cssi/SciSciGPT-SciSciNet huggingface dataset:
Death Row Narratives: A Qualitative Analysis of Mental Health Issues Found In Death Row Inmate Blog Entries:

Death row inmate narratives, culled from online blogs, are used to explore the social determinants of mental health in the context of the stresses and deprivations of living on death row. Legal and...
Assessment of a small-scale fishery: Lane Snapper (Lutjanus synagris) using a length metric method

Small-scale fisheries are hard to assess because of the limited availability of data. Therefore, a method requiring easy-to-obtain catch-data is important for the assessment and management of small-scale fisheries. The objective of this study was to assess the effect of fishing gear selectivity on a length-based metric method proposed by Froese by estimating three indicators using catch-data from Lane Snapper (Lutjanus synagris) collected in Honduras. These indicators are (1) the percentage of mature individuals in the catch, (2) the percentage of fish within the range of estimated optimal lengths to be captured, and (3) the percentage of fish larger than the optimal length. These indicators determine the level of overfishing. The indicators were estimated separately for catch-data corresponding to gill nets, and each indicator was estimated with and without selectivity correction. Selectivity and mesh sizes of the fishing gear had a major impact on the estimation of indicators 1 and 2. As for indicator 3, it consistently showed a high level of exploitation. The three estimated indicators suggested that the Lane Snapper fishery in Honduras is experiencing overfishing. Overall, the method appears to be promising for the assessment of small-scale fisheries, but it should be used cautiously.
Incidence of catheter‐associated right atrial thrombus detected by transthoracic echocardiogram

Introduction The development of right atrial (RA) thrombus (RAT) is a known complication of central venous catheter insertion (CVC). Deeper insertion of CVC within the RA may increase the risk for RAT development versus those placed at the superior vena cava (SVC)-RA junction. We sought to evaluate the incidence of catheter-associated RAT as detected by transthoracic echocardiograms (TTEs), characterize thrombi though multimodal imaging, and evaluate thrombi management with follow-up imaging. Methods A retrospective analysis was conducted of consecutive TTEs from our institution between October 1, 2018, and January 1, 2020, in which a venous catheter was visualized in the RA. Studies were reviewed in detail to determine the presence of suspected RAT. Demographic data, comorbidities, laboratory values, characteristics of the catheter and the thrombus, subsequent imaging and management, and outcomes were collected. Results A total of 364 TTEs were performed in 290 patients with a venous catheter visualized in the RA. Of these 290 patients, 15 had an imaging suspicion for RAT yielding an incidence of 5.2%. Management strategies included anticoagulation in 13 (86.7%) patients and catheter removal in 11 (73.3%) patients. At eight months of follow-up, 11 (73.3%) patients had resolution of RAT based on subsequent imaging. Conclusion In patients with deeply placed CVC catheters, the incidental detection of RAT by TTE was not trivial. Anticoagulation and catheter removal and replacement, if deemed safe, were effective methods of thrombus management. RAT as a complication of CVCs must be accounted for when addressing factors that influence depth of CVC insertion.
Dorsal anterior cingulate cortex intrinsic functional connectivity linked to electrocortical measures of error monitoring.

The error-related negativity (ERN) is a response-locked event-related potential, occurring approximately 50 ms following an erroneous response at frontocentral electrode sites. Source localization and functional magnetic resonance imaging (fMRI) research indicate that the ERN is likely generated by activity in the dorsal anterior cingulate cortex (dACC). The dACC is thought to be a part of a broader network of brain regions that collectively comprise an error monitoring network. However, little is known about how intrinsic connectivity within the dACC-based error monitoring network contributes to variability in ERN amplitude. The purpose of this study was to assess the relationship between dACC functional connectivity and ERN amplitude. In a sample of highly trait anxious individuals, the ERN was elicited in a flanker task and functional connectivity was assessed in a 10-min resting-state fMRI scan. Results suggest that the strength of dACC seeded functional connectivity with the supplementary motor area is correlated with the ΔERN (i.e., incorrect-correct responses) amplitude such that greater ΔERN amplitude was accompanied by greater functional coupling between these regions. In sum, ERN amplitude appears to be related to the strength of functional connectivity between error monitoring and motor control regions of the brain.
Factorizations of surjective maps of connected quandles

We consider the problem of when one quandle homomorphism will factor through another, restricting our attention to the case where all quandles involved are connected. We provide a complete solution to the problem for surjective quandle homomorphisms using the structure theorem for connected quandles of Ehrman et al. (2008) and the factorization system for surjective quandle homomorphsims of Bunch et al. (2010) as our primary tools. The paper contains the substantive results obtained by an REU research group consisting of the first four authors under the mentorship of the fifth, and was supported by National Science Foundation, grant DMS-1659123.
Flexibility and Rigidity in Steady Fluid Motion

Flexibility and rigidity properties of steady (time-independent) solutions of the Euler, Boussinesq and Magnetohydrostatic equations are investigated. Specifically, certain Liouville-type theorems are established which show that suitable steady solutions with no stagnation points occupying a two-dimensional periodic channel, or axisymmetric solutions in (hollowed out) cylinder, must have certain structural symmetries. It is additionally shown that such solutions can be deformed to occupy domains which are themselves small perturbations of the base domain. As application of the general scheme, Arnol’d stable solutions are shown to be structurally stable.
Centriole-less pericentriolar material serves as a microtubule organizing center at the base of C. elegans sensory cilia.

