cssi/SciSciGPT-SciSciNet huggingface dataset:
Reducing Exclusionary Attitudes through Interpersonal Conversation: Evidence from Three Field Experiments

Exclusionary attitudes—prejudice toward outgroups and opposition to policies that promote their well-being—are presenting challenges to democratic societies worldwide. Drawing on insights from psychology, we argue that non-judgmentally exchanging narratives in interpersonal conversations can facilitate durable reductions in exclusionary attitudes. We support this argument with evidence from three pre-registered field experiments targeting exclusionary attitudes toward unauthorized immigrants and transgender people. In these experiments, 230 canvassers conversed with 6,869 voters across 7 US locations. In Experiment 1, face-to-face conversations deploying arguments alone had no effects on voters’ exclusionary immigration policy or prejudicial attitudes, but otherwise identical conversations also including the non-judgmental exchange of narratives durably reduced exclusionary attitudes for at least four months (d = 0.08). Experiments 2 and 3, targeting transphobia, replicate these findings and support the scalability of this strategy (ds = 0.08, 0.04). Non-judgmentally exchanging narratives can help overcome the resistance to persuasion often encountered in discussions of these contentious topics.
The Interplay between Molten Globules and Heme Disassociation Defines Human Hemoglobin Disassembly

Abstract Hemoglobin functions as a tetrameric oxygen transport protein, with each subunit containing a heme cofactor. Its denaturation, either in vivo or in vitro, involves autoxidation to methemoglobin, followed by cofactor loss and globin unfolding. We have proposed a global disassembly scheme for human methemoglobin, linking hemin (ferric protoporphyrin IX) disassociation and apoprotein unfolding pathways. The model is based on the evaluation of circular dichroism and visible absorbance measurements of guanidine-hydrochloride-induced disassembly of methemoglobin and previous measurements of apohemoglobin unfolding. The populations of holointermediates and equilibrium disassembly parameters were estimated quantitatively for adult and fetal hemoglobins. The key stages are characterized by hexacoordinated hemichrome intermediates, which are important for preventing hemin disassociation from partially unfolded, molten globular species during early disassembly and late-stage assembly events. Both unfolding experiments and independent small angle x-ray scattering measurements demonstrate that heme disassociation leads to the loss of tetrameric structural integrity. Our model predicts that after autoxidation, dimeric and monomeric hemichrome intermediates occur along the disassembly pathway inside red cells, where the hemoglobin concentration is very high. This prediction suggests why misassembled hemoglobins often get trapped as hemichromes that accumulate into insoluble Heinz bodies in the red cells of patients with unstable hemoglobinopathies. These Heinz bodies become deposited on the cell membranes and can lead to hemolysis. Alternatively, when acellular hemoglobin is diluted into blood plasma after red cell lysis, the disassembly pathway appears to be dominated by early hemin disassociation events, which leads to the generation of higher fractions of unfolded apo subunits and free hemin, which are known to damage the integrity of blood vessel walls. Thus, our model provides explanations of the pathophysiology of hemoglobinopathies and other disease states associated with unstable globins and red cell lysis and also insights into the factors governing hemoglobin assembly during erythropoiesis.
When your wearables become your fitness mate

Abstract Acknowledging the powerful sensors on wearables and smartphones enabling various applications to improve users' life styles and qualities (e.g., sleep monitoring and running rhythm tracking), this paper takes one step forward developing FitCoach, a virtual fitness coach leveraging users' wearable mobile devices (including wrist-worn wearables and arm-mounted smartphones) to assess dynamic postures (movement patterns & positions) in workouts. FitCoach aims to help the user to achieve effective workout and prevent injury by dynamically depicting the short-term and long-term picture of a user's workout based on various sensors in wearable mobile devices. In particular, FitCoach recognizes different types of exercises and interprets fine-grained fitness data (i.e., motion strength and speed) to an easy-to-understand exercise review score, which provides a comprehensive workout performance evaluation and recommendation. Our system further enables contactless device control during workouts (e.g., gesture to pick up an incoming call) through distinguishing customized gestures from regular exercise movement. In addition, FitCoach has the ability to align the sensor readings from wearable devices to the human coordinate system, ensuring the accuracy and robustness of the system. Extensive experiments with over 5000 repetitions of 12 types of exercises involve 12 participants doing both anaerobic and aerobic exercises in indoors as well as outdoors. Our results demonstrate that FitCoach can provide meaningful review and recommendations to users by accurately measure their workout performance and achieve 93 % and 90 % accuracy for workout analysis and customized control gesture recognition, respectively.
Cholesterol-dependent enrichment of understudied erythrocytic stages of human Plasmodium parasites

