cssi/SciSciGPT-SciSciNet huggingface dataset:
Power System Event Identification Based on Deep Neural Network With Information Loading

Online power system event identification and classification are crucial to enhancing the reliability of transmission systems. In this paper, we develop a deep neural network (DNN) based approach to identify and classify power system events by leveraging real-world measurements from hundreds of phasor measurement units (PMUs) and labels from thousands of events. Two innovative designs are embedded into the baseline model built on convolutional neural networks (CNNs) to improve the event classification accuracy. First, we propose a graph signal processing based PMU sorting algorithm to improve the learning efficiency of CNNs. Second, we deploy information loading based regularization to strike the right balance between memorization and generalization for the DNN. Numerical results based on real-world dataset from the Eastern Interconnection of the U.S power transmission grid show that the combination of PMU based sorting and the information loading based regularization techniques help the proposed DNN approach achieve highly accurate event identification and classification results.
The effects of disclosure and enforcement on payday lending in Texas

Abstract In 2012, Texas and two municipalities therein adopted regulations governing the payday loan market. Austin and Dallas enacted supply restrictions limiting the loan-to-income ratio and mandating amortization. The state adopted an information disclosure inspired by Bertrand and Morse (2011) presenting the cost and typical usage of payday loans in easy-to-understand terms. We find that the municipal restrictions led to a 61% decline in loan volume in Austin and a 44% decline in Dallas, with the effects driven by the start of enforcement. The statewide disclosures led to a persistent 12% decline in loan volume in the first six months.
Death on the job: The Great Recession and work-related traffic fatalities.

Abstract In light of recent discussions about shifting employees from traditional workplaces to virtual employment, we are motivated by the question of whether this phenomenon will end up saving lives even in the absence of an infectious disease outbreak. Motor vehicle incidents are the leading cause of work-related fatalities in the US, killing more than 1200 workers each year, which make up about a quarter of all work-related deaths. Not only are motor vehicle crashes the top killer at work, but economic expansions can further increase occupational and traffic deaths as they both tend to be procyclical. In this paper, we examine the effects of business cycles on traffic fatalities in the US with a special focus on work-related deaths. Specifically, we implement a longitudinal design across all 50 states by compiling quarterly data for 2004–2012 and consider macroeconomic fluctuations around the Great Recession. Our findings show that traffic deaths during prosperous times are not solely due to an increase in risky behaviors such as drunk driving, but directly related to work. Given the highly preventable nature of traffic crashes, policy makers, public health advocates, and employers can develop effective strategies, including remote work arrangements, to improve both occupational and traffic safety.
THE "MEMORY EFFECT" OF ECONOMIC SANCTIONS AGAINST RUSSIA: OPPOSING APPROACHES TO THE LEGALITY OF UNILATERAL SANCTIONS CLASH AGAIN

The “sanctions war” between the West and Russia, the worst crisis since the end of the Cold War, again raises the issue of whether the imposition of economic sanctions without authorization of the U.N. Security Council is permissible under international law. In this Article, I analyze the legal justifications and contemporary sanctions practice of the United States, and the evolution of Russia’s arguments against the use of those economic sanctions. On the basis of a brief case study analysis of episodes related to the imposition of the tacit trade embargo upon Russia’s neighboring countries, I argue that while on political and diplomatic levels the United States and Russia demonstrate opposite views towards the legality of unilateral economic sanctions, today, at its very core, the actual activity of both states tends to make unilateral sanctions more recognizable as a part of international customary law.
Volume and symplectic structure for ℓ-adic local systems

Abstract We introduce a notion of volume for an l-adic local system over an algebraic curve and, under some conditions, give a symplectic form on the rigid analytic deformation space of the corresponding geometric local system. These constructions can be viewed as arithmetic analogues of the volume and the Chern-Simons invariants of a representation of the fundamental group of a 3-manifold which fibers over the circle and of the symplectic form on the character varieties of a Riemann surface. We show that the absolute Galois group acts on the deformation space by conformal symplectomorphisms which extend to an l-adic analytic flow. We also prove that the locus of local systems which are arithmetic over a cyclotomic extension is the critical set of a collection of rigid analytic functions. The vanishing cycles of these functions give additional invariants.
Spectral statistics in constrained many-body quantum chaotic systems

The authors study the spectral form factor for Floquet random quantum circuits and analytically identify the onset of quantum chaos in many-body systems subject to local constraints, showing that systems with conserved higher moments display subdiffusive dynamics.
Frozen 1-RSB structure of the symmetric Ising perceptron