Summary During mitosis in animal cells, the centrosome acts as a microtubule organizing center (MTOC) to assemble the mitotic spindle. MTOC function at the centrosome is driven by proteins within the pericentriolar material (PCM), however the molecular complexity of the PCM makes it difficult to differentiate the proteins required for MTOC activity from other centrosomal functions. We used the natural spatial separation of PCM proteins during mitotic exit to identify a minimal module of proteins required for centrosomal MTOC function in C. elegans. Using tissue-specific degradation, we show that SPD-5, the functional homolog of CDK5RAP2, is essential for embryonic mitosis, while SPD-2/CEP192 and PCMD-1, which are essential in the one-cell embryo, are dispensable. Surprisingly, although the centriole is known to be degraded in the ciliated sensory neurons in C. elegans, 1 , 2 , 3 we find evidence for “centriole-less PCM” at the base of cilia and use this structure as a minimal testbed to dissect centrosomal MTOC function. Super-resolution imaging revealed that this PCM inserts inside the lumen of the ciliary axoneme and directly nucleates the assembly of dendritic microtubules toward the cell body. Tissue-specific degradation in ciliated sensory neurons revealed a role for SPD-5 and the conserved microtubule nucleator γ-TuRC, but not SPD-2 or PCMD-1, in MTOC function at centriole-less PCM. This MTOC function was in the absence of regulation by mitotic kinases, highlighting the intrinsic ability of these proteins to drive microtubule growth and organization and further supporting a model that SPD-5 is the primary driver of MTOC function at the PCM.
Conditional Dichotomy of Boolean Ordered Promise CSPs.

Promise Constraint Satisfaction Problems (PCSPs) are a generalization of Constraint Satisfaction Problems (CSPs) where each predicate has a strong and a weak form and given a CSP instance, the objective is to distinguish if the strong form can be satisfied vs. even the weak form cannot be satisfied. Since their formal introduction by Austrin, Guruswami, and Hastad, there has been a flurry of works on PCSPs [BBKO19,KO19,WZ20]. The key tool in studying PCSPs is the algebraic framework developed in the context of CSPs where the closure properties of the satisfying solutions known as the polymorphisms are analyzed. The polymorphisms of PCSPs are much richer than CSPs. In the Boolean case, we still do not know if dichotomy for PCSPs exists analogous to Schaefer's dichotomy result for CSPs. In this paper, we study a special case of Boolean PCSPs, namely Boolean Ordered PCSPs where the Boolean PCSPs have the predicate $x \leq y$. In the algebraic framework, this is the special case of Boolean PCSPs when the polymorphisms are monotone functions. We prove that Boolean Ordered PCSPs exhibit a computational dichotomy assuming the Rich 2-to-1 Conjecture [BKM21] which is a perfect completeness surrogate of the Unique Games Conjecture. Assuming the Rich 2-to-1 Conjecture, we prove that a Boolean Ordered PCSP can be solved in polynomial time if for every $\epsilon>0$, it has polymorphisms where each coordinate has Shapley value at most $\epsilon$, else it is NP-hard. The algorithmic part of our dichotomy is based on a structural lemma that Boolean monotone functions with each coordinate having low Shapley value have arbitrarily large threshold functions as minors. The hardness part proceeds by showing that the Shapley value is consistent under a uniformly random 2-to-1 minor. Of independent interest, we show that the Shapley value can be inconsistent under an adversarial 2-to-1 minor.
The diabetic foot.

A comprehensive program focused on foot-specific patient education, skin and nail care, and protective footwear lowers the incidence of diabetic foot ulcers and lower extremity amputations. Orthopedic or podiatric consultations are warranted for those "at risk" or with foot ulcers.
Substrate-controlled dynamics of spin qubits in low dimensional van der Waals materials

We report a theoretical study of the coherence dynamics of spin qubits in two-dimensional materials (2DMs) and van der Waals heterostructures, as a function of the host thickness and the composition of the surrounding environment. We focus on MoS2 and WS2, two promising systems for quantum technology applications, and we consider the decoherence arising from the interaction of the spin qubit with nuclear spins. We show that the Hahn-echo coherence time is determined by a complex interplay between the source of decoherence in the qubit host and in the environment, which in turn determines whether the noise evolution is in a classical or quantum mechanical regime. We suggest that the composition and thickness of van der Waals heterostructures encapsulating a qubit host can be engineered to maximize coherence times. Finally, we discuss how quantum sensors may be able to probe the dynamics of the nuclear bath in 2DMs.
A unified analysis of the English bare plural

