cssi/SciSciGPT-SciSciNet huggingface dataset:
Decreasing the Surgical Errors by Neurostimulation of Primary Motor Cortex and the Associated Brain Activation via Neuroimaging

Acquisition of fine motor skills is a time-consuming process as it is based on learning via frequent repetitions. Transcranial electrical stimulation (tES) is a promising means of enhancing simple motor skill development via neuromodulatory mechanisms. Here, we report that non-invasive neurostimulation facilitates the learning of complex fine bimanual motor skills associated with a surgical task. During the training of 12 medical students on the Fundamentals of Laparoscopic Surgery (FLS) pattern cutting task over a period of 12 days, we observed that transcranial direct current stimulation (tDCS) decreased error level and the variability in performance, compared to the Sham group. Furthermore, by concurrently monitoring the cortical activations of the subjects via functional near-infrared spectroscopy (fNIRS), our study showed that the cortical activation patterns were significantly different between the tDCS and Sham group, with the activation of primary motor cortex (M1) and prefrontal cortex (PFC) contralateral to the anodal electrode significantly decreased while supplemental motor area (SMA) increased by tDCS. The lowered performance errors were retained after 1-month post-training. This work supports the use of tDCS to enhance performance accuracy in fine bimanual motor tasks.
Application of Seq2Seq Models on Code Correction.

We apply various seq2seq models on programming language correction tasks on Juliet Test Suite for C/C++ and Java of Software Assurance Reference Datasets(SARD), and achieve 75%(for C/C++) and 56%(for Java) repair rates on these tasks. We introduce Pyramid Encoder in these seq2seq models, which largely increases the computational efficiency and memory efficiency, while remain similar repair rate to their non-pyramid counterparts. We successfully carry out error type classification task on ITC benchmark examples (with only 685 code instances) using transfer learning with models pre-trained on Juliet Test Suite, pointing out a novel way of processing small programing language datasets. keyword: programing language correction, seq2seq, pyramid encoder, attention mechanism, transfer learning
Radiation oncologist perceptions of therapeutic cannabis use among cancer patients.

Cancer patients are increasingly incorporating medical marijuana into the management of treatment-related side effects. Currently however, data is limited regarding the risks and benefits of therapeutic cannabis for cancer patients. We sought to characterize radiation oncologists’ practices and opinions regarding therapeutic cannabis via a nationwide survey. An anonymous survey was distributed via email to 873 radiation oncologists in the American Society for Radiation Oncology member database. Radiation oncologists were asked their opinions and practices regarding the use of therapeutic cannabis for their patients. Bivariate analyses of potential predictors for responses were conducted using standard statistical techniques. One hundred seven radiation oncologists completed the survey. According to the survey, 36% of respondents would recommend therapeutic cannabis to their patients to mitigate treatment toxicity. Physicians practicing in states where medical marijuana is legal were more likely to recommend it compared to physicians working in states that have not legalized medical marijuana (OR = 3.79, 1.19–12.1, p = 0.01). Seventy-one percent of respondents reported therapeutic cannabis as being effective at least some of the time for managing treatment-related toxicities. Fifty-eight percent of physicians reported lacking sufficient knowledge to advise patients regarding therapeutic cannabis, while 86% of respondents were interested in learning more about therapeutic cannabis for cancer patients. Although a majority of radiation oncologists believe there are benefits to therapeutic cannabis, many are hesitant to recommend for or against its use. Radiation oncologists appear to be interested in learning more about how therapeutic cannabis may play a role in their patients’ care.
The fluid limit of a random graph model for a shared ledger

A shared ledger is a record of transactions that can be updated by any member of a group of users. The notion of independent and consistent record-keeping in a shared ledger is important for blockchain and more generally for distributed ledger technologies. In this paper we analyze a stochastic model for the shared ledger known as the tangle, which was devised as the basis for the IOTA cryptocurrency. The model is a random directed acyclic graph, and its growth is described by a non-Markovian stochastic process. We first prove ergodicity of the stochastic process, and then derive a delay differential equation for the fluid model which describes the tangle at high arrival rate. We prove convergence in probability of the tangle process to the fluid model, and also prove global stability of the fluid model. The convergence proof relies on martingale techniques.
Stochastic Adaptive Line Search for Differentially Private Optimization