We prove, under an assumption on the critical points of a real-valued function, that the symmetric Ising perceptron exhibits the `frozen 1-RSB' structure conjectured by Krauth and Mezard in the physics literature; that is, typical solutions of the model lie in clusters of vanishing entropy density. Moreover, we prove this in a very strong form conjectured by Huang, Wong, and Kabashima: a typical solution of the model is isolated with high probability and the Hamming distance to all other solutions is linear in the dimension. The frozen 1-RSB scenario is part of a recent and intriguing explanation of the performance of learning algorithms by Baldassi, Ingrosso, Lucibello, Saglietti, and Zecchina. We prove this structural result by comparing the symmetric Ising perceptron model to a planted model and proving a comparison result between the two models. Our main technical tool towards this comparison is an inductive argument for the concentration of the logarithm of number of solutions in the model.
Monitoring of structural integrity of composite structures by embedded optical fiber sensors

In-service structural health monitoring of engineering structures has assumed a significant role in assessing their safety and integrity. Fiber Bragg grating (FBG) sensors have emerged as a reliable, in-situ, non-destructive tool for monitoring, diagnostics and control in civil and aerospace structures. They can be used to measure a multitude of parameters like strain, temperature, pressure and chemical and biological effects of the environment. The versatility of Fiber Bragg grating sensors represents a key advantage over other technologies in the structural sensing field. In this study, Bragg gratings written into a Germanium doped optical fiber are embedded in carbon-epoxy laminates to measure the tensile strain in the composite material. Strain measurements from a more conventional electro-mechanical instrumentation are compared to those from the Bragg sensors and an excellent agreement between the two measurements has been verified. The experimental results of this study indicate that fiber optic Bragg grating sensors, integrated with composites, have potential applications for monitoring the structural integrity of composite structures.
Computerized multilevel analysis for multilayered fiber composites

Abstract A FORTRAN IV computer code for the micromechanics, macromechanics, and laminate analysis of multilayered fiber composite structural components is described. The code can be used either individually or as a subroutine within a complex structural analysis/synthesis program. The inputs to the code are constituent materials properties, composite geometry, and loading conditions. The outputs are various properties for ply and composite; composite structural response, including bending-stretching coupling; and composite stress analysis, including comparisons with failure criteria for combined stress. The code was used successfully in the analysis and structural synthesis of flat panels, in the buckling analysis of flat panels, in multilayered composite material failure studies, and lamination residual stresses analysis.
A finite-element analysis of fatigue crack closure

Experiments have shown that fatigue cracks close at positive loads during constant-amplitude load cycling. The crack-closure phenomenon is caused by residual plastic deformations remaining in the wake of an advancing crack tip. The present paper is concerned with the application of a two-dimensional, nonlinear, finite-element analysis using an incremental theory of plasticity to predict crack-closure and crack-opening stresses during the crack-growth process under cyclic loading. A two-dimensional finite-element computer program, which accounts for both elastic-plastic material behavior and changing boundary conditions associated with crack extension and intermittent contact of the crack surfaces under cyclic loading, has been developed. An efficient technique to account for changing boundary conditions under cyclic loading was also incorporated into the nonlinear analysis program. This program was used subsequently to study crack extension and crack closure behavior in a center-cracked panel under constant-amplitude and two-level block loading. The calculated crack-opening stresses were found to be quantitatively consistent with experimental measurements.
Control coordination between DFIG-based wind turbines and synchronous generators for optimal primary frequency response

This paper proposes a novel coordinating mechanism between synchronous generators (SGs) and wind turbines (WTs) based on doubly-fed induction generators (DFIGs) for enhanced primary frequency regulation. WTs are urged to participate on frequency regulation, specially if wind power penetration keeps increasing. WTs control support is possible, but it is transient due to the WTs lack of energy storage. This drawback can result in either a further delayed response from the governors of SGs or further frequency decay when WTs support is over. The proposed coordination attempt to tackle this issue. An artificial neural network (ANN) is used to obtain an optimal coordination signal to improve frequency response. As a proof of concept, the proposed coordination is tested on a 9-bus test system that includes a wind farm with 5 WTs. Simulation results show that frequency nadir is reduced in about 22% and rates of change of the system frequency (RoCoF) in about 29.5%. Further work is needed to validate this concept in large-scale systems, but the development and results obtained so far are promising to strengthen power systems.
Polynomial bound and nonlinear smoothing for the Benjamin-Ono equation on the circle

