cssi/SciSciGPT-SciSciNet huggingface dataset:
Joint Concordance Index

Existing metrics in competing risks survival analysis such as concordance and accuracy do not evaluate a model’s ability to jointly predict the event type and the event time. To address these limitations, we propose a new metric, which we call the joint concordance. The joint concordance measures a model’s ability to predict the overall risk profile, i.e., risk of death from different event types. We develop a consistent estimator for the new metric that accounts for the censoring bias. We use the new metric to develop a variable importance ranking approach. Using the real and synthetic data experiments, we show that models selected using the existing metrics are worse than those selected using joint concordance at jointly predicting the event type and event time. We show that the existing approaches for variable importance ranking often fail to recognize the importance of the event-specific risk factors, whereas, the proposed approach does not, since it compares risk factors based on their contribution to the prediction of the different event-types. To summarize, joint concordance is helpful for model comparisons and variable importance ranking and has the potential to impact applications such as risk-stratification and treatment planning in multimorbid populations.
Prediction of Individual Progression Rate in Parkinson’s Disease Using Clinical Measures and Biomechanical Measures of Gait and Postural Stability

Parkinson’s disease (PD) is a common neurological disorder characterized by gait impairment. PD has no cure, and an impediment to developing a treatment is the lack of any accepted method to predict disease progression rate. The primary aim of this study was to develop a model using clinical measures and biomechanical measures of gait and postural stability to predict an individual’s PD progression over two years. Data from 160 PD subjects were utilized. Machine learning models, including XGBoost and Feed Forward Neural Networks, were developed using extensive model optimization and cross-validation. The highest performing model was a neural network that used a group of clinical measures, achieved a PPV of 71% in identifying fast progressors, and explained a large portion (37%) of the variance in an individual’s progression rate on held-out test data. This demonstrates the potential to predict individual PD progression rate and enrich trials by analyzing clinical and biomechanical measures with machine learning.
Using a WLFSR to Embed Test Pattern Pairs in Minimum Time

We propose a methodology for reducing the number of test cycles needed by a Weighted LFSR (WLFSR) to reproduce a 2P × W test matrix T of P pattern pairs. The methodology introduces a very small number δ of extra cells into the WLFSR and uses appropriate combinational mapping logic in order to make the time be equal to that required by a (W + δ)-bit WLFSR to generate vectors containing the W bits of the first pattern for each pair plus the δ extra bits. We present an algorithm that makes the value of δ be less than or equal to ⌈log2λ⌉, where λ is the size of the maximum subset of pairs in T with identical first patterns. This is a significant improvement over the time E P,W · P required by a trivial approach that uses a WLFSR with W cells to generate the first patterns of the pairs and a P × W ROM to store the second patterns of the pairs. Experimental results on the application of the methodology to the embedding of test matrices for path delay faults are particularly encouraging, even for very large numbers of test pattern pairs that are necessary for provably high fault coverage.
Vasoreactivity of the Murine External Jugular Vein and Carotid Artery

INTRODUCTION Impaired venous reactivity has potential to contribute to clinically significant pathologies such as arteriovenous fistula (AVF) maturation failure. Vascular segments commonly used in murine preclinical models of AVF include the carotid artery and external jugular vein. Detailed descriptions of isometric procedures to evaluate function of murine external jugular vein ex vivo have not been previously published. OBJECTIVE To establish isometric procedures to measure naive murine external jugular vein reactivity ex vivo. METHODS Vasomotor responses of external jugular veins and ipsilateral common carotid arteries from C57BL/6 mice were evaluated using isometric tension procedures. RESULTS External jugular veins developed tension (p < 0.05) to potassium chloride and U-46619, but not to phenylephrine, whereas common carotid arteries responded to all 3 agents (p < 0.05). While maximal responses to acetylcholine (ACh) were similar between the venous and arterial segments, the dose required to achieve this value was lower (p < 0.05) in the artery versus vein. Nitric oxide synthase inhibition attenuated (p < 0.05) but did not abolish ACh-evoked vasorelaxation in both vascular segments, whereas cyclooxygenase blockade had no effect. Endothelium-independent vasorelaxation to sodium nitroprusside was similar in the artery and vein. CONCLUSION Vasorelaxation and vasocontraction can be reliably assessed in the external jugular vein in C57BL/6 mice using isometric procedures.
Random Forest Models for Accurate Identification of Coordination Environments from X-Ray Absorption Near-Edge Structure