It is argued that the English ‘bare plural’ (an NP with plural head that lacks a determiner), in spite of its apparently diverse possibilities of interpretation, is optimally represented in the grammar as a unified phenomenon. The chief distinction to be dealt with is that between the ‘generic’ use of the bare plural (as in ‘Dogs bark’) and its existential or ‘indefinite plural’ use (as in ‘He threw oranges at Alice’). The difference between these uses is not to be accounted for by an ambiguity in the NP itself, but rather by explicating how the context of the sentence acts on the bare plural to give rise to this distinction. A brief analysis is sketched in which bare plurals are treated in all instances as proper names of kinds of things. A subsidiary argument is that the null determiner is not to be regarded as the plural of the indefinite article a.
Improving Information from Manipulable Data

Data-based decisionmaking must account for the manipulation of data by agents who are aware of how decisions are being made and want to affect their allocations. We study a framework in which, due to such manipulation, data becomes less informative when decisions depend more strongly on data. We formalize why and how a decisionmaker should commit to underutilizing data. Doing so attenuates information loss and thereby improves allocation accuracy.
SU(3)-skein algebras and webs on surfaces

The $$SU_3$$ -skein algebra of a surface F is spanned by isotopy classes of certain framed graphs in $$F\times I$$ called 3-webs subject to the skein relations encapsulating relations between $$U_q(sl(3))$$ -representations. It is expected that their theory parallels that of the Kauffman bracket skein algebras. We make the first step towards developing that theory by proving that the reduced $$SU_3$$ -skein algebra of any surface of finite type is finitely generated. We achieve that result by developing a theory of canonical forms of webs in surfaces. Specifically, we show that for any ideal triangulation of F every reduced 3-web can be uniquely decomposed into unions of pyramid formations of hexagons and disjoint arcs in the faces of the triangulation with possible additional “crossbars” connecting their edges along the ideal triangulation. We show that such canonical position is unique up to “crossbar moves”. That leads us to an associated system of coordinates for webs in triangulated surfaces (counting intersections of the web with the edges of the triangulation and their rotation numbers inside of the faces of the triangulation) which determine a reduced web uniquely. Finally, we relate our skein algebras to $${\mathcal {A}}$$ -varieties of Fock–Goncharov and to $$\text {Loc}_{SL(3)}$$ -varieties of Goncharov–Shen. We believe that our coordinate system for webs is a manifestation of a (quantum) mirror symmetry conjectured by Goncharov–Shen.
Stochastic Quantization of an Abelian Gauge Theory

We study the Langevin dynamics of a U(1) lattice gauge theory on the two-dimensional torus, and prove that they converge for short time in a suitable gauge to a system of stochastic PDEs driven by space-time white noises. We fix gauge via a DeTurck trick. This also yields convergence of some gauge invariant observables on a short time interval. The proof relies on a discrete version of the theory of regularity structures.
SWARM: Adaptive Load Balancing in Distributed Streaming Systems for Big Spatial Data

The proliferation of GPS-enabled devices has led to the development of numerous location-based services. These services need to process massive amounts of streamed spatial data in real-time. The cu...
Node-weighted Network Design in Planar and Minor-closed Families of Graphs

We consider node-weighted survivable network design (SNDP) in planar graphs and minor-closed families of graphs. The input consists of a node-weighted undirected graph G = (V, E) and integer connectivity requirements r(uv) for each unordered pair of nodes uv. The goal is to find a minimum weighted subgraph H of G such that H contains r(uv) disjoint paths between u and v for each node pair uv. Three versions of the problem are edge-connectivity SNDP (EC-SNDP), element-connectivity SNDP (Elem-SNDP), and vertex-connectivity SNDP (VC-SNDP), depending on whether the paths are required to be edge, element, or vertex disjoint, respectively. Our main result is an O(k)-approximation algorithm for EC-SNDP and Elem-SNDP when the input graph is planar or more generally if it belongs to a proper minor-closed family of graphs; here, k = max uvr(uv) is the maximum connectivity requirement. This improves upon the O(klog n)-approximation known for node-weighted EC-SNDP and Elem-SNDP in general graphs [31]. We also obtain an O(1) approximation for node-weighted VC-SNDP when the connectivity requirements are in {0, 1, 2}; for higher connectivity our result for Elem-SNDP can be used in a black-box fashion to obtain a logarithmic factor improvement over currently known general graph results. Our results are inspired by, and generalize, the work of Demaine, Hajiaghayi, and Klein [13], who obtained constant factor approximations for node-weighted Steiner tree and Steiner forest problems in planar graphs and proper minor-closed families of graphs via a primal-dual algorithm.
ACTS MOBILE TERMINALS