Abstract For initial data in Sobolev spaces H s ( T ) , 1 2 s ⩽ 1 , the solution to the Cauchy problem for the Benjamin-Ono equation on the circle is shown to grow at most polynomially in time at a rate ( 1 + t ) 3 ( s − 1 2 ) + ϵ , 0 ϵ ≪ 1 . The key to establishing this result is the discovery of a nonlinear smoothing effect for the Benjamin-Ono equation, according to which the solution to the equation satisfied by a certain gauge transform, which is widely used in the well-posedness theory of the Cauchy problem, becomes smoother once its free solution is removed.
A novel construction of Urysohn universal ultrametric space via the Gromov-Hausdorff ultrametric

Abstract We establish universality and ultra-homogeneity of ( U , u GH ) , the collection of all compact ultrametric spaces endowed with the so-called Gromov-Hausdorff ultrametric. This result also gives rise to a novel construction of the so-called R-Urysohn universal ultrametric space for each countable subset R ⊂ R ≥ 0 containing 0.
How to Adapt Your Pretrained Multilingual Model to 1600 Languages

Pretrained multilingual models (PMMs) enable zero-shot learning via cross-lingual transfer, performing best for languages seen during pretraining. While methods exist to improve performance for unseen languages, they have almost exclusively been evaluated using amounts of raw text only available for a small fraction of the world’s languages. In this paper, we evaluate the performance of existing methods to adapt PMMs to new languages using a resource available for close to 1600 languages: the New Testament. This is challenging for two reasons: (1) the small corpus size, and (2) the narrow domain. While performance drops for all approaches, we surprisingly still see gains of up to 17.69% accuracy for part-of-speech tagging and 6.29 F1 for NER on average over all languages as compared to XLM-R. Another unexpected finding is that continued pretraining, the simplest approach, performs best. Finally, we perform a case study to disentangle the effects of domain and size and to shed light on the influence of the finetuning source language.
Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions

Language generation models' democratization benefits many domains, from answering health-related questions to enhancing education by providing AI-driven tutoring services. However, language generation models' democratization also makes it easier to generate human-like text at-scale for nefarious activities, from spreading misinformation to targeting specific groups with hate speech. Thus, it is essential to understand how people interact with bots and develop methods to detect bot-generated text. This paper shows that bot-generated text detection methods are more robust across datasets and models if we use information about how people respond to it rather than using the bot's text directly. We also analyze linguistic alignment, providing insight into differences between human-human and human-bot conversations.
Characterizing aeroelastic systems using eigenanalysis, explicitly retaining the aerodynamic degrees of freedom

Discrete time aeroelastic models with explicitly retained aerodynamic modes have been generated employing a time marching vortex lattice aerodynamic model. This paper presents analytical results from eigenanalysis of these models. The potential of these models to calculate the behavior of modes that represent damped system motion (noncritical modes) in addition to the simple harmonic modes is explored. A typical section with only structural freedom in pitch is examined. The eigenvalues are examined and compared to experimental data. Issues regarding the convergence of the solution with regard to refining the aerodynamic discretization are investigated. Eigenvector behavior is examined; the eigenvector associated with a particular eigenvalue can be viewed as the set of modal participation factors for that particular mode. For the present formulation of the equations of motion, the vorticity for each aerodynamic element appears explicitly as an element of each eigenvector in addition to the structural dynamic generalized coordinates. Thus, modal participation of the aerodynamic degrees of freedom can be assessed in M addition to participation of structural degrees of freedom.
Algebraic branch points at all loop orders from positive kinematics and wall crossing

There is a remarkable connection between the boundary structure of the positive kinematic region and branch points of integrated amplitudes in planar $$ \mathcal{N} $$ = 4 SYM. A long-standing question has been precisely how algebraic branch points emerge from this picture. We use wall crossing and scattering diagrams to systematically study the boundary structure of the positive kinematic regions associated with MHV amplitudes. The notion of asymptotic chambers in the scattering diagram naturally explains the appearance of algebraic branch points. Furthermore, the scattering diagram construction also motivates a new coordinate system for kinematic space that rationalizes the relations between algebraic letters in the symbol alphabet. As a direct application, we conjecture a complete list of all algebraic letters that could appear in the symbol alphabet of the 8-point MHV amplitude.
Cooperation and Division: An Empirical Analysis of Voting Similarities and Differences during the Stable Rehnquist Court Era - 1994 to 2005