The performance of private gradient-based optimization algorithms is highly dependent on the choice of step size (or learning rate) which often requires non-trivial amount of tuning. In this paper, we introduce a stochastic variant of classic backtracking line search algorithm that satisfies Renyi differential privacy. Specifically, the proposed algorithm adaptively chooses the step size satisfying the the Armijo condition (with high probability) using noisy gradients and function estimates. Furthermore, to improve the probability with which the chosen step size satisfies the condition, it adjusts per-iteration privacy budget during runtime according to the reliability of noisy gradient. A naive implementation of the backtracking search algorithm may end up using unacceptably large privacy budget as the ability of adaptive step size selection comes at the cost of extra function evaluations. The proposed algorithm avoids this problem by using the sparse vector technique combined with the recent privacy amplification lemma. We also introduce a privacy budget adaptation strategy in which the algorithm adaptively increases the budget when it detects that directions pointed by consecutive gradients are drastically different. Extensive experiments on both convex and non-convex problems show that the adaptively chosen step sizes allow the proposed algorithm to efficiently use the privacy budget and show competitive performance against existing private optimizers.
Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions

Lesion studies are an important tool for cognitive neuroscientists and neurologists. However, while brain lesion studies have traditionally aimed to localize neurological symptoms to specific anatomical loci, a growing body of evidence indicates that neurological diseases such as stroke are best conceptualized as brain network disorders. While researchers in the fields of neuroscience and neurology are therefore increasingly interested in quantifying the effects of focal brain lesions on the white matter connections that form the brain's structural connectome, few dedicated tools exist to facilitate this endeavor. Here, we present the Lesion Quantification Toolkit, a publicly available MATLAB software package for quantifying the structural impacts of focal brain lesions. The Lesion Quantification Toolkit uses atlas-based approaches to estimate parcel-level grey matter lesion loads and multiple measures of white matter disconnection severity that include tract-level disconnection measures, voxel-wise disconnection maps, and parcel-wise disconnection matrices. The toolkit also estimates lesion-induced increases in the lengths of the shortest structural paths between parcel pairs, which provide information about changes in higher-order structural network topology. We describe in detail each of the different measures produced by the toolkit, discuss their applications and considerations relevant to their use, and perform example analyses using real behavioral data collected from sub-acute stroke patients. We show that analyses performed using the different measures produced by the toolkit produce results that are highly consistent with results that have been reported in the prior literature, and we demonstrate the consistency of results obtained from analyses conducted using the different disconnection measures produced by the toolkit. We anticipate that the Lesion Quantification Toolkit will empower researchers to address research questions that would be difficult or impossible to address using traditional lesion analyses alone, and ultimately, lead to advances in our understanding of how white matter disconnections contribute to the cognitive, behavioral, and physiological consequences of focal brain lesions.
Small-scale creation for solutions of the SQG equation

We construct examples of solutions to the conservative surface quasigeostrophic (SQG) equation that must either exhibit infinite-in-time growth of derivatives or blow up in finite time.
Do Androids Dream of Electric Free Speech? Visions of the future of copyright, privacy and the First Amendment in science fiction

Science fiction authors have long projected the future of technology, including communication devices and the way in which future societies may use them. In this essay, these visions of future technology, and their implications on the future of media law and policy, are explored in three areas in particular – copyright, privacy, and the First Amendment. Themes examined include moving toward massively open copyright systems, problems of perpetual surveillance by the state, addressing rights of obscurity in public places threatened by wearable and implantable computing devices, and considering free speech rights of autonomous machines created by humans. In conclusion, the author offers a rebuttal to recent critics of legal scholarship by suggesting the value in exploratory, forward-looking research that examines the problems judges and policymakers may be addressing in the near and distant future.
Leading order corrections to the quantum extremal surface prescription

We show that a naive application of the quantum extremal surface (QES) prescription can lead to paradoxical results and must be corrected at leading order. The corrections arise when there is a second QES (with strictly larger generalized entropy at leading order than the minimal QES), together with a large amount of highly incompressible bulk entropy between the two surfaces. We trace the source of the corrections to a failure of the assumptions used in the replica trick derivation of the QES prescription, and show that a more careful derivation correctly computes the corrections. Using tools from one-shot quantum Shannon theory (smooth min- and max-entropies), we generalize these results to a set of refined conditions that determine whether the QES prescription holds. We find similar refinements to the conditions needed for entanglement wedge reconstruction (EWR), and show how EWR can be reinterpreted as the task of one-shot quantum state merging (using zero-bits rather than classical bits), a task gravity is able to achieve optimally efficiently.
A stochastic collocation approach for parabolic PDEs with random domain deformations.

