.12m/s, 0.17m/s, and 0.35m/s). Also, experimental results for four iterations in a 14-inch diameter PVC pipe show that the controller brings initial values of stabilizing states to zero and oscillate around it with a margin of ±2° and the system tracks desired velocities of 0.1m/s, 0.2m/s, 0.3m/s, and 0.35m/s in which makes the robot dexterous in uncertain and highly disturbed environment of pipelines during operation.
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models

In this paper, we detail the relationship between convolutions and self-attention in natural language tasks. We show that relative position embeddings in self-attention layers are equivalent to recently-proposed dynamic lightweight convolutions, and we consider multiple new ways of integrating convolutions into Transformer self-attention. Specifically, we propose composite attention, which unites previous relative position encoding methods under a convolutional framework. We conduct experiments by training BERT with composite attention, finding that convolutions consistently improve performance on multiple downstream tasks, replacing absolute position embeddings. To inform future work, we present results comparing lightweight convolutions, dynamic convolutions, and depthwise-separable convolutions in language model pre-training, considering multiple injection points for convolutions in self-attention layers.
Cell Adhesions Link Subcellular Actomyosin Dynamics to Tissue Scale Force Production During Vertebrate Convergent Extension

Axis extension is a fundamental biological process that shapes multicellular organisms. The design of an animal’s body plan is encoded in the genome and execution of this program is a multiscale mechanical progression involving the coordinated movement of proteins, cells, and whole tissues. Thus, a key challenge to understanding axis extension is connecting events that occur across these various length scales. Here, we use approaches from proteomics, cell biology, and tissue biomechanics to describe how a poorly characterized cell adhesion effector, the Armadillo Repeat protein deleted in Velo-Cardio-Facial syndrome (Arvcf) catenin, controls vertebrate head-to-tail axis extension. We find that Arvcf catenin is required for axis extension within the intact organism but is not required for extension of isolated tissues. We then show that the organism scale phenotype is caused by a modest defect in force production at the tissue scale that becomes apparent when the tissue is challenged by external resistance. Finally, we show that the tissue scale force defect results from dampening of the pulsatile recruitment of cell adhesion and cytoskeletal proteins to cell membranes. These results not only provide a comprehensive understanding of Arvcf function during an essential biological process, but also provide insight into how a modest cellular scale defect in cell adhesion results in an organism scale failure of development.
Introducing “Focused Firms”: Implications from REIT Prime Operating Revenue

We examine the relationship between a firm’s main business focus and its risk and performance, using the unique settings of U.S. equity real estate investment trusts (REITs). In this paper, a REIT’s prime operating revenue ratio (POR) is measured as the ratio of rental revenue to total revenue. The empirical results show that REITs that earn more revenue from their prime business—property rentals—are less apt to take on risk but also achieve higher operational performance in the cross-section and over the medium term. The magnitudes of these results in a market crisis period are even stronger than in normal times. We also find evidence that REITs with higher POR are associated with less information asymmetry, higher operational efficiency, and higher market value. We also use three alternative REIT business focus measures based on their assets, expenses, and income. The results are qualitatively and quantitatively similar. To investigate why some REITs focus to a greater extent on non-prime businesses, the paper provides evidence that REIT executives receive, on average, higher pay when their firms engage more extensively in other businesses, and larger REITs are more likely to explore non-rental-revenue businesses. Lastly, we use the coronavirus pandemic as a quasi-experimental setting and provide evidence that REITs that have earned higher POR in recent years generally achieve better operational performance and reduce risk during the first three quarters of 2020. In sum, the results suggest that a REIT’s focus on its prime business generally leads to greater profitability and lower risk.
Fundamental Limits of Obfuscation for Linear Gaussian Dynamical Systems: An Information-Theoretic Approach

In this paper, we study the fundamental limits of obfuscation in terms of privacy-distortion tradeoffs for linear Gaussian dynamical systems via an information-theoretic approach. Particularly, we obtain analytical formulas that capture the fundamental privacy-distortion tradeoffs when privacy masks are to be added to the outputs of the dynamical systems, while indicating explicitly how to design the privacy masks in an optimal way: The privacy masks should be colored Gaussian with power spectra shaped specifically based upon the system and noise properties.
Ripple-Type Control for Enhancing Resilience of Networked Physical Systems