The development of the advanced communications technology satellite (ACTS) mobile terminal (AMT) and its follow-on, the broadband aeronautical terminal (BAT), have provided an excellent testbed for the evaluation of K- and Ka-band mobile satellite communications systems. Such systems have proved viable for many different commercial and government applications. Combining emerging satellite communications technologies such as ACTS' highly focused spotbeams with the smaller, higher-gain K- and Ka-band antenna technology, results in system designs that can support significantly higher throughput capacity than today's current commercial systems. An overview of both of these terminals is presented in this paper.
Derived loss of signal complexity and plasticity in a genus of weakly electric fish

Signal plasticity can maximize the usefulness of costly animal signals such as the electric organ discharges (EODs) of weakly electric fishes. Some species of the order Gymnotiformes rapidly alter their EOD amplitude and duration in response to circadian cues and social stimuli. How this plasticity is maintained across related species with different degrees of signal complexity is poorly understood. In one genus of weakly electric gymnotiform fish (Brachyhypopomus), only one species, B. bennetti, produces a monophasic signal while all other species emit complex biphasic or multiphasic EOD waveforms produced by two overlapping but asynchronous action potentials in each electric organ cell (electrocyte). One consequence of this signal complexity is the suppression of low-frequency signal content that is detectable by electroreceptive predators. In complex EODs, reduction of the EOD amplitude and duration during daytime inactivity can decrease both predation risk and the metabolic cost of EOD generation. We compared EOD plasticity and its underlying physiology in Brachyhypopomus focusing on B. bennetti. We found that B. bennetti exhibits minimal EOD plasticity, but that its electrocytes retained vestigial mechanisms of biphasic signaling and vestigial mechanisms for modulating the EOD amplitude. These results suggest that this species represents a transitional phenotypic state within a clade where signal complexity and plasticity were initially gained and then lost. Signal mimicry, mate recognition and sexual selection are potential factors maintaining the monophasic EOD phenotype in the face of detection by electroreceptive predators.
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation

A black-box spectral method is introduced for evaluating the adversarial robustness of a given machine learning (ML) model. Our approach, named SPADE, exploits bijective distance mapping between the input/output graphs constructed for approximating the manifolds corresponding to the input/output data. By leveraging the generalized Courant-Fischer theorem, we propose a SPADE score for evaluating the adversarial robustness of a given model, which is proved to be an upper bound of the best Lipschitz constant under the manifold setting. To reveal the most non-robust data samples highly vulnerable to adversarial attacks, we develop a spectral graph embedding procedure leveraging dominant generalized eigenvectors. This embedding step allows assigning each data sample a robustness score that can be further harnessed for more effective adversarial training. Our experiments show the proposed SPADE method leads to promising empirical results for neural network models that are adversarially trained with the MNIST and CIFAR-10 data sets.
Universal Prediction Band Via Semi-Definite Programming

We propose a computationally efficient method to construct nonparametric, heteroskedastic prediction bands for uncertainty quantification, with or without any user-speciﬁed predictive model. The data-adaptive prediction band is universally applicable with minimal distributional assumptions, with strong non-asymptotic coverage properties, and easy to implement using standard convex programs. Our approach can be viewed as a novel variance interpolation with confidence and further leverages techniques from semi-definite programming and sum-of-squares optimization. Theoretical and numerical performances for the proposed approach for uncertainty quantification are analyzed.
The Faster, Better, Cheaper Approach to Space Missions: An Engineering Management Assessment

NASA was chartered as an independent civilian space agency in 1958 following the Soviet Union's dramatic launch of the Sputnik 1 (1957). In his state of the union address in May of 1961, President Kennedy issued to the fledging organization his famous challenge for a manned lunar mission by the end of the decade. The Mercury, Gemini and Apollo programs that followed put the utmost value on high quality, low risk (as low as possible within the context of space flight), quick results, all with little regard for cost. These circumstances essentially melded NASAs culture as an organization capable of great technological achievement but at extremely high cost. The Space Shuttle project, the next major agency endeavor, was put under severe annual budget constraints in the 1970's. NASAs response was to hold to the high quality standards, low risk and annual cost and let schedule suffer. The result was a significant delay in the introduction of the Shuttle as well as overall total cost growth. By the early 1990's, because NASA's budget was declining, the number of projects was also declining. Holding the same cost and schedule productivity levels as before was essentially causing NASA to price itself out of business. In 1992, the helm of NASA was turned over to a new Administrator. Dan Goldin's mantra was "faster, better, cheaper" and his enthusiasm and determination to change the NASA culture was not to be ignored. This research paper documents the various implementations of "faster, better, cheaper" that have been attempted, analyzes their impact and compares the cost performance of these new projects to previous NASA benchmarks. Fundamentally, many elements of "faster, better, cheaper" are found to be working well, especially on smaller projects. Some of the initiatives are found to apply only to smaller or experimental projects however, so that extrapolation to "flagship" projects may be problematic.
Trajectory Optimization of Electric Aircraft Subject to Subsystem Thermal Constraints

