cssi/SciSciGPT-SciSciNet huggingface dataset:
Do auditor judgment frameworks help in constraining aggressive reporting? Evidence under more precise and less precise accounting standards

We experimentally investigate whether alternative judgment frameworks help Big 4 audit managers and partners constrain management's aggressive financial reporting under accounting standards that differ in their precision. We find that a framework based on the SEC's Advisory Committee on Improvements to Financial Reporting's (CIFiR's) recommendation that auditors critically evaluate the pros and cons of alternative accounting methods helps auditors constrain aggressive reporting under less precise standards. While our results highlight a limitation of counterfactual reasoning on its own at enhancing auditors' constraint of aggressive reporting, this study provides evidence on how structured thinking can overcome this limitation. In particular, we find that combining this consideration of the alternatives with a structured thought process that encourages auditors to think about the issue at increasing levels of abstraction effectively shifts auditors' focus away from client considerations and towards substance-over-form considerations, thereby incrementally enhancing auditors' constraint of aggressive reporting across different levels of accounting standard precision. These results should be of interest to academics, regulators, standard-setters, and auditors as they continue to contemplate ways to improve auditors' professional judgments under different levels of accounting standard precision.
Does Kant Have a Theory of Punishment

Several years ago I thought that I understood Kant’s theory of criminal punishment—an understanding aided by a Rawlsian reconstruction of certain aspects of that theory.1 I thought that Kant’s theory was profound, inspiring, and—although subject to certain problems—the only morally acceptable theory of punishment.2 I was confident that all philosophical work on problems of crime and punishment could be done within a generally Kantian perspective (which I took to be an unambiguously retributive perspective), and some of my own work exhibited that confidence to an uncritical degree.3
World Markets for Raising New Capital

Abstract We examine the extent to which firms from different countries rely on alternative sources of capital, the locations in which they raise capital, and the factors that affect these choices. During the 1990–2001 period, firms raised about $25.3 trillion of new capital, including $4.9 trillion from abroad. International debt issuances are substantially more common than equity issuances, with debt (equity) issues accounting for 87% (9%) of all securities issued internationally, and about 20% (12%) of all public debt issuances. Market timing considerations appear to be important in security issuance decisions in most countries.
Multi-lateral trade negotiations and the Most Favored Nation clause

Abstract This paper considers the interaction between private information and the Most Favored Nation clause in trade negotiations. It demonstrates that by aggregating uncertainty over a number of trading partners, the Most Favored Nation clause may offer a welfare improvement over a set of bilateral trade negotiations. This improvement is shown to be most pronounced when a large number of countries are involved in negotiations.
The Changing Standard of Accountability and the Positive Relationship between Human Rights Treaty Ratification and Compliance

Researchers have puzzled over the finding that countries that ratify UN human rights treaties such as the Convention Against Torture are more likely to abuse human rights than non-ratifiers over time. This article presents evidence that the changing standard of accountability – the set of expectations that monitoring agencies use to hold states responsible for repressive actions – conceals real improvements to the level of respect for human rights in data derived from monitoring reports. Using a novel dataset that accounts for systematic changes to human rights reports, it is demonstrated that the ratification of human rights treaties is associated with higher levels of respect for human rights. This positive relationship is robust to a variety of measurement strategies and model specifications.
Congress, the Supreme Court, and Judicial Review: Testing a Constitutional Separation of Powers Model

RecentscholarshipsuggeststhattheU.S.SupremeCourtmightbeconstrainedbyCongressinconstitutionalcases.Wesuggest two potential paths to Congressional influence on the Court’s constitutional decisions: a rational-anticipation model, in which the Court moves away from its preferences in order to avoid being overruled, and an institutional-maintenance model, in which the Court protects itself against Congressional attacks to its institutional prerogatives by scaling back its striking of laws when the distance between the Court and Congress increases. We test these models by using Common Space Scores and the original roll-call votes to estimate support in the current Congress for the original legislation and the Court’s preferences over that legislation. We find that the Court does not appear to consider the likelihood of override in Constitutional cases, but it does back away from striking laws when it is ideologically distant from Congress.
Metric-Free Individual Fairness with Cooperative Contextual Bandits