Distributed control agents have been advocated as an effective means for improving the resiliency of our physical infrastructures under unexpected events. Purely local control has been shown to be insufficient, centralized optimal resource allocation approaches can be slow. In this context, we put forth a hybrid low-communication saturation-driven protocol for the coordination of control agents that are distributed over a physical system and are allowed to communicate with peers over a “hotline” communication network. According to this protocol, agents act on local readings unless their control resources have been depleted, in which case they send a beacon for assistance to peer agents. Our ripple-type scheme triggers communication locally only for the agents with saturated resources and it is proved to converge. Moreover, under a monotonicity assumption on the underlying physical law coupling control outputs to inputs, the devised control is proved to converge to a configuration satisfying safe operational constraints. The assumption is shown to hold for voltage control in electric power systems and pressure control in water distribution networks. Numerical tests corroborate the efficacy of the novel scheme.
Evaluating the Evidence on Vertical Mergers

In theory, vertical mergers can have both procompetitive and anticompetitive effects. Many early empirical studies found benefits for vertical relationships; but the seminal surveys of this literature are now over a decade old. We review the empirical evidence from the last decade on vertical integration—as well as that in two frequently cited surveys from the mid-2000s. Taken as a whole, the empirical evidence as to the change in welfare that is due to vertical mergers is decidedly mixed, and should certainly not be used as a basis for a presumption that most vertical mergers are procompetitive or harmless.
Estimating basis functions in massive fields under the spatial mixed effects model

Spatial prediction is commonly achieved under the assumption of a Gaussian random field (GRF) by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values. For massive datasets, fixed rank kriging using the Expectation-Maximization (EM) algorithm for estimation has been proposed as an alternative to the usual but computationally prohibitive kriging method. The method reduces computation cost of estimation by redefining the spatial process as a linear combination of basis functions and spatial random effects. A disadvantage of this method is that it imposes constraints on the relationship between the observed locations and the knots. We develop an alternative method that utilizes the Spatial Mixed Effects (SME) model, but allows for additional flexibility by estimating the range of the spatial dependence between the observations and the knots via an Alternating Expectation Conditional Maximization (AECM) algorithm. Experiments show that our methodology improves estimation without sacrificing prediction accuracy while also minimizing the additional computational burden of extra parameter estimation. The methodology is applied to a temperature data set archived by the United States National Climate Data Center, with improved results over previous methodology.
The instability of functional connectomes across the first year of life.

Abstract The uniqueness and stability of the adolescent and adult functional connectome has been demonstrated to be high (80–95 % identification) using connectome-based identification (ID) or “fingerprinting”. However, it is unclear to what extent individuals exhibit similar distinctiveness and stability in infancy, a developmental period of rapid and unparalleled brain development. In this study, we examined connectome-based ID rates within and across the first year of life using a longitudinal infant dataset at 1.5 month and 9 months of age. We also calculated the test–retest reliability of individual connections across the first year of life using the intraclass correlation coefficient (ICC). Overall, we found substantially lower infant ID rates than have been reported in adult and adolescent populations. Within-session ID rates were moderate and significant (ID = 48.94–70.83 %). Between-session ID rates were very low and not significant, with task-to-task connectomes resulting in the highest between-session ID rate (ID = 26.6 %). Similarly, average edge-level test-retest reliability was higher within-session than between-session (mean within-session ICC = 0.17, mean between-session ICC = 0.10). These findings suggest a lack of uniqueness and stability in functional connectomes across the first year of life consistent with the unparalleled changes in brain functional organization during this critical period.
Identifying voter preferences through two-stage multivoting elections: Experiments in the preface of the 2020 democratic primaries

Abstract This paper examines a method of quantifying voter preferences and behavior using a two-stage multivoting (2SMV) model. The 2SMV model gives voters an endowment of additional votes exceeding the number of policies under consideration in a direct democracy-style election. Voters may freely allocate this endowment to any of the policies up for election. Using the 2SMV mechanism, the paper provides a methodology for identifying voter preferences and voting behavior within a staged multivoting system. From this methodology, three types of voting behavior are defined: policy indifference, strictly-dominating preferences, and fixed-weight preferences. Using experimental data collected from college students, the study evaluates the performance of the two-stage multivoting system in the context of the 2020 Democratic Presidential Primaries, compared to the traditional “one-person, one-vote” (1P1V) system. Using the full sample of observations, the 1P1V system resulted in a tie between Joe Biden and Bernie Sanders, while the 2SMV system selected Joe Biden over Bernie Sanders as the nominee by a 206 net vote difference. The study finds that the 2SMV system produces more unique and distant ranks between candidates, reducing the prevalence of ties common within the 1P1V system.
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis

We present DietNeRF, a 3D neural scene representation estimated from a few images. Neural Radiance Fields (NeRF) learn a continuous volumetric representation of a scene through multi-view consistency, and can be rendered from novel viewpoints by ray casting. While NeRF has an impressive ability to reconstruct geometry and fine details given many images, up to 100 for challenging 360° scenes, it often finds a degenerate solution to its image reconstruction objective when only a few input views are available. To improve few-shot quality, we propose DietNeRF. We introduce an auxiliary semantic consistency loss that encourages realistic renderings at novel poses. DietNeRF is trained on individual scenes to (1) correctly render given input views from the same pose, and (2) match high-level semantic attributes across different, random poses. Our semantic loss allows us to supervise DietNeRF from arbitrary poses. We extract these semantics using a pre-trained visual encoder such as CLIP, a Vision Transformer trained on hundreds of millions of diverse single-view, 2D photographs mined from the web with natural language supervision. In experiments, DietNeRF improves the perceptual quality of few-shot view synthesis when learned from scratch, can render novel views with as few as one observed image when pre-trained on a multi-view dataset, and produces plausible completions of completely unobserved regions.
Beyond single-shot fault-tolerant quantum error correction

Extensive quantum error correction is necessary in order to perform a useful computation on a noisy quantum computer. Moreover, quantum error correction must be implemented based on imperfect parity check measurements that may return incorrect outcomes or inject additional faults into the qubits. To achieve fault-tolerant error correction, Shor proposed to repeat the sequence of parity check measurements until the same outcome is observed sufficiently many times. Then, one can use this information to perform error correction. A basic implementation of this fault tolerance strategy requires Ω(rd2) parity check measurements for a distance-d code defined by r parity checks. For some specific highly structured quantum codes, Bombin has shown that single-shot fault-tolerant quantum error correction is possible using only r measurements. In this work, we consider a phenomenological noise model for parity check measurements assuming that each bit of a codeword and the measurement outcome suffer from independent bit flips with some error rate p. For this model, we demonstrate that fault-tolerant quantum error correction can be achieved using O(d log(d)) measurements for any code with distance d ≥ Ω(nα) for some constant α > 0. Moreover, we prove the existence of a sub-single-shot fault-tolerant quantum error correction scheme using fewer than r measurements. In some cases, the number of parity check measurements required for fault-tolerant quantum error correction is exponentially smaller than the number of parity checks defining the code. The short measurement sequences constructed generally have high weight and our phenomenological noise model is not realistic in this regime. Our error correction strategy could find applications to small codes and LDPC codes if one can manage to keep the weight of the measured parity checks low.
Household Deleveraging and Saving Rates: A Cross-Country Analysis

Historically high household debt in several economies is calling for a deleveraging, but according to some economists, this adjustment can slow GDP growth by weighing on consumption. Using a sample of advanced and emerging market economies, this paper finds evidence of a negative relationship between changes of household debt-to-income ratios and saving rates. This relationship is however asymmetric, being significant only for debt build-ups. Declining debt ratios and saving are significantly related in some economies, but the relationship is driven by consumer credit, not by mortgages. Results therefore suggest that the economic cost associated with household deleveraging may be overestimated and motivate a deleveraging via lower mortgages.
PoSAT: Proof-of-Work Availability and Unpredictability, Without the Work

An important feature of Proof-of-Work (PoW) blockchains is full dynamic availability, allowing miners to go online and offline while requiring only 50% of the online miners to be honest. Existing Proof-of-stake (PoS), Proof-of-Space and related protocols are able to achieve this property only partially, either putting the additional assumption that adversary nodes to be online from the beginning and no new adversary nodes come online afterwards, or use additional trust assumptions for newly joining nodes.We propose a new PoS protocol PoSAT which can provably achieve dynamic availability fully without any additional assumptions. The protocol is based on the longest chain and uses a Verifiable Delay Function for the block proposal lottery to provide an arrow of time. The security analysis of the protocol draws on the recently proposed technique of Nakamoto blocks as well as the theory of branching random walks. An additional feature of PoSAT is the complete unpredictability of who will get to propose a block next, even by the winner itself. This unpredictability is at the same level of PoW protocols, and is stronger than that of existing PoS protocols using Verifiable Random Functions.
Anisotropic Foams Via Frontal Polymerization.