Electric aircraft pose a unique design challenge in that they lack a simple way to reject waste heat from the power train. While conventional aircraft reject most of their excess heat in the exhaust stream, for electric aircraft this is not an option. To examine the implications of this challenge on electric aircraft design and performance, we developed a model of the electric subsystems for the NASA X-57 electric testbed aircraft. We then coupled this model with a model of simple 2D aircraft dynamics and used a Legendre-Gauss-Lobatto collocation optimal control approach to find optimal trajectories for the aircraft with and without thermal constraints. The results show that the X-57 heat rejection systems are well designed for maximum-range and maximum-efficiency flight, without the need to deviate from an optimal trajectory. Stressing the thermal constraints by reducing the cooling capacity or requiring faster flight has a minimal impact on performance, as the trajectory optimization technique is able to find flight paths which honor the thermal constraints with relatively minor deviations from the nominal optimal trajectory.
Hierarchical Neural Dynamic Policies

We tackle the problem of generalization to unseen configurations for dynamic tasks in the real world while learning from high-dimensional image input. The family of nonlinear dynamical system-based methods have successfully demonstrated dynamic robot behaviors but have difficulty in generalizing to unseen configurations as well as learning from image inputs. Recent works approach this issue by using deep network policies and reparameterize actions to embed the structure of dynamical systems but still struggle in domains with diverse configurations of image goals, and hence, find it difficult to generalize. In this paper, we address this dichotomy by leveraging embedding the structure of dynamical systems in a hierarchical deep policy learning framework, called Hierarchical Neural Dynamical Policies (H-NDPs). Instead of fitting deep dynamical systems to diverse data directly, H-NDPs form a curriculum by learning local dynamical system-based policies on small regions in state-space and then distill them into a global dynamical system-based policy that operates only from high-dimensional images. H-NDPs additionally provide smooth trajectories, a strong safety benefit in the real world. We perform extensive experiments on dynamic tasks both in the real world (digit writing, scooping, and pouring) and simulation (catching, throwing, picking). We show that H-NDPs are easily integrated with both imitation as well as reinforcement learning setups and achieve state-of-the-art results. Video results at https://shikharbahl.github.io/hierarchical-ndps.
SNOW Revisited: Understanding When Ideal READ Transactions Are Possible

READ transactions that read data distributed across servers dominate the workloads of real-world distributed storage systems. The SNOW Theorem [13] stated that ideal READ transactions that have optimal latency and the strongest guarantees—i.e., “SNOW” READ transactions–are impossible in one specific setting that requires three or more clients: at least two readers and one writer. However, it left many open questions. We close all of these open questions with new impossibility results and new algorithms. First, we prove rigorously the result from [13] saying that it is impossible to have a READ transactions system that satisfies SNOW properties with three or more clients. The insight we gained from this proof led to teasing out the implicit assumptions that are required to state the results and also, resolving the open question regarding the possibility of SNOW with two clients. We show that it is possible to design an algorithm, where SNOW is possible in a multi-writer, single-reader (MWSR) setting when a client can send messages to other clients; on the other hand, we prove it is impossible to implement SNOW in a multi-writer, single-reader (MWSR) setting-which is more general than the two-client setting-when client-to-client communication is disallowed. We also correct the previous claim in [13] that incorrectly identified one existing system, Eiger [12], as supporting the strongest guarantees (SW) and whose read-only transactions had bounded latency. Thus, there were no previous algorithms that provided the strongest guarantees and had bounded latency. Finally, we introduce the first two algorithms to provide the strongest guarantees with bounded latency.
CLASSIFYING ISCHIAL TUBEROSITY AVULSION FRACTURES BY OSSIFICATION STAGE AND TENDON ATTACHMENT

Background:Currently, there is no classification system for ischial tuberosity avulsion fractures.Hypothesis/Purpose:To provide a new classification system for ischial tuberosity fractures based on...
Commuting integral and differential operators and the master symmetries of the Korteweg-de Vries equation

The singular value decomposition going with many problems in medical imaging, non-destructive testing, geophysics, etc. is of central importance. The singular functions are characterized as the eigenfunctions of a compact, selfadjoint integral operator with (as in the case of limited angle tomography) a certain number of signiﬁcantly nonzero eigenvalues. A stable reconstruction algorithm should aim at ﬁnding the projection of the unknown object on the linear span of the singular functions going with only these eigenvalues. If this linear span is not satisfactory for the spatial resolution that one wants to achieve, one needs to measure over a larger range of angles and compute the new singular value decomposition from scratch. Unfortunately the eﬀective numerical determination of the eigenfunctions of these integral operators, i.e. the singular functions in question, is a very ill-posed problem. The best known remedy to this problem goes back the work of Slepian, Landau and Pollak, Bell Labs 1960-1965. They found a mathematical miracle: one can produce a diﬀerential operator that shares these eigenfunctions and has a very spread out spectrum, resulting in a numerically stable problem. The search for other situations where this (albeit exceptional) miracle holds is the motivation of this paper. We show that the master symmetries of the Korteweg-de Vries equation give a way to extend the remarkable result of David Slepian in connection with the Bessel integral kernel and the existence of a diﬀerential operator that commutes with it. The original result of Slepian has already played an important role in signal processing as well as in Random matrix theory.
Notes on the Leonard System Classification