Abstract In this article we analyze the linear parabolic partial differential equation with a stochastic domain deformation. In particular, we concentrate on the problem of numerically approximating the statistical moments of a given Quantity of Interest (QoI). The geometry is assumed to be random. The parabolic problem is remapped to a fixed deterministic domain with random coefficients and shown to admit an extension on a well defined region embedded in the complex hyperplane. The stochastic moments of the QoI are computed by employing a collocation method in conjunction with an isotropic Smolyak sparse grid. Theoretical sub-exponential convergence rates as a function to the number of collocation interpolation knots are derived. Numerical experiments are performed and they confirm the theoretical error estimates.
Maximizing Agreements for Ranking, Clustering and Hierarchical Clustering via MAX-CUT

In this paper, we study a number of well-known combinatorial optimization problems that fit in the following paradigm: the input is a collection of (potentially inconsistent) local relationships between the elements of a ground set (e.g., pairwise comparisons, similar/dissimilar pairs, or ancestry structure of triples of points), and the goal is to aggregate this information into a global structure (e.g., a ranking, a clustering, or a hierarchical clustering) in a way that maximizes agreement with the input. Well-studied problems such as rank aggregation, correlation clustering, and hierarchical clustering with triplet constraints fall in this class of problems. We study these problems on stochastic instances with a hidden embedded ground truth solution. Our main algorithmic contribution is a unified technique that uses the maximum cut problem in graphs to approximately solve these problems. Using this technique, we can often get approximation guarantees in the stochastic setting that are better than the known worst case inapproximability bounds for the corresponding problem. On the negative side, we improve the worst case inapproximability bound on several hierarchical clustering formulations through a reduction to related ranking problems.
Sound Event Detection in Urban Audio with Single and Multi-Rate Pcen

Recent literature has demonstrated that the use of per-channel energy normalization (PCEN), has significant performance improvements over traditional log-scaled mel-frequency spectrograms in acoustic sound event detection (SED) in a multi-class setting with overlapping events. However, the configuration of PCEN’s parameters is sensitive to the recording environment, the characteristics of the class of events of interest, and the presence of multiple overlapping events [1]. This leads to improvements on a class-by-class basis, but poor cross-class performance. In this article, we experiment using PCEN spectrograms as an alternative method for SED in urban audio using the UrbanSED dataset, demonstrating per-class improvements based on parameter configuration. Furthermore, we address cross-class performance with PCEN using a novel method, Multi-Rate PCEN (MR-PCEN). We demonstrate cross-class SED performance with MRPCEN, demonstrating improvements to cross-class performance compared to traditional single-rate PCEN.
Model-Assisted Uniformly Honest Inference for Optimal Treatment Regimes in High Dimension

This article develops new tools to quantify uncertainty in optimal decision making and to gain insight into which variables one should collect information about given the potential cost of measurin...
2D-Shape Analysis Using Conformal Mapping

The study of 2D shapes and their similarities is a central problem in the field of vision. It arises in particular from the task of classifying and recognizing objects from their observed silhouette. Defining natural distances between 2D shapes creates a metric space of shapes, whose mathematical structure is inherently relevant to the classification task. One intriguing metric space comes from using conformal mappings of 2D shapes into each other, via the theory of Teichmuller spaces. In this space every simple closed curve in the plane (a "shape") is represented by a `fingerprint' which is a diffeomorphism of the unit circle to itself (a differentiable and invertible, periodic function). More precisely, every shape defines to a unique equivalence class of such diffeomorphisms up to right multiplication by a Mobius map. The fingerprint does not change if the shape is varied by translations and scaling and any such equivalence class comes from some shape. This coset space, equipped with the infinitesimal Weil-Petersson (WP) Riemannian norm is a metric space. In this space, the shortest path between each two shapes is unique, and is given by a geodesic connecting them. Their distance from each other is given by integrating the WP-norm along that geodesic. In this paper we concentrate on solving the "welding" problem of "sewing" together conformally the interior and exterior of the unit circle, glued on the unit circle by a given diffeomorphism, to obtain the unique 2D shape associated with this diffeomorphism. This will allow us to go back and forth between 2D shapes and their representing diffeomorphisms in this "space of shapes". We then present an efficient method for computing the unique shortest path, the geodesic of shape morphing between each two end-point shapes. The group of diffeomorphisms of S1 acts as a group of isometries on the space of shapes and we show how this can be used to define shape transformations, like for instance `adding a protruding limb' to any shape.
ICASSP 2021 Deep Noise Suppression Challenge

The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality. We recently organized a DNS challenge special session at INTERSPEECH 2020 where we open-sourced training and test datasets for researchers to train their noise suppression models. We also open-sourced a subjective evaluation framework and used the tool to evaluate and select the final winners. Many researchers from academia and industry made significant contributions to push the field forward. We also learned that as a research community, we still have a long way to go in achieving excellent speech quality in challenging noisy real-time conditions. In this challenge, we expanded both our training and test datasets. Clean speech in the training set has increased by 200% with the addition of singing voice, emotion data, and non-English languages. The test set has increased by 100% with the addition of singing, emotional, non-English (tonal and non-tonal) languages, and, personalized DNS test clips. There are two tracks with focus on (i) real-time denoising, and (ii) real-time personalized DNS. We present the challenge results at the end.
Does unilateral divorce impact women’s labor supply? Evidence from Mexico

Abstract From 2008 to 2018, Mexican states introduced unilateral no-fault divorce. Using state-level variation in the timing and adoption of these divorce laws, we study how the legislation affected married women’s labor supply. Our results suggest that married women did not increase their labor force participation. Among employed married women, hours worked increased, but the effect is not large enough to be observed in the full sample of women. We find suggestive evidence that social norms against female labor supply and lack of access to formal work may explain the limited labor supply response. Our results highlight the importance of the cultural context in studying the consequences of divorce legislation.
GGA-Level Subsystem DFT Achieves Sub-kcal/mol Accuracy Intermolecular Interactions by Mimicking Nonlocal Functionals.