For intracellular pathogens, the host cell provides needed protection and nutrients. A major challenge of intracellular parasite research is collection of high parasite numbers separated from host contamination. This situation is exemplified by the malaria parasite, which spends a substantial part of its life cycle inside erythrocytes as rings, trophozoites, and schizonts, before egress and reinvasion. Erythrocytic Plasmodium parasite forms refractory to enrichment remain understudied due to high host contamination relative to low parasite numbers. Here, we present a method for separating all stages of Plasmodium-infected erythrocytes through lysis and removal of uninfected erythrocytes. The Streptolysin O-Percoll (SLOPE) method is effective on previously inaccessible forms, including circulating rings from malaria-infected patients and artemisinin-induced quiescent parasites. SLOPE can be used on multiple parasite species, under multiple media formulations, and lacks measurable impacts on parasite viability. We demonstrate erythrocyte membrane cholesterol levels modulate the preferential lysis of uninfected host cells by SLO, and therefore modulate the effectiveness of SLOPE. Targeted metabolomics of SLOPE-enriched ring stage samples confirms parasite-derived metabolites are increased and contaminating host material is reduced compared to non-enriched samples. Due to consumption of cholesterol by other intracellular bacteria and protozoa, SLOPE holds potential for improving research on organisms beyond Plasmodium.
Stable, Concurrent Controller Composition for Multi-Objective Robotic Tasks

Robotic systems often need to consider multiple tasks concurrently. This challenge calls for controller synthesis algorithms that fulfill multiple control specifications while maintaining the stability of the overall system. In this paper, we decompose multi-objective tasks into subtasks, where individual subtask controllers are designed independently and then combined to generate the overall control policy. In particular, we adopt Riemannian Motion Policies (RMPs), a recently proposed controller structure in robotics, and, RMPflow, its associated computational framework for combining RMP controllers. We re-establish and extend the stability results of RMPflow through a rigorous Control Lyapunov Function (CLF) treatment. We then show that RMPflow can stably combine individually designed subtask controllers that satisfy certain CLF constraints. This new insight leads to an efficient CLF-based computational framework to generate stable controllers that consider all the subtasks simultaneously. Compared with the original usage of RMPflow, our framework provides users the flexibility to incorporate design heuristics through nominal controllers for the subtasks. We validate the proposed computational framework through numerical simulation and robotic implementation.
A Computational Theory of Robust Localization Verifiability in the Presence of Pure Outlier Measurements

The problem of localizing a set of nodes from relative pairwise measurements is at the core of many applications such as Structure from Motion (SfM), sensor networks, and Simultaneous Localization And Mapping (SLAM). In practical situations, the accuracy of the relative measurements is marred by noise and outliers; hence, we have the problem of quantifying how much we should trust the solution returned by some given localization solver. In this work, we focus on the question of whether an l 1 -norm robust optimization formulation can recover a solution that is identical to the ground truth, under the scenario of translation-only measurements corrupted exclusively by outliers and no noise; we call this concept verifiability. On the theoretical side, we prove that the verifiability of a problem depends only on the topology of the graph of measurements, the edge support of the outliers, and their signs, while it is independent of ground truth locations of the nodes, and of any positive scaling of the outliers. On the computational side, we present a novel approach based on the dual simplex algorithm that can check the verifiability of a problem, completely characterize the space of equivalent solutions if they exist, and identify subgraphs that are verifiable. As an application of our theory, we provide a procedure to compute a priori probability of recovering a solution congruent or equivalent to the ground truth given a measurement graph and the probabilities of each edge containing an outlier.
Unique factorization in polynomial rings with zero divisors

Given a certain factorization property of a ring R, we can ask if this property extends to the polynomial ring over R or vice versa. For example, it is well known that R is a unique factorization d...
Higher arity self-distributive operations in Cascades and their cohomology

We investigate constructions of higher arity self-distributive operations, and give relations between cohomology groups corresponding to operations of different arities. For this purpose we introdu...
Inverse Statics Optimization for Compound Tensegrity Robots

Robots built from cable-driven tensegrity (`tension-integrity') structures have many of the advantages of soft robots, such as flexibility and robustness, yet still obey simple statics and dynamics models. However, existing approaches cannot natively model tensegrity robots with arbitrary rigid bodies in their tension network. This work presents a method to calculate the cable tensions in static equilibrium for such robots, here defined as compound tensegrities. First, a static equilibrium model for compound tensegrity robots is reformulated from the standard force density method used with other tensegrity structures. Next, we pose the problem of calculating tension forces in the robot's cables under our model. A solution is proposed as a quadratic optimization problem with practical constraints. Simulations illustrate how this inverse statics optimization problem can be used for both the design and control of two different compound tensegrity applications: a spine robot and a quadruped robot built from that spine. Finally, we verify the accuracy of the inverse statics model through a hardware experiment, demonstrating the feasibility of low-error open-loop control using our methodology.
On the arithmetic of weighted complete intersections of low degree