The properties of foams, an important class of cellular solids, are most sensitive to the volume fraction and openness of its elementary compartments; size, shape, orientation, and the interconnectedness of the cells are other important design attributes. Control of these morphological traits would allow the tailored fabrication of useful materials including highly porous solids, anisotropic heat conductors, tough composites, among others. While approaches like ice templating has produced foams with elongated cells, there is a need for rapid, versatile, and energy efficient methods that also control the local order and macroscopic alignment of cellular elements. Here we describe a fast and convenient method to obtain anisotropic structural foams using frontal polymerization. We fabricated foams by curing mixtures of dicyclopentadiene and a physical blowing agent via frontal ring opening metathesis polymerization (FROMP). The materials were characterized using micro-computed tomography (Micro-CT) and an image analysis protocol to quantify morphological characteristics including volume fraction and anisotropy. The cellular structure, porosity, and hardness of the foams changed with blowing agent, concentration, and resin viscosity. Moreover, we used a full factorial combination of variables to correlate each parameter with the structure of the obtained foams. We found a strong correlation between the resin viscosity and the foam's cellular structure. Furthermore, a specific combination of input parameters controlled the transitions from (i) isotropic to anisotropic cellular structures, (ii) porous to non-porous, and (iii) soft to hard foams. Our results demonstrate the controlled production of foams with specific morphologies using the simple and efficient method of frontal polymerization. This work shows promise for creating foams with aligned cellular structures that allow anisotropic mass and energy transport properties in high performance structural solids. This article is protected by copyright. All rights reserved.
Entanglement Spheres and a UV-IR Connection in Effective Field Theories.

We show that long-distance quantum correlations probe short-distance physics. Two disjoint regions of the latticized, massless scalar field vacuum are numerically demonstrated to become separable at distances beyond the negativity sphere, which extends to infinity in the continuum limit. The size of this quantum coherent volume is determined by the highest momentum mode supported in the identical regions, each of diameter $d$. More generally, effective field theories (EFTs), describing a system up to a given momentum scale $\mathrm{\ensuremath{\Lambda}}$, are expected to share this feature---entanglement between regions of the vacuum depends upon the UV completion beyond a separation proportional to $\mathrm{\ensuremath{\Lambda}}$. Through calculations extended to three dimensions, the magnitude of the negativity at which entanglement becomes sensitive to UV physics in an EFT (lattice or otherwise) is conjectured to scale as $\ensuremath{\sim}{e}^{\ensuremath{-}\mathrm{\ensuremath{\Lambda}}d}$, independent of the number of spatial dimensions. It is concluded that two-region vacuum entanglement at increasing separations depends upon the structure of the theory at increasing momentum scales. This phenomenon may be manifest in perturbative QCD processes.
Bayesian meta-prior learning using Empirical Bayes

Adding domain knowledge to a learning system is known to improve results. In multiparameter Bayesian frameworks, such knowledge is incorporated as a prior. On the other hand, the various model para...
On the Intrinsic Robustness of NVM Crossbars Against Adversarial Attacks

The increasing computational demand of Deep Learning has propelled research in special-purpose inference accelerators based on emerging non-volatile memory (NVM) technologies. Such NVM crossbars promise fast and energy-efficient in-situ Matrix Vector Multiplication (MVM) thus alleviating the long-standing von Neuman bottleneck in today’s digital hardware. However, the analog nature of computing in these crossbars is inherently approximate and results in deviations from ideal output values, which reduces the overall performance of Deep Neural Networks (DNNs) under normal circumstances. In this paper, we study the impact of these non-idealities under adversarial circumstances. We show that the non-ideal behavior of analog computing lowers the effectiveness of adversarial attacks, in both Black-Box and White-Box attack scenarios. In a non-adaptive attack, where the attacker is unaware of the analog hardware, we observe that analog computing offers a varying degree of intrinsic robustness, with a peak adversarial accuracy improvement of 35.34%, 22.69%, and 9.90% for white box PGD (ϵ=1/255, iter =30) for CIFAR-10, CIFAR-100, and ImageNet respectively. We also demonstrate “Hardware-in-Loop” adaptive attacks that circumvent this robustness by utilizing the knowledge of the NVM model.
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time

Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obtain spatio-temporal super-resolution in neuronal time series by exploiting relationships among neurons, embedded in latent low-dimensional population dynamics. Our novel neural network training strategy, selective backpropagation through time (SBTT), enables learning of deep generative models of latent dynamics from data in which the set of observed variables changes at each time step. The resulting models are able to infer activity for missing samples by combining observations with learned latent dynamics. We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data. In electrophysiology, SBTT enables accurate inference of neuronal population dynamics with lower interface bandwidths, providing an avenue to significant power savings for implanted neuroelectronic interfaces. In applications to two-photon calcium imaging, SBTT accurately uncovers high-frequency temporal structure underlying neural population activity, substantially outperforming the current state-of-the-art. Finally, we demonstrate that performance could be further improved by using limited, high-bandwidth sampling to pretrain dynamics models, and then using SBTT to adapt these models for sparsely-sampled data.
Principles-based accounting standards and audit outcomes: empirical evidence