The key feature of nonlocal kinetic energy functionals is their ability to reduce to the Thomas-Fermi functional in the regions of high density and to the von Weizsacker functional in the region of low-density/high reduced density gradient. This behavior is crucial when these functionals are employed in subsystem DFT simulations to approximate the nonadditive kinetic energy. We propose a GGA nonadditive kinetic energy functional which mimics the good behavior of nonlocal functionals, retaining the computational complexity of typical semilocal functionals. Crucially, this functional depends on the inter-subsystem density overlap. The new functional reproduces Kohn-Sham DFT and benchmark CCSD(T) interaction energies of weakly interacting dimers in the S22-5 and S66 test sets with a mean absolute deviation well below 1 kcal/mol.
Impact of Nonuniform Thermionic Emission on the Transition Behavior Between Temperature-and Space-Charge-Limited Emission

Experimental observations have long-established that there exists a smooth roll-off or knee transition between the temperature-limited (TL) and full-space-charge-limited (FSCL) emission regions of the emission current density-temperature ( ${J}$ – ${T}$ ) (Miram) curve, or the emission current density-voltage ( ${J}$ – ${V}$ ) curve for a thermionic emission cathode. In this article, we demonstrate that this experimentally observed smooth transition does not require frequently used ${a}$ priori assumptions of a continuous distribution of work functions on the cathode surface. Instead, we find that the smooth transition arises as a natural consequence of the physics of nonuniform thermionic emission from a spatially heterogeneous cathode surface. We obtain this smooth transition for both ${J}$ – ${T}$ and ${J}$ – ${V}$ curves using a predictive nonuniform thermionic emission model that includes 3-D space charge, patch fields (electrostatic potential nonuniformity on the cathode surface based on local work function values), and Schottky barrier lowering physics and illustrates that a smooth knee can arise from a thermionic cathode surface with as few as two discrete work function values. Importantly, we find that the inclusion of patch field effects is crucial for obtaining accurate ${J}$ – ${T}$ and ${J}$ – ${V}$ curves, and the further inclusion of Schottky barrier lowering is needed for accurate ${J}$ – ${V}$ curves. This finding and the emission model provided in this article have important implications for modeling electron emission from realistic, heterogeneous surfaces. Such modeling is important for an improved understanding of the interplay of emission physics, cathode materials engineering, and the design of numerous devices employing electron emission cathodes.
Alpha-Robust Equilibrium in Anonymous Games

In this paper, we consider the notion of -robust equilibrium for finite strategic players in anonymous games, where utility of a player depends on other players’ actions only through the resulting distribution of actions played. This equilibrium is defined as the set of strategies of the players such that no user wants to deviate as long as N-alpha-1 number are playing the equilibrium strategies (whom we call rational). In this paper we provide sufficient conditions for the existence of such an equilibrium. We first define best response functions of the players for any arbitrary strategies of the rational and irrational players. Then we define a second response function that takes the intersection of the previous best responses over all possible strategies of the irrational players. We first prove an extension of Berge’s maximal theorem for correspondences to show that such an intersection is hemi-continuous. Using this and Kakutani’s fixed-point theorem, we argue that if such an intersection is non-empty for every strategy profile of the rational players then there exists an alpha-robust equilibrium. Then we provide sufficient conditions on the utilities of the players when such a best response could be non-empty.
SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning

We present an autonomous adaptive cruise control (ACC) system namely SAINT-ACC: {S}afety-{A}ware {Int}elligent {ACC} system (SAINT-ACC) that is designed to achieve simultaneous optimization of traffic efficiency, driving safety, and driving comfort through dynamic adaptation of the inter-vehicle gap based on deep reinforcement learning (RL). A novel dual RL agent-based approach is developed to seek and adapt the optimal balance between traffic efficiency and driving safety/comfort by effectively controlling the driving safety model parameters and inter-vehicle gap based on macroscopic and microscopic traffic information collected from dynamically changing and complex traffic environments. Results obtained through over 12,000 simulation runs with varying traffic scenarios and penetration rates demonstrate that SAINT-ACC significantly enhances traffic flow, driving safety and comport compared with the state-of-the-art approach.
DEEP-UV BASED ACOUSTO-OPTIC TUNABLE FILTER FOR SPECTRAL SENSING APPLICATIONS