A variety is rationally connected if two general points can be joined by a rational curve. A higher version of this notion is rational simple connectedness, which requires suitable spaces of rational curves through two points to be rationally connected themselves. We prove that smooth, complex, weighted complete intersections of low enough degree are rationally simply connected. This result has strong arithmetic implications for weighted complete intersections defined over the function field of a smooth, complex curve. Namely, it implies that these varieties satisfy weak approximation at all places, that R-equivalence of rational points is trivial, and that the Chow group of zero cycles of degree zero is zero.
Experimental studies of the non-oxidative dehydrogenation of ethylbenzene using a membrane reactor

Abstract The non-oxidative dehydrogenation of ethylbenzene to styrene has been studied in membrane reactors consisting of two concentric tubes. The inner tube is packed with particles of Shell 105 catalyst. This tube is an ALCOA alumina membrane characterized by a mean pore diameter of 50A˚. The feed to the inner tube consists of ethylbenzene, water and nitrogen. A sweep stream of nitrogen flows through the annulus in cocurrent fashion. Two membrane reactors were employed. The first was operated at 555° C and the second at 602° C. During the first 25 to 35 h of operation, equilibrium was achieved with respect to transport of hydrogen, water and nitrogen across the membrane. Subsequently, the amount of material transferred across the membrane steadily decreased. Inspection of used membranes indicated that carbon deposition was causing significant clogging of the membrane.
Coupling of paraxial and white-noise approximations of the Helmholtz equation in randomly layered media

Abstract We study the simultaneous paraxial and white-noise limit of the Helmholtz equation in randomly layered media where the refractive index fluctuations are in the direction of propagation. We consider the regime in which the wavelength is of the same order as the correlation length of the random fluctuations of the refractive index. We show that this simultaneous limit can be taken in this regime by introducing into the equation an arbitrarily small regularization parameter. The corresponding paraxial white-noise approximation that we derive is different from that of the previously studied high-frequency regime. Since the correlation length of the refractive index fluctuations due to atmospheric turbulence varies substantially, our results are relevant for numerous different propagation scenarios including microwave and radiowave propagation through various regions of the atmosphere.
Inspection of Penstocks and Featureless Tunnel-like Environments Using Micro UAVs.

Micro UAVs are receiving a great deal of attention in many diverse applications. In this paper, we are interested in a unique application, surveillance for maintenance of large infrastructure assets such as dams and penstocks, where the goal is to periodically inspect and map the structure to detect features that might indicate the potential for failures. Availability of architecture drawings of these constructions makes the mapping problem easier. However large buildings with featureless geometries pose a significant problem since it is difficult to design a robust localization algorithm for inspection operations. In this paper we show how a small quadrotor equipped with minimal sensors can be used for inspection of tunnel-like environments such as seen in dam penstocks. Penstocks in particular lack features and do not provide adequate structure for robot localization, especially along the tunnel axis. We develop a Rao-Blackwellized particle filter based localization algorithm which uses a derivative of the ICP for integrating laser measurements and IMU for short-to-medium range pose estimation. To our knowledge, this is the only study in the literature focusing on localization and autonomous control of a UAV in 3-D, featureless tunnel-like environments. We show the success of our work with results from real experiments.
Economic Analysis of Law

Abstract This is a survey of economic analysis of law, that is, of the emerging field under which the standard tools of microeconomics are employed to identify the effects of legal rules and their social desirability. Five basic subject areas are covered. The first is legal liability for harm. Here we discuss liability rules as incentives to reduce risk, issues of risk-bearing and insurance, and the costs of the liability system. Second, we consider property law, where we address the nature and justification of property rights, public property, the acquisition and transfer of property, externalities surrounding the use of property, and intellectual property. Third, we examine contract law, including the formation of contracts, their interpretation, and remedies for their breach. We focus on production contracts but also discuss other types, including donative contracts. Fourth, we treat the subject of civil litigation, that is, the bringing of lawsuits, and their settlement or disposition at trial. We also mention the appeals process, alternative dispute resolution, the provision of legal advice, and several additional topics relating to litigation. Fifth, we consider public enforcement of law, focusing on the level of law enforcement effort, the magnitude of sanctions, and other issues relevant to criminal law. Finally, we discuss criticisms that are commonly made by legal academics of economic analysis of law and offer concluding remarks.
Can increasing private school participation and monetary loss in a voucher program affect public school performance? Evidence from Milwaukee ☆