The purpose of this study is to empirically examine the relations between audit outcomes and accounting standard design (principles-based vs. rules-based) for US firms. Considering that audit outcomes may vary with audit risk, which may differ under different accounting standards, we examine and find that audit risk and audit fees are lower when client firms rely more on principles-based standards. Next, we find that principles-based standards are associated with a lower likelihood of receiving a going concern opinion and with a shorter audit report lag. However, for firms that rely more on principles-based standards and have greater incentives to engage in earnings management, audit fees are higher. Collectively, our results inform the FASB, the SEC, and the PCAOB of the potential benefits of using principles-based standards with respect to audit outcomes and, more broadly, provide evidence on the role of accounting standard design in auditing.
Strong and almost strong modes of Floquet spin chains in Krylov subspaces

Integrable Floquet spin chains are known to host strong zero and $\ensuremath{\pi}$ modes which are boundary operators that respectively commute and anticommute with the Floquet unitary generating stroboscopic time evolution, in addition to anticommuting with a discrete symmetry of the Floquet unitary. Thus the existence of strong modes implies a characteristic pairing structure of the full spectrum. Weak interactions modify the strong modes to almost strong modes that almost commute or anticommute with the Floquet unitary. Manifestations of strong and almost strong modes are presented in two different Krylov subspaces. One is a Krylov subspace obtained from a Lanczos iteration that maps the time evolution generated by the Floquet Hamiltonian onto dynamics of a single particle on a fictitious chain with nearest-neighbor hopping. The second is a Krylov subspace obtained from the Arnoldi iteration that maps the time evolution generated directly by the Floquet unitary onto dynamics of a single particle on a fictitious chain with longer-range hopping. While the former Krylov subspace is sensitive to the branch of the logarithm of the Floquet unitary, the latter obtained from the Arnoldi scheme is not. The effective single-particle models in the Krylov subspace are discussed, and the topological properties of the Krylov chain that ensure stable zero and $\ensuremath{\pi}$ modes at the boundaries are highlighted. The role of interactions is discussed. Expressions for the lifetime of the almost strong modes are derived in terms of the parameters of the Krylov subspace, and are compared with exact diagonalization.
Simultaneous variable selection, clustering, and smoothing in function‐on‐scalar regression

We address the problem of multicollinearity in a function-on-scalar regression model by using a prior which simultaneously selects, clusters, and smooths functional effects. Our methodology groups effects of highly correlated predictors, performing dimension reduction without dropping relevant predictors from the model. We validate our approach via a simulation study, showing superior performance relative to existing dimension reduction approaches in the function-on-scalar literature. We also demonstrate the use of our model on a data set of age specific fertility rates from the United Nations Gender Information database.
Exact Analytical Parallel Vectors

This paper demonstrates that parallel vector curves are piecewise cubic rational curves in 3D piecewise linear vector fields. Parallel vector curves -- loci of points where two vector fields are parallel -- have been widely used to extract features including ridges, valleys, and vortex core lines in scientific data. We define the term \emph{generalized and underdetermined eigensystem} in the form of $\mathbf{A}\mathbf{x}+\mathbf{a}=\lambda(\mathbf{B}\mathbf{x}+\mathbf{b})$ in order to derive the piecewise rational representation of 3D parallel vector curves. We discuss how singularities of the rationals lead to different types of intersections with tetrahedral cells.
Oscillator Strength Sums and Effective Electron Number in Optical Spectroscopy of Solids

Recently developed composite optical data for metallic aluminum are employed to illustrate various definitions of neff, the number of electrons effective in an absorptive process. Two effects are demonstrated : the “screening” of valence-electron processes by the polarizable ion cores and the redistribution of oscillator strength between valence and core electrons.
Pareto vs. Wicksell on adjustment costs in consumption: A reformulation

Abstract Pareto's adjustment cost model generating cyclical consumption patterns and Wicksell's observation that it contains mathematical errors are analyzed. An alternative model capturing the essence of Pareto's model and meeting Wicksell's criticisms is proposed where it is shown that consumption and leisure patterns may be cyclical over time.
Wavelength conversion in scaled silicon waveguides

Raman induced four-wave mixing is used to demonstrate wavelength conversion in silicon waveguides with sub-micron modal area. The properties of submicron waveguides that lend themselves to high efficiency conversion are also discussed.