In this paper, recent progress made in the development of quartz and KDP crystal based acousto-optic tunable filters (AOTF) are presented. These AOTFs are developed for operation over deep-UV to near-UV wavelengths of 190 nm to 400 nm. Preliminary output performance measurements of quartz AOTF and design specifications of KDP AOTF are presented. At 355 nm, the quartz AOTF device offered ∼15% diffraction efficiency with a passband full-width-half-maximum (FWHM) of less than 0.0625 nm. Further characterization of quartz AOTF devices at deep-UV wavelengths is progressing. The hermetic packaging of KDP AOTF is nearing completion. The solid-state optical sources being used for excitation include nonlinear optics based high-energy tunable UV transmitters that operate around 320 nm and 308 nm wavelengths, and a tunable deep-UV laser operating over 193 nm to 210 nm. These AOTF devices have been developed as turn-key devices for primarily for space-based chemical and biological sensing applications using laser induced Fluorescence and resonance Raman techniques.
Design of two-dimensional particle assemblies using isotropic pair interactions with an attractive well

Using ground-state and relative-entropy based inverse design strategies, isotropic interactions with an attractive well are determined to stabilize and promote assembly of particles into two-dimensional square, honeycomb, and kagome lattices. The design rules inferred from these results are discussed and validated in the discovery of interactions that favor assembly of the highly open truncated-square and truncated-hexagonal lattices.
Scale invariant robot behavior with fractals

Robots deployed at orders of magnitude different size scales, and that retain the same desired behavior at any of those scales, would greatly expand the environments in which the robots could operate. However it is currently not known whether such robots exist, and, if they do, how to design them. Since self similar structures in nature often exhibit self similar behavior at different scales, we hypothesize that there may exist robot designs that have the same property. Here we demonstrate that this is indeed the case for some, but not all, modular soft robots: there are robot designs that exhibit a desired behavior at a small size scale, and if copies of that robot are attached together to realize the same design at higher scales, those larger robots exhibit similar behavior. We show how to find such designs in simulation using an evolutionary algorithm. Further, when fractal attachment is not assumed and attachment geometries must thus be evolved along with the design of the base robot unit, scale invariant behavior is not achieved, demonstrating that structural self similarity, when combined with appropriate designs, is a useful path to realizing scale invariant robot behavior. We validate our findings by demonstrating successful transferal of self similar structure and behavior to pneumatically-controlled soft robots. Finally, we show that biobots can spontaneously exhibit self similar attachment geometries, thereby suggesting that self similar behavior via self similar structure may be realizable across a wide range of robot platforms in future.
SMART: A Situation Model for Algebra Story Problems via Attributed Grammar.

Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability. Previous neural solvers of math word problems directly translate problem texts into equations, lacking an explicit interpretation of the situations, and often fail to handle more sophisticated situations. To address such limits of neural solvers, we introduce the concept of a \emph{situation model}, which originates from psychology studies to represent the mental states of humans in problem-solving, and propose \emph{SMART}, which adopts attributed grammar as the representation of situation models for algebra story problems. Specifically, we first train an information extraction module to extract nodes, attributes, and relations from problem texts and then generate a parse graph based on a pre-defined attributed grammar. An iterative learning strategy is also proposed to improve the performance of SMART further. To rigorously study this task, we carefully curate a new dataset named \emph{ASP6.6k}. Experimental results on ASP6.6k show that the proposed model outperforms all previous neural solvers by a large margin while preserving much better interpretability. To test these models' generalization capability, we also design an out-of-distribution (OOD) evaluation, in which problems are more complex than those in the training set. Our model exceeds state-of-the-art models by 17\% in the OOD evaluation, demonstrating its superior generalization ability.
Bootstrap robust prescriptive analytics

We address the problem of prescribing an optimal decision in a framework where the cost function depends on uncertain problem parameters that need to be learned from data. Earlier work proposed prescriptive formulations based on supervised machine learning methods. These prescriptive methods can factor in contextual information on a potentially large number of covariates to take context specific actions which are superior to any static decision. When working with noisy or corrupt data, however, such nominal prescriptive methods can be prone to adverse overfitting phenomena and fail to generalize on out-of-sample data. In this paper we combine ideas from robust optimization and the statistical bootstrap to propose novel prescriptive methods which safeguard against overfitting. We show indeed that a particular entropic robust counterpart to such nominal formulations guarantees good performance on synthetic bootstrap data. As bootstrap data is often a sensible proxy to actual out-of-sample data, our robust counterpart can be interpreted to directly encourage good out-of-sample performance. The associated robust prescriptive methods furthermore reduce to convenient tractable convex optimization problems in the context of local learning methods such as nearest neighbors and Nadaraya–Watson learning. We illustrate our data-driven decision-making framework and our novel robustness notion on a small newsvendor problem.
2-D Modeling to Understand the Design Configuration and Flow Dynamics of Pond-in-Pond (Pip) Wastewater Treatment System for Reuse