Data mining algorithms are increasingly used in automated decision making across all walks of daily life. Unfortunately, as reported in several studies these algorithms learn bias from data and environment leading to unequitable and unfair solutions. To mitigate bias in machine learning, different formalizations of fairness have been proposed that can be categorized into group fairness and individual fairness. Group fairness requires that different groups should be treated similarly which might be unfair to some individuals within a group. On the other hand, individual fairness requires that similar individuals be treated similarly. However, individual fairness remains understudied due to its reliance on problem-specific similarity metric. We propose a metric-free individual fairness and a cooperative contextual bandits (CCB) algorithm. The CCB algorithm utilizes fairness as a reward and attempts to maximize it. The advantage of treating fairness as a reward is that the fairness criterion does not need to be differentiable. The proposed algorithm is tested on multiple real-world benchmark datasets. The results show the effectiveness of the proposed algorithm at mitigating bias and at achieving both individual and group fairness.
Semantic Implicit Neural Scene Representations With Semi-Supervised Training

The recent success of implicit neural scene representations has presented a viable new method for how we capture and store 3D scenes. Unlike conventional 3D representations, such as point clouds, which explicitly store scene properties in discrete, localized units, these implicit representations encode a scene in the weights of a neural network which can be queried at any coordinate to produce these same scene properties. Thus far, implicit representations have primarily been optimized to estimate only the appearance and/or 3D geometry information in a scene. We take the next step and demonstrate that an existing implicit representation (SRNs) [67] is actually multi-modal; it can be further leveraged to perform per-point semantic segmentation while retaining its ability to represent appearance and geometry. To achieve this multi-modal behavior, we utilize a semi-supervised learning strategy atop the existing pre-trained scene representation. Our method is simple, general, and only requires a few tens of labeled 2D segmentation masks in order to achieve dense 3D semantic segmentation. We explore two novel applications for this semantically aware implicit neural scene representation: 3D novel view and semantic label synthesis given only a single input RGB image or 2D label mask, as well as 3D interpolation of appearance and semantics.
Strategic Experimentation with Congestion

This paper considers a two-player game of strategic experimentation with competition. Each agent faces a two-armed bandit problem where she continually chooses between her private, risky arm and a common, safe arm. Each agent has exclusive access to her private arm. However, the common arm can only be activated by one agent at a time. This congestion creates negative payoff externalities. Our main finding is that congestion gives rise to new strategic considerations: players perceive a strategic option value from occupying the common arm, making it more attractive than in the absence of competition or when switching is irreversible.
Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis

This paper proposes a multi-agent reinforcement learning based medium access framework for wireless networks. The access problem is formulated as a Markov Decision Process (MDP) and solved using reinforcement learning (RL) with every network node acting as a distributed learning agent. The solution components are developed step by step, starting from a single-node access scenario in which a node agent incrementally learns to control MAC layer packet loads for reining in self-collisions. The strategy is then scaled up for multi-node fully connected scenarios by using more elaborate RL reward structures. It also demonstrates preliminary feasibility for more general partially connected topologies. It is shown that by learning to adjust MAC layer transmission probabilities, the protocol is not only able to attain theoretical maximum throughput at an optimal load, but unlike classical approaches, it can also retain that maximum throughput at higher loading conditions. Additionally, the mechanism is agnostic to heterogeneous loading while preserving that feature. It is also shown that access priorities of the protocol across nodes can be parametrically adjusted. Finally, it is also shown that the online learning feature of reinforcement learning is able to make the protocol adapt to time-varying loading conditions.
Collapsing and the convex hull property in a soap film capillarity model

Abstract Soap films hanging from a wire frame are studied in the framework of capillarity theory. Minimizers in the corresponding variational problem are known to consist of positive volume regions with boundaries of constant mean curvature/pressure, possibly connected by “collapsed” minimal surfaces. We prove here that collapsing only occurs if the mean curvature/pressure of the bulky regions is negative, and that, when this last property holds, the whole soap film lies in the convex hull of its boundary wire frame.
Public Debt and r - g at Risk

As interest rate-growth differentials (r-g) turned negative in many countries, governments consider pursuing fiscal expansion and the potential risks involved. Using a large sample of advanced and emerging economies, our analysis suggests that high public debts can lead to adverse future r-g dynamics. Specifically, countries with higher initial public debt experience (i) a shorter duration of negative r-g episodes and a higher probability of reversal, (ii) higher average r-g, and (iii) a more right-skewed r-g distribution, that implies higher down-side risks. Furthermore, high-debt countries experience larger increases in interest rates in response to (iv) an unexpected decline in domestic output and (v) an increase of global volatility. Results are stronger when public debts are denominated in foreign currencies.
Understanding and Controlling Hexagonal Patterns of Wrinkles in Neo-Hookean Elastic Bilayer Structures

A controlled surface wrinkling pattern has been widely used in diverse applications such as stretchable electronics, smart windows, and haptics. Here, we focus on hexagonal wrinkling patterns becau...
A Short Review of Loop Quantum Gravity.