The Stable Rehnquist Court Era (SRCE) covers the period from the appointment of Justice Breyer to the passing of Chief Justice Rehnquist. There has been only one longer period of stability in the Court’s history, and that was in the early nineteenth century when far fewer cases were decided. Thus the SRCE presents a unique opportunity with a large number of observations to conduct statistical analysis of the Justices’ votes while the composition of the Court is held constant. I present a statistical empirical analysis of voting for this period both for the potentially interesting results that can be learned, and as an example of how to conduct and present an empirical study which is objective and replicable. Some of the findings include: the fact that only a few pairs of Justices have statistically significant differences in voting records; the magnitude of the departure from independent voting is enormous in statistical terms; Justice Thomas is the most predictable Justice, and Justice Scalia is the least-changed Justice. Of particular interest is a finding that is contrary to conventional wisdom. Conventional wisdom suggests that the median Justice closest to the center, presumably Justice Kennedy, is the most influential Justice. However, I develop a measure of influence which employs the statistically significant effects the Justices have on each other and suggests that the most influential Justices on the Court were Rehnquist, Souter, and Breyer.
Minimal synthetic enhancers reveal control of the probability of transcriptional engagement and its timing by a morphogen gradient

How enhancers interpret morphogen gradients to generate spatial patterns of gene expression is a central question in developmental biology. Although recent studies have begun to elucidate that enhancers can dictate whether, when, and at what rate a promoter will engage in transcription, the complexity of endogenous enhancers calls for theoretical models with too many free parameters to quantitatively dissect these regulatory strategies. To overcome this limitation, we established a minimal synthetic enhancer system in embryos of the fruit fly Drosophila melanogaster . Here, a gradient of the Dorsal activator is read by a single Dorsal binding site. By quantifying transcriptional activity using live imaging, our experiments revealed that this single Dorsal binding site is capable of regulating whether promoters engage in transcription in a Dorsal concentration-specific manner. By modulating binding-site affinity, we determined that a gene's decision to engage in transcription and its transcriptional onset time can be explained by a simple theoretical model where the promoter has to traverse multiple kinetic barriers before transcription can ensue. The experimental platform developed here pushes the boundaries of live-imaging in studying gene regulation in the early embryo by enabling the quantification of the transcriptional activity driven by a single transcription factor binding site, and making it possible to build more complex enhancers from the ground up in the context of a dialogue between theory and experiment.
Shaking Things Up: Disruptive Events and Inequality

This paper develops a theory of how disruptive events could reduce racial and gender inequality in organizations. Despite efforts from regulators and advocates, racial and gender inequality in the ...
Debiasing In-Sample Policy Performance for Small-Data, Large-Scale Optimization.

Motivated by the poor performance of cross-validation in settings where data are scarce, we propose a novel estimator of the out-of-sample performance of a policy in data-driven optimization.Our approach exploits the optimization problem's sensitivity analysis to estimate the gradient of the optimal objective value with respect to the amount of noise in the data and uses the estimated gradient to debias the policy's in-sample performance. Unlike cross-validation techniques, our approach avoids sacrificing data for a test set, utilizes all data when training and, hence, is well-suited to settings where data are scarce. We prove bounds on the bias and variance of our estimator for optimization problems with uncertain linear objectives but known, potentially non-convex, feasible regions. For more specialized optimization problems where the feasible region is "weakly-coupled" in a certain sense, we prove stronger results. Specifically, we provide explicit high-probability bounds on the error of our estimator that hold uniformly over a policy class and depends on the problem's dimension and policy class's complexity. Our bounds show that under mild conditions, the error of our estimator vanishes as the dimension of the optimization problem grows, even if the amount of available data remains small and constant. Said differently, we prove our estimator performs well in the small-data, large-scale regime. Finally, we numerically compare our proposed method to state-of-the-art approaches through a case-study on dispatching emergency medical response services using real data. Our method provides more accurate estimates of out-of-sample performance and learns better-performing policies.
When Fantasy Meets the Courtroom: An Examination of the Intellectual Property Issues Surrounding the Burgeoning Fantasy Sports Industry

With an estimated fifteen million people nationwide participating in fantasy sports games, the fantasy sports industry has grown into one of the greatest cultural obsessions in modern-day America. Despite adding excitement to the lives of sports fans, the fantasy sports craze raises a number of complex intellectual property law issues that are in need of examination. This Note provides an in-depth exploration of the legal doctrines of copyright and the right of publicity and how they should be applied in the context of fantasy sports.
Decision-making and strategic planning in a public research organisation: The case of Fiocruz