Abstract Water reuse for irrigation is increasingly recognized as an essential and economical strategy in areas with water scarcity. A simple, low cost, low-maintenance, and highly efficient Pond-In-Pond (PIP) treatment system can be used for wastewater reuse. PIP is a treatment technology in which two types of ponds -- anaerobic and aerobic -- are combined into a single pond and consists of a deeper inner section entirely submerged within the outer pond. Previous studies on PIPs and PIP-like systems have reinforced the potential for reuse through promising performance results with BOD removal over 80 % and a reduction in land area requirements by approximately 40 %. Yet, no prior efforts have been made to understand the performance mechanism of such systems. This study makes use of two, 2-D modeling tools in developing a fundamental understanding of PIP flow dynamics and the expected performance. The modeling results showed that the PIP configuration offers improved flow-diversion along with reduced flow velocity. Additionally, the PIP retained approximately 17 % more (p
Experience Effects in Finance: Foundations, Applications, and Future Directions

This article establishes four key findings of the growing literature on experience effects in finance: (1) the long-lasting imprint of past experiences on beliefs and risk taking, (2) recency effects, (3) the domain-specificity of experience effects, and (4) imperviousness to information that is not experience-based. I first discuss the neuroscientific foundations of experience-based learning and sketch a simple model of its role in the stock market based on Malmendier et al. (2020a,b). I then distill the empirical findings on experience effects in stock-market investment, trade dynamics, and international capital flows, highlighting these four key features. Finally, I contrast models of belief formation that rely on “learned information” with models accounting for the neuroscience evidence on synaptic tagging and memory formation, and provide directions for future research.
Nonlinear fluid damping of elastically mounted pitching wings in quiescent water

We experimentally study the nonlinear fluid damping of a rigid but elastically mounted pitching wing in the absence of a free-stream flow. The dynamics of the elastic mount are simulated using a cyber-physical system. We perturb the wing and measure the fluid damping coefficient from damped oscillations over a large range of pitching frequencies, pitching amplitudes, pivot locations and sweep angles. A universal fluid damping scaling is proposed to incorporate all these parameters. Flow fields obtained using particle image velocimetry are analysed to explain the nonlinear behaviours of the fluid damping.
Evidence Acquisition and Voluntary Disclosure

A sender seeks hard evidence to persuade a receiver to take a certain action. There is uncertainty about whether the sender obtains evidence. If she does, she can choose to disclose it or pretend to not have obtained it. When the probability of obtaining information is low, we show that the optimal evidence structure is a binary certification: all it reveals is whether the (continuous) state of the world is above or below a certain threshold. Moreover, the set of low states that are concealed is non-monotone in the probability of obtaining evidence. When binary structures are optimal, higher uncertainty leads to less pooling at the bottom because the sender uses binary certification to commit to disclose evidence more often.
Learning without loss

We explore a new approach for training neural networks where all loss functions are replaced by hard constraints. The same approach is very successful in phase retrieval, where signals are reconstructed from magnitude constraints and general characteristics (sparsity, support, etc.). Instead of taking gradient steps, the optimizer in the constraint based approach, called relaxed–reflect–reflect (RRR), derives its steps from projections to local constraints. In neural networks one such projection makes the minimal modification to the inputs x, the associated weights w, and the pre-activation value y at each neuron, to satisfy the equation $x\cdot w=y$ . These projections, along with a host of other local projections (constraining pre- and post-activations, etc.) can be partitioned into two sets such that all the projections in each set can be applied concurrently—across the network and across all data in the training batch. This partitioning into two sets is analogous to the situation in phase retrieval and the setting for which the general purpose RRR optimizer was designed. Owing to the novelty of the method, this paper also serves as a self-contained tutorial. Starting with a single-layer network that performs nonnegative matrix factorization, and concluding with a generative model comprising an autoencoder and classifier, all applications and their implementations by projections are described in complete detail. Although the new approach has the potential to extend the scope of neural networks (e.g. by defining activation not through functions but constraint sets), most of the featured models are standard to allow comparison with stochastic gradient descent.
Scam Pandemic: How Attackers Exploit Public Fear through Phishing

As the COVID-19 pandemic started triggering widespread lockdowns across the globe, cybercriminals did not hesitate to take advantage of users’ increased usage of the Internet and their reliance on it. In this paper, we carry out a comprehensive measurement study of online social engineering attacks in the early months of the pandemic. By collecting, synthesizing, and analyzing DNS records, TLS certificates, phishing URLs, phishing website source code, phishing emails, web traffic to phishing websites, news articles, and government announcements, we track trends of phishing activity between January and May 2020 and seek to understand the key implications of the underlying trends.We find that phishing attack traffic in March and April 2020 skyrocketed up to 220% of its pre-COVID-19 rate, far exceeding typical seasonal spikes. Attackers exploited victims’ uncertainty and fear related to the pandemic through a variety of highly targeted scams, including emerging scam types against which current defenses are not sufficient as well as traditional phishing which outpaced the ecosystem’s collective response.
The Innovation and Reporting Consequences of Financial Regulation for Young Life-Cycle Firms