Abstract The Milwaukee voucher program, as implemented in 1990, allowed only non-sectarian private schools to participate in the program. Following a Wisconsin Supreme Court ruling, the program saw a major shift and entered into its second phase, when religious private schools were allowed to participate for the first time in 1998. This led to more than a three-fold increase in the number of private schools and almost a four-fold increase in the number of choice students. Moreover, due to some changes in funding provisions, the revenue loss per student from vouchers increased in the second phase of the program. This paper analyzes the impacts of increase in competition brought about by these changes on public school performance in Milwaukee. Using data from 1987 to 2002, and a difference-in-differences estimation strategy in trends, the paper finds that these changes have led to an improvement of the public schools in the second phase of the program as compared to the first phase. The results are robust to alternative samples and specifications, and survive several sensitivity checks including correcting for mean reversion. The findings imply that voucher design matters and choice of parameters in a voucher program is crucial as far as impacts on public school incentives and performance are concerned.
Energy Factors, Leasing Structure and the Market Price of Office Buildings in the U.S.

This paper presents an empirical analysis of the relation between energy factor markets, leasing structures, and the transaction prices of office buildings in the U.S. We employ a large sample of 15,133 office building transactions between 2001 and 2010. In addition to building characteristics, we also include information on the operating expenses, net operating income, and market capitalization rates at sale to estimate an asset-pricing model for commercial office real estate assets. A further set of important controls in our analysis is the forward/futures contract prices for electricity and natural gas. We also include weather metrics for each building’s location and sale date. Our final set of controls includes information on the dominant contractual leasing structure of the buildings. Our empirical results suggest that Energy Star labels do not explain additional variance in property prices once the key asset-pricing factors of expenses, income and market capitalization rates are included. By contrast, energy-factor market prices, the shape of the energy forward price curves, and weather metrics are consistently significant determinants of office building transaction prices, suggesting that commercial office building prices are exposed to shocks in these markets.
Managerial contracting and corporate social responsibility

Abstract This paper presents a theory of corporate social responsibility in the form of the private provision of public goods and private redistribution by a firm. These social expenditures are determined by a manager operating under a compensation contract chosen by shareholders in a capital market that prices social expenditures. The theory incorporates three explanations for compensation systems that encompass social performance. First, consumers may reward the firm for its social expenditures; second, managers may have personal preferences for contributing to social causes; and third, the shareholder clientele a firm attracts may prefer social expenditures. Social incentives are higher powered the more consumers reward the firm and the stronger are shareholders' warm glow preferences for social expenditures. Profit incentives are higher powered the more consumers reward the firm but are independent of shareholder preferences. When consumers reward the firm for its social expenditures, firms with higher ability managers have both higher operating profits and higher social expenditures when times are good, so a positive correlation is predicted. In bad times, however, the correlation is negative, except for firms with very low ability managers in very bad times, where the correlation is zero.
Endoscopic treatment of carpal tunnel syndrome: a critical review

Object The goal of this paper is to present a critical review of the endoscopic procedures currently in use for the treatment of carpal tunnel syndrome. Endoscopic techniques and outcomes are discussed. Methods An extensive review of published articles on the subject of endoscopic carpal tunnel release surgery is presented, encompassing six endoscopic techniques used to treat carpal tunnel syndrome. Since the first report in 1987, 7091 patients have undergone 8068 operations. The overall success rate has been 96.52%, with a complication rate of 2.67% and a failure rate of 2.61%. The mean time to return to work in patients not receiving Workers' Compensation was 17.8 days, ranging between 10.8 and 22.3 days. The most common complications were transient paresthesias of the ulnar and median nerves. Other complications included superficial palmar arch injuries, reflex sympathetic dystrophy, flexor tendon lacerations, and incomplete transverse carpal ligament division. In many studies in which open and endoscopic techniques were compared, it was reported that patients in the the latter group experienced significantly less pain and returned to work and activities of daily living earlier. Conclusions Success and complication rates of endoscopic carpal tunnel release surgery are similar to those for standard open procedures.
Multiparty Proximity Testing with Dishonest Majority from Equality Testing.