An outstanding open issue in our quest for physics beyond Einstein is the unification of general relativity (GR) and quantum physics. Loop quantum gravity (LQG) is a leading approach toward this goal. At its heart is the central lesson of GR: Gravity is a manifestation of spacetime geometry. Thus, the approach emphasizes the quantum nature of geometry and focuses on its implications in extreme regimes -- near the big bang and inside black holes-- where Einstein's smooth continuum breaks down. We present a brief overview of the main ideas underlying LQG and highlight a few recent advances. This report is addressed to non-experts.
Volumes of line bundles on schemes

Volumes of line bundles are known to exist as limits on generically reduced projective schemes. However, it is not known if they always exist as limits on more general projective schemes. We show that they do always exist as a limit on a codimension one subscheme of a nonsingular projective variety.
Mitigating Bias in Set Selection with Noisy Protected Attributes

Subset selection algorithms are ubiquitous in AI-driven applications, including, online recruiting portals and image search engines, so it is imperative that these tools are not discriminatory on the basis of protected attributes such as gender or race. Currently, fair subset selection algorithms assume that the protected attributes are known as part of the dataset. However, protected attributes may be noisy due to errors during data collection or if they are imputed (as is often the case in real-world settings). While a wide body of work addresses the effect of noise on the performance of machine learning algorithms, its effect on fairness remains largely unexamined. We find that in the presence of noisy protected attributes, in attempting to increase fairness without considering noise, one can, in fact, decrease the fairness of the result! Towards addressing this, we consider an existing noise model in which there is probabilistic information about the protected attributes (e.g., [19, 32, 44, 56]), and ask is fair selection possible under noisy conditions? We formulate a "denoised" selection problem which functions for a large class of fairness metrics; given the desired fairness goal, the solution to the denoised problem violates the goal by at most a small multiplicative amount with high probability. Although this denoised problem turns out to be NP-hard, we give a linear-programming based approximation algorithm for it. We evaluate this approach on both synthetic and real-world datasets. Our empirical results show that this approach can produce subsets which significantly improve the fairness metrics despite the presence of noisy protected attributes, and, compared to prior noise-oblivious approaches, has better Pareto-tradeoffs between utility and fairness.
Event-Based Signal Temporal Logic Synthesis for Single and Multi-Robot Tasks

We propose a new specification language and control synthesis technique for single and multi-robot high-level tasks; these tasks include timing constraints and reaction to environmental events. Specifically, we define Event-based Signal Temporal Logic (STL) and use it to encode tasks that are reactive to uncontrolled environment events. Our control synthesis approach to Event-based STL tasks combines automata and control barrier functions to produce robot behaviors that satisfy the specification when possible. Our method automatically provides feedback to the user if an Event-based STL task cannot be achieved. We demonstrate the effectiveness of the framework through simulations and physical demonstrations of multi-robot tasks.
Swine Growth Promotion with Antibiotics or Alternatives can Increase Antibiotic Resistance Gene Mobility Potential

Even though the use of antibiotics for food-producing animals may contribute to the emergence of antimicrobial resistance, antibiotics are still used as growth promoters. Due to consumer and regulatory pressures, the use of alternatives to antibiotics as growth promoters is increasing, thus more information is needed on their capability to disseminate antimicrobial resistance compared to antibiotics. We investigated the impacts of carbadox (antibiotic), copper sulfate and zinc oxide (metals) and mushroom powder (natural product) on the pig fecal resistome and microbiome. Antibiotic resistance gene (ARG) and mobile genetic element (MGE) abundances were measured using a high-throughput qPCR array with 382 primer pairs. Bacterial community composition was determined by 16S rRNA gene sequencing. More ARGs co-occurred with MGEs in the growth promoter group samples than in the control group samples. Community composition could not be linked to resistome in the growth promoter group samples, indicating a potential decoupling of ARGs and phylogeny. Additionally, machine-learning methods aided in defining the community and resistome differences in response to treatments. Since increased ARG mobility potential was the primary response to the dietary additives used in this study, we suggest that ARG mobility should be considered when designing antimicrobial use policies and antimicrobial resistance surveillances.
Shortfalls and opportunities in terrestrial vertebrate species discovery.