Firm life-cycle stage reflects a firm’s current strategic direction towards exploration independent of age or size. We provide evidence that young life-cycle firms are particularly vulnerable to negative innovation consequences from financial regulation but do not appear to experience any compensating financial reporting quality benefits. Using a generalized difference-in-differences design around SOX, we document a significant reduction in both R&D spending and innovation outputs for young life-cycle stage firms after regulation. Declines in innovation manifest both from the diversion of scarce resources and from the imposition of an organizational culture mismatched to the pursuit of explorative innovation, resulting in a less generalizable and less diversified patent portfolio. However, we find no evidence that improvements to financial reporting quality materialize to offset these costs. Event study analyses suggests that this negative impact was expected by market participants, and post-regulation returns confirm this expectation.
A passively pumped vacuum package sustaining cold atoms for more than 200 days

Compact cold-atom sensors depend on vacuum technology. One of the major limitations to miniaturizing these sensors is the active pumps—typically ion pumps—that are required to sustain the low pressure needed for laser cooling. Although passively pumped chambers have been proposed as a solution to this problem, technical challenges have prevented successful operation at the levels needed for cold-atom experiments. The authors present the first demonstration of a vacuum package successfully independent of ion pumps for more than a week; their vacuum package is capable of sustaining a cloud of cold atoms in a magneto-optical trap (MOT) for greater than 200 days using only non-evaporable getters and a rubidium dispenser. Measurements of the MOT lifetime indicate that the package maintains a pressure of better than 2 × 10 − 7 Torr. This result will significantly enable the development of compact atomic sensors, including those sensitive to magnetic fields, where the absence of an ion pump will be advantageous.
The Welfare Effects of Vertical Mergers and their Remedies

This paper extends Williamson’s (1968, 58(1):p. 18–36) classic framework of the welfare effect of mergers to the case of vertical mergers, and in particular to those in which the imposition of merger conditions (remedies) may allow an otherwise anticompetitive merger to proceed. While similarities to the case of horizontal mergers without a remedies option are present, differences also arise. Most notably: For prototypical vertical mergers, remedies may yield post–merger economic welfare that is higher than pre–merger levels. This suggests that remedies that are directed toward vertical mergers hold the promise of a more beneficial approach than in the case of horizontal mergers.
Fractography of the high temperature hydrogen attack of a medium carbon steel

Results are reported for an experimental study of the microscopic fracture processes associated with hydrogen attack of a commercially produced plain carbon steel in a well-controlled high-temperature hydrogen environment of high purity. In the experiments, sheet samples were exposed to laboratory-grade hydrogen at a pressure of 3.5 MN/sq m and a temperature of 575 C. The fractography of gas-filled fissures and failed tension specimens is analyzed in an effort to identify any predominant microstructural defect associated with fissure formation, the prevalent modes of fracture, and the contribution of gas-filled fissures to the overall failure process. It is found that the tensile properties of the examined steel were significantly degraded after as few as 136 hr of exposure to a high-purity hydrogen atmosphere at 575 C; that the yield strength, ultimate strength, and elongation at fracture were all reduced progressively with increasing exposure time; and that the yield and ultimate strengths were reduced more than 40% after 408 hr while elongation was reduced to less than 2%.
THEORETICAL EFFECT OF VARIOUS BROADENING PARAMETERS ON ULTRAVIOLET LINE PROFILES.

The relative importance of various damping parameters in UV lines under conditions of normal stellar atmospheres is studied, giving attention to temperatures in the range from 10,000 to 30,000 K. It is found that the damping by electron collisions is always much smaller than either the radiative or the classical damping, except for lines that arise from high-excitation levels and do not therefore develop strong damping wings. A damping constant equal to 10 times the classical value is always much too large when compared to the more realistic (electron + radiative) damping.
Matrix Concentration for Products

This paper develops nonasymptotic growth and concentration bounds for a product of independent random matrices. These results sharpen and generalize recent work of Henriksen–Ward, and they are similar in spirit to the results of Ahlswede–Winter and of Tropp for a sum of independent random matrices. The argument relies on the uniform smoothness properties of the Schatten trace classes.
Principles Determining the Structure of Transition Metals.