This study was drawn to investigate an unusual form of "industrial democracy", in Fiocruz, the Brazilian public research organisation in the health field, linked to the Ministry of Health. It is a single case study research making use of two contrasting cases: Embrapa and Pasteur Institute. The former is the Brazilian public research organisation in the agriculture field, similar to Fiocruz in space; and the latter is the French public research organisation for health, similar to Fiocruz in the field. The investigation works with the triangulation of methods approach, combining the analysis of quantitative and qualitative methods aiming at an in-depth analysis of the decision-making and the strategic planning processes of these research-led organisations. For this purpose, two exclusive tools were created and applied to their management boards and key actors. The first tool, an online questionnaire, was applied to the management boards; the second tool, a face-to-face interview, was made with the key actors. The findings were examined considering the literature, whose results supported the analysis of the context of each organisation and their strategic plans. This "second triangulation" revealed an original approach to industrial democracy by Fiocruz, based on a concept postulated by Arouca, which underpinned the creation of SUS, the Brazilian National Unified Health System: "Democracy is health". It represents a paradigm shift in the way the health concept is addressed, by adding a political dimension to it, which includes the citizenship, the free self-determination, the capacity of mastering the future. This unique approach to industrial democracy underpins the decision-making and the strategic planning of Fiocruz, revealing that it is, in fact, a new type of public research organisation; thus, questioning the validity of the existing framework concerning such entities.
New Impossibility Results for Concurrent Composition and a Non-Interactive Completeness Theorem for Secure Computation.

We consider the client-server setting for the concurrent composition of secure protocols: in this setting, a single server interacts with multiple clients concurrently, executing with each client a specified protocol where only the client should receive any nontrivial output. Such a setting is easily motivated from an application standpoint. There are important special cases for which positive results are known – such as concurrent zero knowledge protocols – and it has been an open question explicitly asked, for instance, by Lindell [J. Cryptology’08] – whether other natural functionalities such as Oblivious Transfer (OT) are possible in this setting. In this work: • We resolve this open question by showing that unfortunately, even in this very limited concurrency setting, broad new impossibility results hold, ruling out not only OT, but in fact all nontrivial asymmetric functionalities. Our new negative results hold even if the inputs of all honest parties are fixed in advance, and the adversary receives no auxiliary information. • Along the way, we establish a new unconditional completeness result for asymmetric functionalities, where we characterize functionalities that are non-interactively complete secure against active adversaries. When we say that a functionality F is non-interactively complete, we mean that every other asymmetric functionality can be realized by parallel invocations of several copies of F , with no other communication in any direction. Our result subsumes a completeness result of Kilian [STOC’00] that uses protocols which require additional interaction in both directions. ∗UCLA. Email: shweta@cs.ucla.edu. Research supported in part from a DARPA/ONR PROCEED award, NSF grants 1136174, 1118096, 1065276, 0916574 and 0830803, a Xerox Faculty Research Award, a Google Faculty Research Award, an equipment grant from Intel, and an Okawa Foundation Research Grant. This material is based upon work supported by the Defense Advanced Research Projects Agency through the U.S. Office of Naval Research under Contract N00014-11-1-0389. The views expressed are those of the author and do not reflect the official policy or position of theDepartment of Defense or the U.S. Government. †MSR, India. Email: vipul@microsoft.com ‡UCLA. Email: abhishek@cs.ucla.edu §UIUC. Email: mmp@uiuc.edu Supported by NSF grant CNS 07-47027. ¶UCLA. Email: sahai@cs.ucla.edu. Research supported in part from a DARPA/ONR PROCEED award, NSF grants 1136174, 1118096, 1065276, 0916574 and 0830803, a Xerox Faculty Research Award, a Google Faculty Research Award, an equipment grant from Intel, and an Okawa Foundation Research Grant. This material is based upon work supported by the Defense Advanced Research Projects Agency through the U.S. Office of Naval Research under Contract N00014-11-1-0389. The views expressed are those of the author and do not reflect the official policy or position of theDepartment of Defense or the U.S. Government.
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data. However, NN predictions are only reliable within well-learned training domains, and show volatile behavior when extrapolating. Uncertainty quantification methods can flag atomic configurations for which prediction confidence is low, but arriving at such uncertain regions requires expensive sampling of the NN phase space, often using atomistic simulations. Here, we exploit automatic differentiation to drive atomistic systems towards high-likelihood, high-uncertainty configurations without the need for molecular dynamics simulations. By performing adversarial attacks on an uncertainty metric, informative geometries that expand the training domain of NNs are sampled. When combined with an active learning loop, this approach bootstraps and improves NN potentials while decreasing the number of calls to the ground truth method. This efficiency is demonstrated on sampling of kinetic barriers, collective variables in molecules, and supramolecular chemistry in zeolite-molecule interactions, and can be extended to any NN potential architecture and materials system.
Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learning