Around 2001 we classified the Leonard systems up to isomorphism. The proof was lengthy and involved considerable computation. In this paper we give a proof that is shorter and involves minimal computation. We also give a comprehensive description of the intersection numbers of a Leonard system.
Bounded-Rational Pursuit-Evasion Games

We present a framework that incorporates the principle of bounded rationality into a pursuit-evasion game between two aerial vehicles in a stochastic wind field. We initially formulate the problem as a continuous zero-sum stochastic game under perfect rationality. We then discretize the game via the Markov Chain Approximation Method. Leveraging the cognitive hierarchy theory (“level-k thinking”) we relax the perfect rationality assumption and compute the solution of the ensuing discrete game, while taking into consideration the rationality level of each agent. We also present an online algorithm to infer the rationality of the opponent, which enables the agents to deploy appropriate countermeasures. Finally, we verify the efficacy of this framework through simulations.
Synthesis with Asymptotic Resource Bounds

We present a method for synthesizing recursive functions that satisfy both a functional specification and an asymptotic resource bound. Prior methods for synthesis with a resource metric require the user to specify a concrete expression exactly describing resource usage, whereas our method uses big-O notation to specify the asymptotic resource usage. Our method can synthesize programs with complex resource bounds, such as a sort function that has complexity O(n log(n)). Our synthesis procedure uses a type system that is able to assign an asymptotic complexity to terms, and can track recurrence relations of functions. These typing rules are justified by theorems used in analysis of algorithms, such as the Master Theorem and the Akra-Bazzi method. We implemented our method as an extension of prior type-based synthesis work. Our tool, SynPlexity, was able to synthesize complex divide-and-conquer programs that cannot be synthesized by prior solvers.
Boundary layer models of the Hou-Luo scenario

Abstract Finite time blow up vs global regularity question for 3D Euler equation of fluid mechanics is a major open problem. Several years ago, Luo and Hou [16] proposed a new finite time blow up scenario based on extensive numerical simulations. The scenario is axi-symmetric and features fast growth of vorticity near a ring of hyperbolic points of the flow located at the boundary of a cylinder containing the fluid. An important role is played by a small boundary layer where intense growth is observed. Several simplified models of the scenario have been considered, all leading to finite time blow up [3] , [2] , [9] , [13] , [11] , [15] . In this paper, we propose two models that are designed specifically to gain insight in the evolution of fluid near the hyperbolic stagnation point of the flow located at the boundary. One model focuses on analysis of the depletion of nonlinearity effect present in the problem. Solutions to this model are shown to be globally regular. The second model can be seen as an attempt to capture the velocity field near the boundary to the next order of accuracy compared with the one-dimensional models such as [3] , [2] . Solutions to this model blow up in finite time.
The Essential Meaning of Executive Power

Scholars disagree about the Executive Power Clause’s meaning. Some scholars claim that the clause does not vest any power but instead establishes the title and number for the apex of the executive branch. Others believe that the clause vests power but are unsure what domestic power it might vest. This article seeks to resolve the dispute by shedding light on the essential, historical meaning of executive power. Although “executive power” was often used to encompass a bundle of powers typically enjoyed by the executive branch, such as foreign relations control and the power to appoint, the phrase most often was used as a shorthand for the power to execute the laws. Indeed, the phrase “executive power” comes from the principal or essential power of an executive—the power to execute the law. Evidence from the eighteenth century reveals this essential meaning of executive power. European political theorists of that era declared that the executive power was the power to execute the laws. In America, state constitutions and political commentators employed this basic definition. Debates at the Philadelphia and state ratifying conventions also confirm this meaning, with framers and ratifiers repeatedly observing that the President could execute the law by virtue of the executive power. Finally, after ratification, statesmen from all three branches understood that the executive power was the power to execute the laws. The founders understood that at least two subsidiary authorities flowed from the power to execute the law. Vested with the executive power, the president may execute any federal law by himself. Because the Constitution establishes that the executive power is his, the Constitution authorizes the president to execute any federal law. This constitutionally sanctioned power to execute the law explains why the president is widely regarded as an executive, i.e., an official who exe-
Visual Explanation for Deep Metric Learning

This work explores the visual explanation for deep metric learning and its applications. As an important problem for learning representation, metric learning has attracted much attention recently, while the interpretation of the metric learning model is not as well-studied as classification. To this end, we propose an intuitive idea to show where contributes the most to the overall similarity of two input images by decomposing the final activation. Instead of only providing the overall activation map of each image, we propose to generate point-to-point activation intensity between two images so that the relationship between different regions is uncovered. We show that the proposed framework can be directly applied to a wide range of metric learning applications and provides valuable information for model understanding. Both theoretical and empirical analyses are provided to demonstrate the superiority of the proposed overall activation map over existing methods. Furthermore, our experiments validate the effectiveness of the proposed point-specific activation map on two applications, i.e. cross-view pattern discovery and interactive retrieval. Code is available at https://github.com/Jeff-Zilence/Explain_Metric_Learning
Publication Benchmarking Data Based on Faculty Promoted at the Top 200 Worldwide Accounting Research Institutions