For the better part of a century researchers across disciplines have sought to explain the crystallography of the elemental transition metals: hexagonal close packed, body centered cubic, and face centered cubic in a form similar to that used to rationalize the structure of organic molecules and inorganic complexes. Pauling himself tried with limited success to address the origins of transition metal stability. These early investigators were handicapped, however, by incomplete knowledge regarding the structure of metallic electron density. Here, we exploit modern approaches to electron density analysis to first comprehensively describe transition metal electron density. Then, we use topological partitioning and quantum mechanically rigorous treatments of kinetic energy to account for the structure of the density as arising from the interactions between metallic polyhedra. We argue that the crystallography of the early transition metals results from charge transfer from the so called “octahedral” to “tetrahedral cages” while the face centered cubic structure of the late transition metals is a consequence of anti-bonding interactions that increase octahedral hole kinetic energy.
Intranasal oxytocin drives coordinated social approach

Coordinated responses to challenge are essential to survival for bonded monogamous animals and may depend on behavioral compatibility. Oxytocin (OT) context-dependently regulates social affiliation and vocal communication, but its role in pair members' decision to jointly respond to challenge is unclear. To test for OT effects, California mouse females received an intranasal dose of OT (IN-OT) or saline after bonding with males either matched or mismatched in their approach response to an aggressive vocal challenge. Pair mates were re-tested jointly for approach response, time spent together, and vocalizations. Females and males converged in their approach after pairing, but mismatched pairs with females given a single dose of IN-OT displayed a greater convergence that resulted from behavioral changes by both pair members. Unpaired females given IN-OT did not change their approach, indicating a social partner was necessary for effects to emerge. Moreover, IN-OT increased time spent approaching together, suggesting behavioral coordination beyond a further increase in bonding. This OT-induced increase in joint approach was associated with a decrease in the proportion of sustained vocalizations, a type of vocalization that can be associated with intra-pair conflict. Our results expand OT's effects on behavioral coordination and underscore the importance of emergent social context.
MicroED structure of the human adenosine receptor determined from a single nanocrystal in LCP.

G protein-coupled receptors (GPCRs), or seven-transmembrane receptors, are a superfamily of membrane proteins that are critically important to physiological processes in the human body. Determining high-resolution structures of GPCRs without bound cognate signaling partners, such as a G protein, requires crystallization in lipidic cubic phase (LCP). GPCR crystals grown in LCP are often too small for traditional X-ray crystallography. These microcrystals are ideal for investigation by microcrystal electron diffraction (MicroED), but the gel-like nature of LCP makes traditional approaches to MicroED sample preparation insurmountable. Here, we show that the structure of a human A2A adenosine receptor can be determined by MicroED after converting the LCP into the sponge phase followed by focused ion-beam milling. We determined the structure of the A2A adenosine receptor to 2.8-A resolution and resolved an antagonist in its orthosteric ligand-binding site, as well as four cholesterol molecules bound around the receptor. This study lays the groundwork for future structural studies of lipid-embedded membrane proteins by MicroED using single microcrystals that would be impossible with other crystallographic methods.
On two geometric means and sum of adjoint orbits

Abstract In this paper, we study the metric geometric mean introduced by Pusz and Woronowicz and the spectral geometric mean introduced by Fiedler and Ptak, originally for positive definite matrices. The relation between t-metric geometric mean and t-spectral geometric mean is established via log majorization. The result is then extended in the context of symmetric space associated with a noncompact semisimple Lie group. For any Hermitian matrices X and Y, So's matrix exponential formula asserts that there are unitary matrices U and V such that e X / 2 e Y e X / 2 = e U X U ⁎ + V Y V ⁎ . In other words, the Hermitian matrix log ⁡ ( e X / 2 e Y e X / 2 ) lies in the sum of the unitary orbits of X and Y. So's result is also extended to a formula for adjoint orbits associated with a noncompact semisimple Lie group.
Optimization of AAV6 transduction enhances site-specific genome editing of primary human lymphocytes.

Adeno-associated virus serotype 6 (AAV6) is a valuable reagent for genome editing of hematopoietic cells due to its ability to serve as a homology donor template. However, a comprehensive study of AAV6 transduction of hematopoietic cells in culture, with the goal of maximizing ex vivo genome editing, has not been reported. Here, we evaluated how the presence of serum, culture volume, transduction time, and electroporation parameters could influence AAV6 transduction. Based on these results, we identified an optimized protocol for genome editing of human lymphocytes based on a short, highly concentrated AAV6 transduction in the absence of serum, followed by electroporation with a targeted nuclease. In human CD4+ T cells and B cells, this protocol improved editing rates up to 7-fold and 21-fold, respectively, when compared to standard AAV6 transduction protocols described in the literature. As a result, editing frequencies could be maintained using 50- to 100-fold less AAV6, which also reduced cellular toxicity. Our results highlight the important contribution of cell culture conditions for ex vivo genome editing with AAV6 vectors and provide a blueprint for improving AAV6-mediated homology-directed editing of human T and B cells.