Deep neural networks are frequently used by autonomous systems for their ability to learn complex, non-linear data patterns and make accurate predictions in dynamic environments. However, their use as black boxes introduces risks as the confidence in each prediction is unknown. Different frameworks have been proposed to compute accurate confidence measures along with the predictions but at the same time introduce a number of limitations like execution time overhead or inability to be used with high-dimensional data. In this paper, we use the Inductive Venn Predictors framework for computing probability intervals regarding the correctness of each prediction in real-time. We propose taxonomies based on distance metric learning to compute informative probability intervals in applications involving high-dimensional inputs. Empirical evaluation on image classification and botnet attacks detection in Internet-of-Things (IoT) applications demonstrates improved accuracy and calibration. The proposed method is computationally efficient, and therefore, can be used in real-time.
Robust and Secure Cache-aided Private Linear Function Retrieval from Coded Servers

This paper investigates the ultimate performance limits of Linear Function Retrieval (LFR) by cache-aided users from distributed coded servers. Each user aims to retrieve a linear function of the files of a library, which are Maximum Distance Separable (MDS) coded and stored at multiple servers. The system needs to guarantee robust decoding in the sense that each user must decode its demanded function with signals from any subset of servers whose cardinality exceeds a threshold. In addition, the following conditions must be met: (a) the content of the library must be kept secure from a wiretapper who obtains all the signals sent by the servers; (b) any subset of users together can not obtain any information about the demands of the remaining users; and (c) the users' demands must be kept private against all the servers even if they collude. A scheme that uses the superposition of security and privacy keys is proposed to meet all those conditions. The achieved load-memory tradeoff is the same as that achieved in single-server case scaled by the inverse of the MDS code rate used to encode the files, and the same optimality guarantees as in single-server setup are obtained.
High-resolution Spatio-temporal Model for County-level COVID-19 Activity in the U.S

We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases 1 week ahead of the current time, at the county level and weekly aggregat...
Adaptive Compliance Shaping with Human Impedance Estimation.

Human impedance parameters play an integral role in the dynamics of strength amplification exoskeletons. Many methods are used to estimate the stiffness of human muscles, but few are used to improve the performance of strength amplification controllers for these devices. We propose a compliance shaping amplification controller incorporating an accurate online human stiffness estimation from surface electromyography (sEMG) sensors and stretch sensors connected to the forearm and upper arm of the human. These sensor values along with exoskeleton position and velocity are used to train a random forest regression model that accurately predicts a person's stiffness despite varying movement, relaxation, and muscle co-contraction. Our model's accuracy is verified using experimental test data and the model is implemented into the compliance shaping controller. Ultimately we show that the online estimation of stiffness can improve the bandwidth and amplification of the controller while remaining robustly stable.
Drivers of Global Trade: A Product-Level Investigation

This paper investigates the drivers of global trade at the six-digit product level. The identification is achieved first by estimating the log-linear product-level bilateral trade implications of a...
The Maximal Eigengap Estimator for Acoustic Vector-Sensor Processing

This paper introduces the maximal eigengap estimator for finding the direction of arrival of a wideband acoustic signal using a single vector-sensor. We show that in this setting narrowband cross-spectral density matrices can be combined in an optimal weighting that approximately maximizes signal-to-noise ratio across a wide frequency band. The signal subspace resulting from this optimal combination of narrowband power matrices defines the maximal eigengap estimator. We discuss the advantages of the maximal eigengap estimator over competing methods, and demonstrate its utility in a real-data application using signals collected in 2019 from an acoustic vector-sensor deployed in the Monterey Bay.
Many Frobenius complements have even order

The theory of Frobenius groups with Frobenius complements of even order largely reduces to tractable algebraic number theory. If we consider only Frobenius complements with an upper bound s on the ...
Soft Robot Optimal Control Via Reduced Order Finite Element Models

Finite element methods have been successfully used to develop physics-based models of soft robots that capture the nonlinear dynamic behavior induced by continuous deformation. These high-fidelity models are therefore ideal for designing controllers for complex dynamic tasks such as trajectory optimization and trajectory tracking. However, finite element models are also typically very high-dimensional, which makes real-time control challenging. In this work we propose an approach for finite element model-based control of soft robots that leverages model order reduction techniques to significantly increase computational efficiency. In particular, a constrained optimal control problem is formulated based on a nonlinear reduced order finite element model and is solved via sequential convex programming. This approach is demonstrated through simulation of a cable-driven soft robot for a constrained trajectory tracking task, where a 9768-dimensional finite element model is used for controller design.
Strategy-indifferent games of best choice