The primary purpose of this paper is to update and extend past promotion benchmarking studies. We update prior benchmarking studies by providing publication benchmarking data for faculty promoted between 2013 and 2019, inclusive. We extend prior benchmarking studies by evaluating a larger number of institutions (the top 200 worldwide accounting institutions), using a more global journal list, and providing sensitivity analyses between journal lists. We group institutions based on rankings and report a possible minimum publication hurdle for each group in each journal category. In addition, we provide data on the mix, or percentage, of publications in each journal category for each institution ranking group.
Why Perfect Tests May Not Be Worth Waiting For: Information as a Commodity

Information products provide agents with additional information that can be used to update actions. In many situations, access to such products can be quite limited. For instance, in epidemics, the...
Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction

Quantitative imaging in MRI usually involves acquisition and reconstruction of a series of images at multi-echo time points, which possibly requires more scan time and specific reconstruction technique compared to conventional qualitative imaging. In this work, we focus on optimizing the acquisition and reconstruction process of multi-echo gradient echo pulse sequence for quantitative susceptibility mapping as one important quantitative imaging method in MRI. A multi-echo sampling pattern optimization block extended from LOUPE-ST is proposed to optimize the k-space sampling patterns along echoes. Besides, a recurrent temporal feature fusion block is proposed and inserted into a backbone deep ADMM network to capture the signal evolution along echo time during reconstruction. Experiments show that both blocks help improve multi-echo image reconstruction performance.
Weakly Supervised Relative Spatial Reasoning for Visual Question Answering

Vision-and-language (V\&L) reasoning necessitates perception of visual concepts such as objects and actions, understanding semantics and language grounding, and reasoning about the interplay between the two modalities. One crucial aspect of visual reasoning is spatial understanding, which involves understanding relative locations of objects, i.e.\ implicitly learning the geometry of the scene. In this work, we evaluate the faithfulness of V\&L models to such geometric understanding, by formulating the prediction of pair-wise relative locations of objects as a classification as well as a regression task. Our findings suggest that state-of-the-art transformer-based V\&L models lack sufficient abilities to excel at this task. Motivated by this, we design two objectives as proxies for 3D spatial reasoning (SR) -- object centroid estimation, and relative position estimation, and train V\&L with weak supervision from off-the-shelf depth estimators. This leads to considerable improvements in accuracy for the "GQA" visual question answering challenge (in fully supervised, few-shot, and O.O.D settings) as well as improvements in relative spatial reasoning. Code and data will be released \href{this https URL}{here}.
TriPoll: computing surveys of triangles in massive-scale temporal graphs with metadata

Understanding the higher-order interactions within network data is a key objective of network science. Surveys of metadata triangles (or patterned 3-cycles in metadata-enriched graphs) are often of interest in this pursuit. In this work, we develop TriPoll, a prototype distributed HPC system capable of surveying triangles in massive graphs containing metadata on their edges and vertices. We contrast our approach with much of the prior effort on triangle analysis, which often focuses on simple triangle counting, usually in simple graphs with no metadata. We assess the scalability of TriPoll when surveying triangles involving metadata on real and synthetic graphs with up to hundreds of billions of edges. We utilize communication-reducing optimizations to demonstrate a triangle counting task on a 224 billion edge web graph in approximately half of the time of competing approaches, while additionally supporting metadata-aware capabilities.
Early Soviet Advertising: "We Have to Extract All the Stinking Bourgeois Elements"

This article unveils the roots of Soviet advertising and the sources for its inspiration, some of which resided in Russian revolutionary visual propaganda as well as in capitalist advertising. It a...
Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation

This paper presents a computational framework that generates ensemble predictive mechanics models with uncertainty quantification (UQ). We first develop a causal discovery algorithm to infer causal relations among time-history data measured during each representative volume element (RVE) simulation through a directed acyclic graph. With multiple plausible sets of causal relationships estimated from multiple RVE simulations, the predictions are propagated in the derived causal graph while using a deep neural network equipped with dropout layers as a Bayesian approximation for UQ. We select two representative numerical examples (traction-separation laws for frictional interfaces, elastoplasticity models for granular assembles) to examine the accuracy and robustness of the proposed causal discovery method for the common material law predictions in civil engineering applications.
Interpreting Internal Consistency of DES Measurements