Much of biodiversity remains undiscovered, causing species and their functions to remain unrealized and potentially lost in ignorance. Here we use extensive species-level data in a time-to-event model framework to identify taxonomic and geographic discovery gaps in terrestrial vertebrates. Biological, environmental and sociological factors all affect discovery probability and together provide strong predictive ability for species discovery. Our model identifies distinct taxonomic and geographic unevenness in future discovery potential, with greatest opportunities for amphibians and reptiles, and for Neotropical and Indo-Malayan forests. Brazil, Indonesia, Madagascar and Colombia emerge as holding greatest discovery opportunities, with a quarter of potential discoveries estimated. These findings highlight the importance of international policy support for basic taxonomic research and the potential of quantitative models to aid species discovery. The number of species unknown to science is profound and limits effective conservation of diversity. Here the authors estimate which clades and locations are most ripe for taxonomic discovery.
John Paul II and Employee Participation in Corporate Governance

Catholic Social Thought ("CST") has called for employees to be active participants in the governance of the enterprises for which they work. This article looks at what CST has to say about employee participation. It shows that John Paul II's distinctive contribution, which was consistent with previous CST, was to lay bare the theological and philosophical justifications for CST's approach to this issue.
Total synthesis of (+)-dactylolide.

The total synthesis of the new cytotoxic marine macrolide (+)-dactylolide (1) has been achieved in nine steps from known vinyl bromide (−)-AB. In addition, (+)-zampanolide (2) has been converted to...
The Disruptive Effect of Open Platforms on Markets for Wireless Services

Application-based discrimination is common in telecommunications. Wireless carriers charge consumers more per byte of traffic for SMS text messages than they do for wireless surfing or voice calls. Such discrimination is possible because carriers and handset manufacturers have the ability to tag and meter each application. While tagging and metering are possible in the case of proprietary platforms such as iPhone, they are not in the case of open platforms like Android. Android is open source with open application programming interfaces, and anyone can develop applications for it. Because the carriers have little control over applications, Android is inherently disruptive of discriminatory pricing across applications. Users and neutrality advocates support Android, believing that a disruption of discrimination can increase consumer surplus. We show why their belief does not always hold. Similarly, firms are expected to prefer discriminatory pricing. We show that this expectation is also not true under certain circumstances.
Sparsity Preserving Discretization With Error Bounds

Abstract Typically when designing distributed controllers it is assumed that the state-space model of the plant consists of sparse matrices. However, in the discrete-time setting, if one begins with a continuous-time model, the discretization process annihilates any sparsity in the model. In this work we propose a discretization procedure that maintains the sparsity of the continuous-time model. We show that this discretization out-performs a simple truncation method in terms of its ability to approximate the “ground truth” model. Leveraging results from numerical analysis we are also be able to upper-bound the error between the dense discretization and our method. Furthermore, we show that in a robust control setting we can design a distributed controller on the approximate (sparse) model that stabilizes the dense model.
SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching

We present a new method SOLOIST that uses transfer learning and machine teaching to build task bots at scale. We parameterize classical modular task-oriented dialog systems using a Transformer-based auto-regressive language model, which subsumes different dialog modules into a single neural model. We pre-train, on heterogeneous dialog corpora, a task-grounded response generation model, which can generate dialog responses grounded in user goals and real-world knowledge for task completion. The pre-trained model can be efficiently adapted to accomplish new tasks with a handful of task-specific dialogs via machine teaching, where training samples are generated by human teachers interacting with the system. Experiments show that (i) SOLOIST creates new state-of-the-art on well-studied task-oriented dialog benchmarks, including CamRest676 and MultiWOZ; (ii) in the few-shot fine-tuning settings, SOLOIST significantly outperforms existing methods, and (iii) the use of machine teaching substantially reduces the labeling cost of fine-tuning. The pre-trained models and codes are available at https://aka.ms/soloist .
A repo model of fire sales with VWAP and LOB pricing mechanisms

Abstract We consider a network of banks that optimally choose a strategy of asset liquidations and borrowing in order to cover short term obligations. The borrowing is done in the form of collateralized repurchase agreements, the haircut level of which depends on the total liquidations of all the banks. Similarly the fire-sale price of the asset obtained by each of the banks depends on the amount of assets liquidated by the bank itself and by other banks. By nature of this setup, banks’ behavior is considered as a Nash equilibrium. This paper provides two forms for market clearing to occur: through a common closing price and through an application of the limit order book. The main results of this work are providing the existence of maximal and minimal clearing solutions (i.e., liquidations, borrowing, fire sale prices, and haircut levels) as well as sufficient conditions for uniqueness of the clearing solutions.
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition

We propose a novel decentralized feature extraction approach in federated learning to address privacy-preservation issues for speech recognition. It is built upon a quantum convolutional neural network (QCNN) composed of a quantum circuit encoder for feature extraction, and a recurrent neural network (RNN) based end-to-end acoustic model (AM). To enhance model parameter protection in a decentralized architecture, an input speech is first up-streamed to a quantum computing server to extract Mel-spectrogram, and the corresponding convolutional features are encoded using a quantum circuit algorithm with random parameters. The encoded features are then down-streamed to the local RNN model for the final recognition. The proposed decentralized framework takes advantage of the quantum learning progress to secure models and to avoid privacy leakage attacks. Testing on the Google Speech Commands Dataset, the proposed QCNN encoder attains a competitive accuracy of 95.12% in a decentralized model, which is better than the previous architectures using centralized RNN models with convolutional features. We conduct an in-depth study of different quantum circuit encoder architectures to provide insights into designing QCNN-based feature extractors. Neural saliency analyses demonstrate a high correlation between the proposed QCNN features, class activation maps, and the input Mel-spectrogram. We provide an implementation1 for future studies.
Sparsification via Compressed Sensing for Automatic Speech Recognition

In order to achieve high accuracy for machine learning (ML) applications, it is essential to employ models with a large number of parameters. Certain applications, such as Automatic Speech Recognition (ASR), however, require real-time interactions with users, hence compelling the model to have as low latency as possible. Deploying large scale ML applications thus necessitates model quantization and compression, especially when running ML models on resource constrained devices. For example, by forcing some of the model weight values into zero, it is possible to apply zero-weight compression, which reduces both the model size and model reading time from the memory. In the literature, such methods are referred to as sparse pruning. The fundamental questions are when and which weights should be forced to zero, i.e. be pruned. In this work, we propose a compressed sensing based pruning (CSP) approach to effectively address those questions. By reformulating sparse pruning as a sparsity inducing and compression-error reduction dual problem, we introduce the classic compressed sensing process into the ML model training process. Using ASR task as an example, we show that CSP consistently outperforms existing approaches in the literature.
Effects of Axial Load on the Location of a Combined Null Point in Energy Piles

Soil structure interaction in energy piles has not yet been understood comprehensively. One of the important underlying issues is the location of a zero-displacement point, known as the null point. This study investigates how the location of a combined null point in fully floating energy piles is affected by relative magnitudes of thermal and mechanical loads. Analytical solutions are used to address four different loading scenarios including compressive and tensile loads, and heating and cooling. It was found that the location of the combined null point coincides with the location of the maximum magnitude of the axial stress induced by thermo-mechanical load. Furthermore, while a thermal null point is always present in fully floating energy piles, the combined null point is absent in the case of a small magnitude of thermal load as compared to the mechanical load. With increase in the relative magnitude of thermal load as compared to the mechanical load the combined null point emerges at different locations for different load combinations. In all cases it subsequently moves towards the mid-length of the pile with increase in the relative magnitude of thermal load.
Flavin-dependent halogenases catalyze enantioselective olefin halocyclization.

Halocyclization of alkenes is a powerful bond-forming tool in synthetic organic chemistry and a key step in natural product biosynthesis, but catalyzing halocyclization with high enantioselectivity remains a challenging task. Identifying suitable enzymes that catalyze enantioselective halocyclization of simple olefins would therefore have significant synthetic value. Flavin-dependent halogenases (FDHs) catalyze halogenation of arene and enol(ate) substrates. Herein, we reveal that FDHs engineered to catalyze site-selective aromatic halogenation also catalyze non-native bromolactonization of olefins with high enantioselectivity and near-native catalytic proficiency. Highly selective halocyclization is achieved by characterizing and mitigating the release of HOBr from the FDH active site using a combination of reaction optimization and protein engineering. The structural origins of improvements imparted by mutations responsible for the emergence of halocyclase activity are discussed. This expansion of FDH catalytic activity presages the development of a wide range of biocatalytic halogenation reactions. Catalytic enantioselective halocyclization of alkenes is an important bond forming tool and a key step in natural product biosynthesis, but so far no examples of the enzymatic counterpart of this reaction on simple achiral olefins have been reported. Here, the authors describe examples of engineered flavin-dependent halogenases that catalyze halolactonization of olefins with high enantioselectivity and near-native catalytic activity.
The new era of unconditional convergence

Abstract The central fact that has motivated the empirics of economic growth—namely unconditional divergence—is no longer true and has not been so for decades. Across a range of data sources, poorer countries have in fact been catching up with richer ones, albeit slowly, since the mid-1990s. This new era of convergence does not stem primarily from growth moderation in the rich world but rather from accelerating growth in the developing world, which has simultaneously become remarkably less volatile and more persistent. Debates about a “middle-income trap” also appear anachronistic: middle-income countries have exhibited higher growth rates than all others since the mid-1980s.
Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages

Unsupervised translation has reached impressive performance on resource-rich language pairs such as English-French and English-German. However, early studies have shown that in more realistic settings involving low-resource, rare languages, unsupervised translation performs poorly, achieving less than 3.0 BLEU. In this work, we show that multilinguality is critical to making unsupervised systems practical for low-resource settings. In particular, we present a single model for 5 low-resource languages (Gujarati, Kazakh, Nepali, Sinhala, and Turkish) to and from English directions, which leverages monolingual and auxiliary parallel data from other high-resource language pairs via a three-stage training scheme. We outperform all current state-of-the-art unsupervised baselines for these languages, achieving gains of up to 14.4 BLEU. Additionally, we outperform strong supervised baselines for various language pairs as well as match the performance of the current state-of-the-art supervised model for Nepali-English. We conduct a series of ablation studies to establish the robustness of our model under different degrees of data quality, as well as to analyze the factors which led to the superior performance of the proposed approach over traditional unsupervised models.
Prospective correlation between the patient microbiome with response to and development of immune-mediated adverse effects to immunotherapy in lung cancer.

e21024Background: Though the gut microbiome has been associated with immunotherapy (ICI) efficacy in certain cancers, similar correlations between microbiomes at other body sites with treatment res...
Instance-Optimal Compressed Sensing via Posterior Sampling

We characterize the measurement complexity of compressed sensing of signals drawn from a known prior distribution, even when the support of the prior is the entire space (rather than, say, sparse vectors). We show for Gaussian measurements and \emph{any} prior distribution on the signal, that the posterior sampling estimator achieves near-optimal recovery guarantees. Moreover, this result is robust to model mismatch, as long as the distribution estimate (e.g., from an invertible generative model) is close to the true distribution in Wasserstein distance. We implement the posterior sampling estimator for deep generative priors using Langevin dynamics, and empirically find that it produces accurate estimates with more diversity than MAP.
On the numerical accuracy in finite-volume methods to accurately capture turbulence in compressible flows

The goal of the present paper is to understand the impact of numerical schemes for the reconstruction of data at cell faces in finite-volume methods, and to assess their interaction with the quadrature rule used to compute the average over the cell volume. Here, third-, fifth- and seventh-order WENO-Z schemes are investigated. On a problem with a smooth solution, the theoretical order of convergence rate for each method is retrieved, and changing the order of the reconstruction at cell faces does not impact the results, whereas for a shock-driven problem all the methods collapse to first-order. Study of the decay of compressible homogeneous isotropic turbulence reveals that using a high-order quadrature rule to compute the average over a finite volume cell does not improve the spectral accuracy and that all methods present a second-order convergence rate. However the choice of the numerical method to reconstruct data at cell faces is found to be critical to correctly capture turbulent spectra. In the context of simulations with finite-volume methods of practical flows encountered in engineering applications, it becomes apparent that an efficient strategy is to perform the average integration with a low-order quadrature rule on a fine mesh resolution, whereas high-order schemes should be used to reconstruct data at cell faces.
Dynamic loading of spur gears with linear or parabolic tooth profile modifications

Abstract A computer simulation was conducted to investigate the effects of both linear and parabolic tooth profile modification on the dynamic response of low-contact-ratio spur gears. The effect of the total amount of modification and the length of the modification zone were studied at various loads and speeds to find the optimal profile modification for minimal dynamic loading. Design charts consisting of normalized maximum dynamic load curves were generated for gear systems operated at various loads and with different tooth profile modification. An optimum profile modification can be determined from these design charts to minimize the dynamic loads of spur gear systems.
Linear-Time Variational Integrators in Maximal Coordinates

Most dynamic simulation tools parameterize the configuration of multi-body robotic systems using minimal coordinates, also called generalized or joint coordinates. However, maximal-coordinate approaches have several advantages over minimal-coordinate parameterizations, including native handling of closed kinematic loops and nonholonomic constraints. This paper describes a linear-time variational integrator that is formulated in maximal coordinates. Due to its variational formulation, the algorithm does not suffer from constraint drift and has favorable energy and momentum conservation properties. A sparse matrix factorization technique allows the dynamics of a loop-free articulated mechanism with n links to be computed in O(n) (linear) time. Additional constraints that introduce loops can also be handled by the algorithm without incurring much computational overhead. Experimental results show that our approach offers speed competitive with state-of-the-art minimal-coordinate algorithms while outperforming them in several scenarios, especially when dealing with closed loops and configuration singularities.
Association of circulating fibroblast growth factor-2 with progression of HIV-chronic kidney diseases in children