Motivated by the recent widespread emergence of location-based services (LBS) over mobile devices, we explore efficient protocols for proximity-testing. Such protocols allow a group of friends to discover if they are all close to each other in some physical location, without revealing their individual locations to each other. We focus on hand-held devices and aim at protocols with very small communication complexity and a small number of rounds. The proximity-testing problem can be reduced to the private equality testing (PET) problem, in which parties find out whether or not they hold the same input (drawn from a low-entropy distribution) without revealing any other information about their inputs to each other. While previous works analyze the 2-party PET special case (and its LBS application), in this work we consider highly-efficient schemes for the multiparty case with no honest majority. We provide schemes for both a direct-communication setting and a setting with a honest-but-curious mediating server that does not learn the users’ inputs. Our most efficient scheme takes 2 rounds, where in each round each user sends only a couple of ElGamal ciphertexts.
Swapped face detection using deep learning and subjective assessment

The tremendous success of deep learning for imaging applications has resulted in numerous beneficial advances. Unfortunately, this success has also been a catalyst for malicious uses such as photo-realistic face swapping of parties without consent. In this study, we use deep transfer learning for face swapping detection, showing true positive rates greater than 96% with very few false alarms. Distinguished from existing methods that only provide detection accuracy, we also provide uncertainty for each prediction, which is critical for trust in the deployment of such detection systems. Moreover, we provide a comparison to human subjects. To capture human recognition performance, we build a website to collect pairwise comparisons of images from human subjects. Based on these comparisons, we infer a consensus ranking from the image perceived as most real to the image perceived as most fake. Overall, the results show the effectiveness of our method. As part of this study, we create a novel dataset that is, to the best of our knowledge, the largest swapped face dataset created using still images. This dataset will be available for academic research use per request. Our goal of this study is to inspire more research in the field of image forensics through the creation of a dataset and initial analysis.
PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition

We propose a novel system for unsupervised skeleton-based action recognition. Given inputs of body-keypoints sequences obtained during various movements, our system associates the sequences with actions. Our system is based on an encoder-decoder recurrent neural network, where the encoder learns a separable feature representation within its hidden states formed by training the model to perform the prediction task. We show that according to such unsupervised training, the decoder and the encoder self-organize their hidden states into a feature space which clusters similar movements into the same cluster and distinct movements into distant clusters. Current state-of-the-art methods for action recognition are strongly supervised, i.e., rely on providing labels for training. Unsupervised methods have been proposed, however, they require camera and depth inputs (RGB+D) at each time step. In contrast, our system is fully unsupervised, does not require action labels at any stage and can operate with body-keypoints input only. Furthermore, the method can perform on various dimensions of body-keypoints (2D or 3D) and can include additional cues describing movements. We evaluate our system on three action recognition benchmarks with different numbers of actions and examples. Our results outperform prior unsupervised skeleton-based methods, unsupervised RGB+D based methods on cross-view tests and while being unsupervised have similar performance to supervised skeleton-based action recognition.
Training Deep Energy-Based Models with f-Divergence Minimization

Deep energy-based models (EBMs) are very flexible in distribution parametrization but computationally challenging because of the intractable partition function. They are typically trained via maximum likelihood, using contrastive divergence to approximate the gradient of the KL divergence between data and model distribution. While KL divergence has many desirable properties, other f-divergences have shown advantages in training implicit density generative models such as generative adversarial networks. In this paper, we propose a general variational framework termed f-EBM to train EBMs using any desired f-divergence. We introduce a corresponding optimization algorithm and prove its local convergence property with non-linear dynamical systems theory. Experimental results demonstrate the superiority of f-EBM over contrastive divergence, as well as the benefits of training EBMs using f-divergences other than KL.
Attribution in Scale and Space

We study the attribution problem for deep networks applied to perception tasks. For vision tasks, attribution techniques attribute the prediction of a network to the pixels of the input image. We propose a new technique called Blur Integrated Gradients (Blur IG). This technique has several advantages over other methods. First, it can tell at what scale a network recognizes an object. It produces scores in the scale/frequency dimension, that we find captures interesting phenomena. Second, it satisfies the scale-space axioms, which imply that it employs perturbations that are free of artifact. We therefore produce explanations that are cleaner and consistent with the operation of deep networks. Third, it eliminates the need for baseline parameter for Integrated Gradients for perception tasks. This is desirable because the choice of baseline has a significant effect on the explanations. We compare the proposed technique against previous techniques and demonstrate application on three tasks: ImageNet object recognition, Diabetic Retinopathy prediction, and AudioSet audio event identification. Code and examples are at https://github.com/PAIR-code/saliency.
A Sharp Multidimensional Hermite–Hadamard Inequality