Bayesian evidence ratios are widely used to quantify the statistical consistency between different experiments. However, since the evidence ratio is prior dependent, the precise translation between its value and the degree of concordance/discordance requires additional information. The most commonly adopted metric, the Jeffreys scale, can falsely suggest agreement between datasets when priors are chosen to be sufficiently wide. In this work, we examine evidence ratios in a DES-Y1 simulated analysis, focusing on the internal consistency between weak lensing and galaxy clustering. We study two scenarios using simulated data in controlled experiments. First, we calibrate the expected evidence ratio distribution given noise realizations around the best fit DES-Y1 $\Lambda$CDM cosmology. Second, we show the behavior of evidence ratios for noiseless fiducial data vectors simulated using a modified gravity model, which generates internal tension in the $\Lambda$CDM analysis. We show that the choice of prior could conceal the discrepancies between weak lensing and galaxy clustering induced by such models and that the evidence ratio in a DES-Y1 study is, indeed, biased towards agreement.
Exhaustive goodness-of-fit via smoothed inference and graphics

Classical tests of goodness-of-fit aim to validate the conformity of a postulated model to the data under study. Given their inferential nature, they can be considered a crucial step in confirmatory data analysis. In their standard formulation, however, they do not allow exploring how the hypothesized model deviates from the truth nor do they provide any insight into how the rejected model could be improved to better fit the data. The main goal of this work is to establish a comprehensive framework for goodness-of-fit which naturally integrates modeling, estimation, inference, and graphics. Modeling and estimation focus on a novel formulation of smooth tests that easily extends to arbitrary distributions, either continuous or discrete. Inference and adequate post-selection adjustments are performed via a specially designed smoothed bootstrap and the results are summarized via an exhaustive graphical tool called CD-plot.
Prenatal exposure to ambient air pollution and traffic and indicators of adiposity in early childhood: the Healthy Start study.

BACKGROUND Prenatal exposure to ambient air pollution and traffic have been related to a lower birth weight and may be associated with greater adiposity in childhood. We aimed to examine associations of maternal exposure to ambient air pollution and traffic during pregnancy with indicators of adiposity in early childhood. METHODS We included 738 participants of the Colorado-based Healthy Start study whose height, weight, waist circumference and/or fat mass were measured at age 4-6 years. We estimated residential exposure to ambient concentrations of fine particulate matter (PM2.5) and ozone (O3) averaged by trimester and throughout pregnancy via inverse distance-weighted interpolation of central site monitoring data. We assessed the distance to the nearest major roadway and traffic density in multiple buffers surrounding the participants' homes. Associations of prenatal exposure to air pollution and traffic with overweight, waist circumference, percent fat mass and fat mass index (FMI) were assessed by logistic and linear regression. RESULTS Associations of exposure to PM2.5 and O3 at the residential address during pregnancy with percent fat mass and FMI at age 4-6 years were inconsistent across trimesters. For example, second trimester PM2.5 was associated with a higher percent fat mass (adjusted difference 0.70% [95% CI 0.05, 1.35%] per interquartile range (IQR; 1.3 µg/m3) increase), while third trimester PM2.5 was associated with a lower percent fat mass (adjusted difference -1.17% [95% CI -1.84, -0.50%] per IQR (1.3 µg/m3) increase). Residential proximity to a highway during pregnancy was associated with higher odds of being overweight at age 4-6 years. We observed no associations of prenatal exposure to PM2.5 and O3 with overweight and waist circumference. CONCLUSIONS We found limited evidence of associations of prenatal exposure to ambient PM2.5 and O3 with indicators of adiposity at age 4-6 years. Suggestive relationships between residential proximity to a highway during pregnancy and greater adiposity merit further investigation.
Smart Village: An IoT Based Digital Transformation

Almost 46% of the world’s population resides in a rural landscape. Smart villages, alongside smart cities, are in need of time for future economic growth, improved agriculture, better health, and education. The smart village is a concept that improves the traditional rural aspects with the help of digital transformation. The smart village is built up using heterogeneous digital technologies pillared around the Internet-of-Thing (IoT). There exist many opportunities in research to design a low-cost, secure, and efficient technical ecosystem. This article identifies the key application areas, where the IoT can be applied in the smart village. The article also presents a comparative study of communication technology options.
Second-Order Neural ODE Optimizer

We propose a novel second-order optimization framework for training the emerging deep continuous-time models, specifically the Neural Ordinary Differential Equations (Neural ODEs). Since their training already involves expensive gradient computation by solving a backward ODE, deriving efficient second-order methods becomes highly nontrivial. Nevertheless, inspired by the recent Optimal Control (OC) interpretation of training deep networks, we show that a specific continuous-time OC methodology, called Differential Programming, can be adopted to derive backward ODEs for higher-order derivatives at the same O(1) memory cost. We further explore a low-rank representation of the second-order derivatives and show that it leads to efficient preconditioned updates with the aid of Kronecker-based factorization. The resulting method converges much faster than first-order baselines in wall-clock time, and the improvement remains consistent across various applications, e.g. image classification, generative flow, and time-series prediction. Our framework also enables direct architecture optimization, such as the integration time of Neural ODEs, with second-order feedback policies, strengthening the OC perspective as a principled tool of analyzing optimization in deep learning.