Children living with HIV frequently show high plasma levels of fibroblast growth factor-2 (FGF-2/bFGF). FGF-2 accelerates the progression of several experimental kidney diseases; however, the role of circulating FGF-2 in childhood HIV-chronic kidney diseases (HIV-CKDs) is unknown. We carried out this study to determine whether high plasma FGF-2 levels were associated with the development of HIV-CKDs in children. The plasma and urine FGF-2 levels were measured in 84 children (< 12 years of age) living with HIV during the pre-modern antiretroviral era, and followed for at least 3 years to determine the prevalence of proteinuria and HIV-CKDs. We also assessed the distribution of the kidney FGF-2 binding sites by autoradiography and Alcian blue staining, and explored potential mechanisms by which circulating FGF-2 may precipitate HIV-CKDs in cultured kidney epithelial and mononuclear cells derived from children with HIV-CKDs. High plasma FGF-2 levels were associated with a high viral load. Thirteen children (~ 15%) developed HIV-CKDs and showed a large reservoir of FGF-2 low-affinity binding sites in the kidney, which can facilitate the recruitment of circulating FGF-2. Children with high plasma and urine FGF-2 levels had 73-fold increased odds (95% CI 9–791) of having HIV-CKDs relative to those with normal FGF-2 values. FGF-2 induced the proliferation and decreased the expression of APOL-1 mRNA in podocytes, and increased the attachment and survival of infected mononuclear cells cultured from children with HIV-CKDs. High plasma FGF-2 levels appear to be an additional risk factor for developing progressive childhood HIV-CKDs.
Cross coupling in pilot-vehicle systems

Multiloop pilot/vehicle analysis is applied to the problem of determining crossfeed techniques which may be employed by pilots in minimizing the effects of vehicle cross coupling. As used here, cross coupling refers to unwanted vehicle motion which occurs in one control axis or loop as the result of pilot control actuation in another control axis or loop. The minimization or elimination of such cross coupling can contribute significantly to the 'workload' associated with tasks like nap-of-the-earth helicopter flight. In contrast, situations arise in which pilot's may use vehicle cross coupling to improve performance by coordinating two control actuations in the control of a single response variable. A crossfeed model is developed based upon simple control system design principles and configured in a manner amenable to pilot pursuit or precognitive control activity. A handling qualities theory developed to analyze single loop tasks is applied to the multiloop problem. The crossfeed model is applied to five different vehicles/configurations ranging from helicopters to fighter aircraft. Results indicate relatively simple crossfeed commands can significantly reduce cross coupling and, in some improve handling qualities as predicted by the single loop theory.
Instance Specific Approximations for Submodular Maximization

For many optimization problems in machine learning, finding an optimal solution is computationally intractable and we seek algorithms that perform well in practice. Since computational intractability often results from pathological instances, we look for methods to benchmark the performance of algorithms against optimal solutions on real-world instances. The main challenge is that an optimal solution cannot be efficiently computed for intractable problems, and we therefore often do not know how far a solution is from being optimal. A major question is therefore how to measure the performance of an algorithm in comparison to an optimal solution on instances we encounter in practice. In this paper, we address this question in the context of submodular optimization problems. For the canonical problem of submodular maximization under a cardinality constraint, it is intractable to compute a solution that is better than a $1-1/e \approx 0.63$ fraction of the optimum. Algorithms like the celebrated greedy algorithm are guaranteed to achieve this $1-1/e$ bound on any instance and are used in practice. Our main contribution is not a new algorithm for submodular maximization but an analytical method that measures how close an algorithm for submodular maximization is to optimal on a given problem instance. We use this method to show that on a wide variety of real-world datasets and objectives, the approximation of the solution found by greedy goes well beyond $1-1/e$ and is often at least 0.95. We develop this method using a novel technique that lower bounds the objective of a dual minimization problem to obtain an upper bound on the value of an optimal solution to the primal maximization problem.
Solutions of Mathisson-Papapetrou Equations for Highly Relativistic Spinning Particles

Different types of essentially nongeodesic motions of highly relativistic spinning particles in Schwarzschild’s and Kerr’s background which follows from the Mathisson-Papapetrou (MP) equations are considered. It is shown that dependently on the correlation of signs of the spin and the particle’s orbital velocity the spin-gravity coupling acts as a significant repulsive or attractive force. Numerical estimates for electrons, protons, and neutrinos in the gravitational field of black holes are presented. The correspondence between the general relativistic Dirac equation and MP equations is discussed. It is stressed that for the highly relativistic motions the adequate supplementary condition for the MP equations is the Mathisson-Pirani condition. In the following it is important to study the possible role of the highly relativistic spin-gravity coupling in astrophysics, cosmology, and high energy physics.
Adversarial Regression Learning for Bone Age Estimation