Let $\Omega \subset \mathbb{R}^d$, $d \geq 2$, be a bounded convex domain and $f\colon \Omega \to \mathbb{R}$ be a non-negative subharmonic function. In this paper we prove the inequality \[ \frac{1}{|\Omega|}\int_\Omega f(x)\,dx \leq \frac{d}{|\partial\Omega|}\int_{\partial\Omega} f(x)\,d\sigma(x)\,. \] Equivalently, the result can be stated as a bound for the gradient of the Saint Venant torsion function. Specifically, if $\Omega \subset \mathbb{R}^d$ is a bounded convex domain and $u$ is the solution of $-\Delta u =1$ with homogeneous Dirichlet boundary conditions, then \[ \|\nabla u\|_{L^\infty(\Omega)} < d\frac{|\Omega|}{|\partial\Omega|}\,. \] Moreover, both inequalities are sharp in the sense that if the constant $d$ is replaced by something smaller there exist convex domains for which the inequalities fail. This improves upon the recent result that the optimal constant is bounded from above by $d^{3/2}$ due to Beck et al.
How recurrent networks implement contextual processing in sentiment analysis

Neural networks have a remarkable capacity for contextual processing--using recent or nearby inputs to modify processing of current input. For example, in natural language, contextual processing is necessary to correctly interpret negation (e.g. phrases such as "not bad"). However, our ability to understand how networks process context is limited. Here, we propose general methods for reverse engineering recurrent neural networks (RNNs) to identify and elucidate contextual processing. We apply these methods to understand RNNs trained on sentiment classification. This analysis reveals inputs that induce contextual effects, quantifies the strength and timescale of these effects, and identifies sets of these inputs with similar properties. Additionally, we analyze contextual effects related to differential processing of the beginning and end of documents. Using the insights learned from the RNNs we improve baseline Bag-of-Words models with simple extensions that incorporate contextual modification, recovering greater than 90% of the RNN's performance increase over the baseline. This work yields a new understanding of how RNNs process contextual information, and provides tools that should provide similar insight more broadly.
Sequence-Dependent Correlated Segments in the Intrinsically Disordered Region of ChiZ

How sequences of intrinsically disordered proteins (IDPs) code for their conformational dynamics is poorly understood. Here, we combined NMR spectroscopy, small-angle X-ray scattering (SAXS), and molecular dynamics (MD) simulations to characterize the conformations and dynamics of ChiZ1-64. MD simulations, first validated by SAXS and secondary chemical shift data, found scant α-helices or β-strands but a considerable propensity for polyproline II (PPII) torsion angles. Importantly, several blocks of residues (e.g., 11–29) emerge as “correlated segments”, identified by their frequent formation of PPII stretches, salt bridges, cation-π interactions, and sidechain-backbone hydrogen bonds. NMR relaxation experiments showed non-uniform transverse relaxation rates (R2s) and nuclear Overhauser enhancements (NOEs) along the sequence (e.g., high R2s and NOEs for residues 11–14 and 23–28). MD simulations further revealed that the extent of segmental correlation is sequence-dependent; segments where internal interactions are more prevalent manifest elevated “collective” motions on the 5–10 ns timescale and suppressed local motions on the sub-ns timescale. Amide proton exchange rates provides corroboration, with residues in the most correlated segment exhibiting the highest protection factors. We propose the correlated segment as a defining feature for the conformations and dynamics of IDPs.
Incorporating structural stigma into network analysis

Abstract A rich literature has explored the modeling of homophily and other forms of nonuniform mixing associated with individual-level covariates within the exponential family random graph (ERGM) framework. Such differential mixing does not fully explain phenomena such as stigma, however, which involve the active maintenance of social boundaries by ostracism of persons with out-group ties. Here, we introduce a new family of statistics that allows for such effects to be captured, making it possible to probe for the potential presence of boundary maintenance above and beyond simple differences in nomination rates. We demonstrate these statistics in the context of gender segregation in a school classroom, and introduce a framework for understanding the associated coefficients via network perturbation.
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games

We show by counterexample that policy-gradient algorithms have no guarantees of even local convergence to Nash equilibria in continuous action and state space multi-agent settings. To do so, we analyze gradient-play in N-player general-sum linear quadratic games, a classic game setting which is recently emerging as a benchmark in the field of multi-agent learning. In such games the state and action spaces are continuous and global Nash equilibria can be found be solving coupled Ricatti equations. Further, gradient-play in LQ games is equivalent to multi-agent policy-gradient. We first show that these games are surprisingly not convex games. Despite this, we are still able to show that the only critical points of the gradient dynamics are global Nash equilibria. We then give sufficient conditions under which policy-gradient will avoid the Nash equilibria, and generate a large number of general-sum linear quadratic games that satisfy these conditions. The existence of such games indicates that one of the most popular approaches to solving reinforcement learning problems in the classic reinforcement learning setting has no local guarantee of convergence in multi-agent settings. Further, the ease with which we can generate these counterexamples suggests that such situations are not mere edge cases and are in fact quite common.
On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities

Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite this, it is common for ERM systems to follow myopic decision policies in the real world. Principled approaches to aid ERM decision-making under uncertainty have been explored but have failed to be accepted into real systems. We identify a key issue impeding their adoption --- algorithmic approaches to emergency response focus on reactive, post-incident dispatching actions, i.e. optimally dispatching a responder after incidents occur. However, the critical nature of emergency response dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. This is not a trivial planning problem --- a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal problem in ERM systems is planning under limited communication, which is particularly important in disaster scenarios that affect communication networks. We address both problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and exploit the structure of the dispatch problem. We evaluate our proposed approach using real-world data, and find that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response time as well as its variance.
Integral Control Barrier Functions for Dynamically Defined Control Laws

This letter introduces integral control barrier functions (I-CBFs) as a means to enable the safety-critical integral control of nonlinear systems. Importantly, I-CBFs allow for the holistic encoding of both state constraints and input bounds in a single framework. We demonstrate this by applying them to a dynamically defined tracking controller, thereby enforcing safety in state and input through a minimally invasive I-CBF controller framed as a quadratic program.
Donald T. Nicolaisen: An Internationalist SEC Chief Accountant (1944–2019)

Donald T. Nicolaisen (1944-2019) was an internationalist, a progressive thinker, and a leader of the U.S. accounting profession. This memorial article traces Nicolaisen’s early education and career in the Milwaukee office of Price Waterhouse & Co. in the 1960s, his rise to partner and eventual transfer to the firm’s national office in New York City in the 1970s, and his appointment and tenure as the Chief Accountant of the Securities and Exchange Commission in the early 2000s. During his time as the Chief Accountant, Nicolaisen laid the groundwork for the lifting of the reconciliation requirement for foreign private issuers traded on American stock exchanges; the implantation of Sarbanes-Oxley’s internal control reporting requirements; and the introduction of XBRL for companies’ annual filings with the Commission. Through these initiatives, Nicolaisen leaves behind a legacy of improving both the quality of financial reporting and the efficiency of U.S. capital markets.
On the (in-)approximability of Bayesian Revenue Maximization for a Combinatorial Buyer

We consider a revenue-maximizing single seller with mitems for sale to a single buyer whose value v(·) for the items is drawn from a known distribution Dof support k. A series of works by Cai et al. establishes that when each v(·) in the support of Dis additive or unit-demand (or c-demand), the revenue-optimal auction can be found in poly(m,k) time. We show that going barely beyond this, even to matroid-based valuations (a proper subset of Gross Substitutes), results in strong hardness of approximation. Specifically, even on instances with mitems and k ≤ m valuations in the support of D, it is not possible to achieve a 1/m1-e-approximation for any e>0 to the revenue-optimal mechanism for matroid-based valuations in (randomized) poly-time unless NP ⊆ RP (note that a $1/k$-approximation is trivial). Cai et al.'s main technical contribution is a black-box reduction from revenue maximization for valuations in class V to optimizing the difference between two values in class V. Our main technical contribution is a black-box reduction in the other direction (for a wide class of valuation classes), establishing that their reduction is essentially tight.
Stochastic Resource Allocation for Electricity Distribution Network Resilience

In recent years, it has become crucial to improve the resilience of electricity distribution networks (DNs) against storm-induced failures. Microgrids enabled by Distributed Energy Resources (DERs) can significantly help speed up re-energization of loads, particularly in the complete absence of bulk power supply. We describe an integrated approach which considers a pre-storm DER allocation problem under the uncertainty of failure scenarios as well as a post-storm dispatch problem in microgrids during the multi-period repair of the failed components. This problem is computationally challenging because the number of scenarios (resp. binary variables) increases exponentially (resp. quadratically) in the network size. Our overall solution approach for solving the resulting two-stage mixed-integer linear program (MILP) involves implementing the sample average approximation (SAA) method and Benders Decomposition. Additionally, we implement a greedy approach to reduce the computational time requirements of the post-storm repair scheduling and dispatch problem. The optimality of the resulting solution is evaluated on a modified IEEE 36-node network.
Exposure to environmental chemical mixtures is associated with nasal colonization by Staphylococcus aureus: NHANES 2001–2004