Abstract The game of best choice (or “secretary problem”) is a model for making an irrevocable decision among a fixed number of candidate choices that are presented sequentially in random order, one at a time. Because the classically optimal solution is known to reject an initial sequence of candidates, a paradox emerges from the fact that candidates have an incentive to position themselves immediately after this cutoff which challenges the assumption that candidates arrive in uniformly random order. One way to resolve this is to consider games for which every (reasonable) strategy results in the same probability of success. In this work, we classify these “strategy-indifferent” games of best choice. It turns out that the probability of winning such a game is essentially the reciprocal of the expected number of left-to-right maxima in the full collection of candidate rank orderings. We present some examples of these games based on avoiding permutation patterns of size 3, which involves computing the distribution of left-to-right maxima in each of these pattern classes.
Revealing the Boundary Weyl Physics of the Four-Dimensional Hall Effect via Phason Engineering in Metamaterials

Quantum Hall physics has been theoretically predicted in four dimensions and higher. In hypothetical 2n dimensions, the topological characters of both the bulk and the boundary are manifested as quantized nonlinear transport coefficients that respectively connect to the $n\mathrm{th}$ Chern number of the bulk gap projection and to the $n\mathrm{th}$ winding number of the Weyl spectral singularities on the ($2n\ensuremath{-}1$)-dimensional boundaries. Here, we introduce the concept of phason engineering in metamaterials and use it as a vehicle to access and apply the quantum Hall physics in arbitrary dimensions. Using these specialized design principles, we fabricate a reconfigurable two-dimensional aperiodic acoustic crystal with a phason living on a 2-torus, giving us access to the four-dimensional quantum Hall physics. Also, we supply a direct experimental confirmation that the topological boundary spectrum assembles in a Weyl singularity when mapped as a function of the quasimomenta. We also demonstrate topological wave steering enabled by the Weyl physics of the three-dimensional boundaries.
Multi-agent Aerial Monitoring of Moving Convoys using Elliptical Orbits

We propose a novel scheme for surveillance of a dynamic ground convoy moving along a non-linear trajectory, by aerial agents that maintain a uniformly spaced formation on a time-varying elliptical orbit encompassing the convoy. Elliptical orbits are used as they are more economical than circular orbits for circumnavigating the group of targets in the moving convoy. The proposed scheme includes an algorithm for computing feasible elliptical orbits, a vector guidance law for agent motion along the desired orbit, and a cooperative strategy to control the speeds of the aerial agents in order to quickly achieve and maintain the desired formation. It achieves mission objectives while accounting for linear and angular speed constraints on the aerial agents. The scheme is validated through simulations and actual experiments with a convoy of ground robots and a team of quadrotors as the aerial agents, in a motion capture environment.
Locally Roelcke precompact Polish groups

A Polish group is said to be locally Roelcke precompact if there is a neighborhood of the identity element that is totally bounded in the Roelcke (or lower) group uniformity. These form a subclass of the locally bounded groups, while generalizing the Roelcke precompact and locally compact Polish groups. We characterize these groups in terms of their geometric structure as those locally bounded groups whose coarsely bounded sets are all Roelcke precompact, and in terms of their uniform structure as those groups whose completions in the Roelcke uniformity are locally compact. We also assess the conditions under which this locally compact space carries the structure of a semi-topological semigroup.
The Discrepancy of Random Rectangular Matrices

A recent approach to the Beck-Fiala conjecture, a fundamental problem in combinatorics, has been to understand when random integer matrices have constant discrepancy. We give a complete answer to this question for two natural models: matrices with Bernoulli or Poisson entries. For Poisson matrices, we further characterize the discrepancy for any rectangular aspect ratio. These results give sharp answers to questions of Hoberg and Rothvoss (SODA 2019) and Franks and Saks (Random Structures Algorithms 2020). Our main tool is a conditional second moment method combined with Stein's method of exchangeable pairs. While previous approaches are limited to dense matrices, our techniques allow us to work with matrices of all densities. This may be of independent interest for other sparse random constraint satisfaction problems.
Cloud Clustering Over January 2003 via Scalable Rotationally Invariant Autoencoder

Unsupervised fashion of cloud analysis has the significant possibility of exploring massive quantities of satellite cloud imagery to discover unknown cloud patterns that can be relevant to climate change research, free from the assumption of artificial cloud categories. We describe a further development of rotation-invariant cloud clustering (RICC) that leverages unsupervised deep learning autoencoder and clustering to be scaled for larger cloud datasets. Results suggest that our rotation-invariant autoencoder shows high scalability conditioned on the size of GPUs, and the clusters generated from RICC on the month-long dataset capture unique spatial patterns with distinct cloud physical properties.
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method

Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism. On the other hand, although kernel machines suffer from the same computation bottleneck in pairwise dot products, several approximation schemes have been successfully incorporated to considerably reduce their computational cost without sacrificing too much accuracy. In this work, we leverage the computation methods for kernel machines to alleviate the high computational cost and introduce Skyformer, which replaces the softmax structure with a Gaussian kernel to stabilize the model training and adapts the Nystr\"om method to a non-positive semidefinite matrix to accelerate the computation. We further conduct theoretical analysis by showing that the matrix approximation error of our proposed method is small in the spectral norm. Experiments on Long Range Arena benchmark show that the proposed method is sufficient in getting comparable or even better performance than the full self-attention while requiring fewer computation resources.
On the Parabolic Boundary Harnack Principle

We investigate the parabolic Boundary Harnack Principle by the analytical methods developed in De Silva and Savin (J Differ Equ 3(15):2419–2429, 2020; J Math Eng (in press)). Besides the classical case, we deal with less regular space-time domains, including slit domains.
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation

Human explanation (e.g., in terms of feature importance) has been recently used to extend the communication channel between human and agent in interactive machine learning. Under this setting, human trainers provide not only the ground truth but also some form of explanation. However, this kind of human guidance was only investigated in supervised learning tasks, and it remains unclear how to best incorporate this type of human knowledge into deep reinforcement learning. In this paper, we present the first study of using human visual explanations in human-in-the-loop reinforcement learning (HRL). We focus on the task of learning from feedback, in which the human trainer not only gives binary evaluative "good" or "bad" feedback for queried state-action pairs, but also provides a visual explanation by annotating relevant features in images. We propose EXPAND (EXPlanation AugmeNted feeDback) to encourage the model to encode task-relevant features through a context-aware data augmentation that only perturbs irrelevant features in human salient information. We choose five tasks, namely Pixel-Taxi and four Atari games, to evaluate the performance and sample efficiency of this approach. We show that our method significantly outperforms methods leveraging human explanation that are adapted from supervised learning, and Human-in-the-loop RL baselines that only utilize evaluative feedback.
Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction.

Compliments and concerns in reviews are valuable for understanding users’ shopping interests and their opinions with respect to specific aspects of certain items. Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. They lack explicit user-attention and item-property modeling, which however could provide valuable information beyond the ability to recommend items. Therefore, we propose a tightly coupled two-stage approach, including an Aspect-Sentiment Pair Extractor (ASPE) and an Attention-Property-aware Rating Estimator (APRE). Unsupervised ASPE mines Aspect-Sentiment pairs (AS-pairs) and APRE predicts ratings using AS-pairs as concrete aspect-level evidences. Extensive experiments on seven real-world Amazon Review Datasets demonstrate that ASPE can effectively extract AS-pairs which enable APRE to deliver superior accuracy over the leading baselines.
Communication and Computation Reduction for Split Learning using Asynchronous Training

Split learning is a promising privacy-preserving distributed learning scheme that has low computation requirement at the edge device but has the disadvantage of high communication overhead between edge device and server. To reduce the communication overhead, this paper proposes a loss-based asynchronous training scheme that updates the client-side model less frequently and only sends/receives activations/gradients in selected epochs. To further reduce the communication over-head, the activations/gradients are quantized using 8-bit floating point prior to transmission. An added benefit of the proposed communication reduction method is that the computations at the client side are reduced due to reduction in the number of client model updates. Furthermore, the privacy of the proposed communication reduction based split learning method is almost the same as traditional split learning. Simulation results on VGG11, VGG13 and ResNet18 models on CIFAR-10 show that the communication cost is reduced by 1.64x-106.7x and the computations in the client are reduced by 2.86x-32.1x when the accuracy degradation is less than 0.5% for the single-client case. For 5 and 10-client cases, the communication cost reduction is 11.9x and 11.3x on VGG11 for 0.5% loss in accuracy.
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings. Currently, the quality of a model's uncertainty is evaluated using point-prediction metrics such as negative log-likelihood or the Brier score on heldout data. In this study, we provide the first large scale evaluation of the empirical frequentist coverage properties of well known uncertainty quantification techniques on a suite of regression and classification tasks. We find that, in general, some methods do achieve desirable coverage properties on \emph{in distribution} samples, but that coverage is not maintained on out-of-distribution data. Our results demonstrate the failings of current uncertainty quantification techniques as dataset shift increases and establish coverage as an important metric in developing models for real-world applications.