Estimation of bone age from hand radiographs is essential to determine skeletal age in diagnosing endocrine disorders and depicting the growth status of children. However, existing automatic methods only apply their models to test images without considering the discrepancy between training samples and test samples, which will lead to a lower generalization ability. In this paper, we propose an adversarial regression learning network (ARLNet) for bone age estimation. Specifically, we first extract bone features from a fine-tuned Inception V3 neural network and propose regression percentage loss for training. To reduce the discrepancy between training and test data, we then propose adversarial regression loss and feature reconstruction loss to guarantee the transition from training data to test data and vice versa, preserving invariant features from both training and test data. Experimental results show that the proposed model outperforms state-of-the-art methods.
Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations

Grasp detection in clutter requires the robot to reason about the 3D scene from incomplete and noisy perception. In this work, we draw insight that 3D reconstruction and grasp learning are two intimately connected tasks, both of which require a fine-grained understanding of local geometry details. We thus propose to utilize the synergies between grasp affordance and 3D reconstruction through multi-task learning of a shared representation. Our model takes advantage of deep implicit functions, a continuous and memory-efficient representation, to enable differentiable training of both tasks. We train the model on self-supervised grasp trials data in simulation. Evaluation is conducted on a clutter removal task, where the robot clears cluttered objects by grasping them one at a time. The experimental results in simulation and on the real robot have demonstrated that the use of implicit neural representations and joint learning of grasp affordance and 3D reconstruction have led to state-of-the-art grasping results. Our method outperforms baselines by over 10% in terms of grasp success rate.
Learning unknown physics of non-Newtonian fluids

Non-Newtonian fluids have a shear-rate dependent viscosity that is difficult to measure in experiments. We present a physics-informed neural networks (PINN) approach for learning the viscosity using indirect measurements (such as velocity and pressure) subject to the momentum conservation and continuity equations constraints. We use the PINN approach to estimate viscosity of polymer melts and suspensions of particles using velocity measurements from two-dimensional shear flow simulations. The PINN-inferred viscosity models agree with empirical models for shear rates with large absolute values but deviate for shear rates near zero where the empirical models have an unphysical singularity.
Three Dimensional Mirror Symmetry beyond $ADE$ quivers and Argyres-Douglas theories

Mirror symmetry, a three dimensional $$ \mathcal{N} $$ = 4 IR duality, has been studied in detail for quiver gauge theories of the ADE-type (as well as their affine versions) with unitary gauge groups. The A-type quivers (also known as linear quivers) and the associated mirror dualities have a particularly simple realization in terms of a Type IIB system of D3-D5-NS5-branes. In this paper, we present a systematic field theory prescription for constructing 3d mirror pairs beyond the ADE quiver gauge theories, starting from a dual pair of A-type quivers with unitary gauge groups. The construction involves a certain generalization of the S and the T operations, which arise in the context of the SL(2, ℤ) action on a 3d CFT with a U(1) 0-form global symmetry. We implement this construction in terms of two supersymmetric observables — the round sphere partition function and the superconformal index on S2 × S1. We discuss explicit examples of various (non-ADE) infinite families of mirror pairs that can be obtained in this fashion. In addition, we use the above construction to conjecture explicit 3d $$ \mathcal{N} $$ = 4 Lagrangians for 3d SCFTs, which arise in the deep IR limit of certain Argyres-Douglas theories compactified on a circle.
Near-Optimal Schedules for Simultaneous Multicasts

We study the store-and-forward packet routing problem for simultaneous multicasts, in which multiple packets have to be forwarded along given trees as fast as possible. This is a natural generalization of the seminal work of Leighton, Maggs and Rao, which solved this problem for unicasts, i.e. the case where all trees are paths. They showed the existence of asymptotically optimal O(C + D)-length schedules, where the congestion C is the maximum number of packets sent over an edge and the dilation D is the maximum depth of a tree. This improves over the trivial O(CD) length schedules. We prove a lower bound for multicasts, which shows that there do not always exist schedules of non-trivial length, o(CD). On the positive side, we construct O(C+D+log² n)-length schedules in any n-node network. These schedules are near-optimal, since our lower bound shows that this length cannot be improved to O(C+D) + o(log n).
A Kinematic Model for Trajectory Prediction in General Highway Scenarios

Highway driving invariably combines high speeds with the need to interact closely with other drivers. Prediction methods enable autonomous vehicles (AVs) to anticipate drivers’ future trajectories and plan accordingly. Kinematic methods for prediction have traditionally ignored the presence of other drivers, or made predictions only for a limited set of scenarios. Data-driven approaches fill this gap by learning from large datasets to predict trajectories in general scenarios. While they achieve high accuracy, they also lose the interpretability and tools for model validation enjoyed by kinematic methods. This letter proposes a novel kinematic model to describe car-following and lane change behavior, and extends it to predict trajectories in general scenarios. Experiments on highway datasets under varied sensing conditions demonstrate that the proposed method outperforms state-of-the-art methods.