Abstract Background Understanding the health effects of exposure to chemical mixtures is critically important given the broad range of concurrent exposures throughout the life-course. While investigations of environmental chemicals and components of the human microbiome are becoming more common, few have examined associations with chemical mixtures. This study assesses the association between exposure to mixtures of 66 different environmental chemicals and nasal colonization of Staphylococcus aureus (SA) and methicillin resistant SA (MRSA). Methods Data came from the National Health and Nutrition Examination Survey (NHANES) 2001–2004. The analytical sample consists of 10,312 participants, age 6 years and older, subdivided into 8 groups with different chemical exposure mixtures. Within each of 6 chemical classes (metals, phthalates, polycyclic aromatic hydrocarbons (PAHs), polybrominated diphenyl ethers (PBDEs), polyfluorochemicals (PFCs), and phenols), weighted quantile sum (WQS) regression was used to analyze the joint association of the component compounds and nasal SA colonization. WQS was also used to assess the joint association of 3 chemical mixtures (metals, metal and PAHs, and metal and triclosan) and nasal MRSA colonization. All regression models were adjusted for confounders. Results The analytical sample was between ages 6–85, slightly more female, and predominantly non-smokers. Prevalence of SA carriage was 29.2%, and MRSA colonization prevalence was 1.2%. Within each chemical class, odds of SA colonization increased statistically significantly with exposure to mixtures of metals (OR = 1.11, 95% CI = 1.02–1.20), phthalates (OR = 1.09, 95% CI = 1.04–1.14), and phenols (OR = 1.08, 95% CI = 1.01–1.15). Exposure to a mixture of metals combined with PAHs was also associated with increased odds of MRSA carriage (OR = 1.38, 95% CI = 1.02–1.86). Conclusion Results indicate an association between multiple environmental chemical mixtures and SA colonization, including MRSA. These findings support the need for further analysis of associations between chemical mixtures and SA colonization, as well as other components of the human microbiome.
The Category of Factorization

We introduce and investigate the category of factorization of a multiplicative, commutative, cancellative, pre-ordered monoid A, which we denote $$\mathcal {F}(A)$$ . The objects of $$\mathcal {F}(A)$$ are factorizations of elements of A, and the morphisms in $$\mathcal {F}(A)$$ encode combinatorial similarities and differences between the factorizations. We pay particular attention to the divisibility pre-order and to the monoid $$A=D{\setminus }\{0\}$$ where D is an integral domain. Among other results, we show that $$\mathcal {F}(A)$$ is a symmetric and strict monoidal category with weak equivalences and compute the associated category of fractions obtained by inverting the weak equivalences. Also, we use this construction to characterize various factorization properties of integral domains: atomicity, unique factorization, and so on.
Stability Analysis of Interface Conditions for Ocean–Atmosphere Coupling

In this paper we analyze the stability of different coupling strategies for multidomain PDEs that arise in general circulation models used in climate simulations. We focus on fully coupled ocean–atmosphere models that are needed to represent and understand the complicated interactions of these two systems, becoming increasingly important in climate change assessment in recent years. Numerical stability issues typically arise because of different time-stepping strategies applied to the coupled PDE system. In particular, the contributing factors include using large time steps, lack of accurate interface flux, and single-iteration coupling. We investigate the stability of the coupled ocean–atmosphere models for various interface conditions such as the Dirichlet–Neumann condition and the bulk interface condition, which is unique to climate modeling. By analyzing a simplified model, we demonstrate how the parameterization of the bulk condition and other numerical and physical parameters affect the coupling stability and establish stability conditions for different coupling strategies.
Observational Learning with Fake Agents

It is common in online markets for agents to learn from other’s actions. Such observational learning can lead to herding or information cascades in which agents eventually "follow the crowd". Models for such cascades have been well studied for Bayes-rational agents that choose pay-off optimal actions. In this paper, we additionally consider the presence of fake agents that seek to influence other agents into taking one particular action. To that end, these agents take a fixed action in order to influence the subsequent agents towards their preferred action. We characterize how the fraction of such fake agents impacts behavior of the remaining agents and show that in certain scenarios, an increase in the fraction of fake agents in fact reduces the chances of their preferred outcome.
Sectional monodromy groups of projective curves

Fix a degree $d$ projective curve $X \subset \mathbb{P}^r$ over an algebraically closed field $K$. Let $U \subset (\mathbb{P}^r)^*$ be a dense open subvariety such that every hyperplane $H \in U$ intersects $X$ in $d$ smooth points. Varying $H \in U$ produces the monodromy action $\varphi: \pi_1^{\text{et}}(U) \to S_d$. Let $G_X := \mathrm{im}(\varphi)$. The permutation group $G_X$ is called the sectional monodromy group of $X$. In characteristic zero $G_X$ is always the full symmetric group, but sectional monodromy groups in characteristic $p$ can be smaller. For a large class of space curves ($r \geqslant 3$) we classify all possibilities for the sectional monodromy group $G$ as well as the curves with $G_X=G$. We apply similar methods to study a particular family of rational curves in $\mathbb{P}^2$, which enables us to answer an old question about Galois groups of generic trinomials.