Analyzing coordination environments using X-ray absorption spectroscopy has broad applications in solid-state physics and material chemistry. Here, we show that random forest models trained on 190,000 K-edge X-ray absorption near-edge structure (XANES) spectra can identify the main atomic coordination environment with a high accuracy of 85.4% and all associated coordination environments with a high Jaccard score of 81.8% for 33 cation elements in oxides, significantly outperforming other machine-learning models. In a departure from prior works, the coordination environment is described as a distribution over 25 distinct coordination motifs with coordination numbers ranging from 1 to 12. More importantly, we show that the random forest models can be used to predict coordination environments from experimental K-edge XANES with minimal loss in accuracy. A drop-variable feature importance analysis highlights the key roles that the pre-edge and main-peak regions play in coordination environment identification.
Haptic Assistance That Restricts the Use of Redundant Solutions is Detrimental to Motor Learning

Understanding the use of haptic assistance to facilitate motor learning is a critical issue, especially in the context of tasks requiring control of motor variability. However, the question of how haptic assistance should be designed in tasks with redundancy, where multiple solutions are available, is currently unknown. Here we examined the effect of haptic assistance that either allowed or restricted the use of redundant solutions on the learning of a bimanual steering task. 60 college-aged participants practiced steering a single cursor placed in between their hands along a smooth W-shaped track of a certain width as quickly as possible. Haptic assistance was either applied at (i) the ‘task’ level using a force channel that only constrained the cursor to the track, allowing for the use of different hand trajectories, or (ii) the ‘individual effector’ level using a force channel that constrained each hand to a specific trajectory. In addition, we also examined the effect of simply ‘fading’ assistance in a linear fashion– i.e., decreasing force gains with practice to reduce dependence on haptic assistance. Results showed all groups improved with practice - however, groups with haptic assistance at the individual effector level performed worse than those at the task level. Besides, we did not find sufficient evidence for the benefits of linearly fading assistance in our task. Overall, the results suggest that haptic assistance is not effective for motor learning when it restricts the use of redundant solutions.
DataFed: Towards Reproducible Research via Federated Data Management

The increasingly collaborative, globalized nature of scientific research combined with the need to share data and the explosion in data volumes present an urgent need for a scientific data management system (SDMS). An SDMS presents a logical and holistic view of data that greatly simplifies and empowers data organization, curation, searching, sharing, dissemination, etc. We present DataFed - a lightweight, distributed SDMS that spans a federation of storage systems within a loosely-coupled network of scientific facilities. Unlike existing SDMS offerings, DataFed uses high-performance and scalable user management and data transfer technologies that simplify deployment, maintenance, and expansion of DataFed. DataFed provides web-based and command-line interfaces to manage data and integrate with complex scientific workflows. DataFed represents a step towards reproducible scientific research by enabling reliable staging of the correct data at the desired environment.
Endogenous and exogenous control of visuospatial selective attention in freely behaving mice.

Visuospatial selective attention has been investigated primarily in head-fixed animals and almost exclusively in primates. Here, we develop two human-inspired, discrimination-based behavioral paradigms for studying selective visuospatial attention in freely behaving mice. In the ‘spatial probability’ task, we find enhanced accuracy, sensitivity, and rate of evidence accumulation at the location with higher probability of target occurrence, and opposite effects at the lower probability location. Together with video-based 3D head-tracking, these results demonstrate endogenous expectation-driven shifts of spatial attention. In the ‘flanker’ task, we find that a second stimulus presented with the target, but with conflicting information, causes switch-like decrements in accuracy and sensitivity as a function of its contrast, and slower evidence accumulation, demonstrating exogenous capture of spatial attention. The ability to study primate-like selective attention rigorously in unrestrained mice opens a rich avenue for research into neural circuit mechanisms underlying this critical executive function in a naturalistic setting. The authors describe behavioural tasks for the study of primate-like, endogenous and exogenous control of visuospatial selective attention in freely behaving mice.
Team Players: How Social Skills Improve Group Performance

Most jobs require teamwork. Are some people good team players? In this paper we design and test a new method for identifying individual contributions to group performance. We randomly assign people to multiple teams and predict team performance based on previously assessed individual skills. Some people consistently cause their group to exceed its predicted performance. We call these individuals “team players”. Team players score significantly higher on a well-established measure of social intelligence, but do not differ across a variety of other dimensions, including IQ, personality, education and gender. Social skills – defined as a single latent factor that combines social intelligence scores with the team player effect – improve group performance about as much as IQ. We find suggestive evidence that team players increase effort among teammates.
Caring for memories.

Bereavement is a somewhat misleading term for the experience of loss of an intimate person in one’s life. It suggests that the loss itself is the whole experience. But grieving is about yearning as...
When The Wrong People Are Immune

After a disaster such as the COVID-19 pandemic, there will be an irresistible desire to blame others Despite documented failures in the federal government’s response to the pandemic, injured individuals will not be able to hold it accountable due to the broad application of governmental immunity Congress and state governments have provided targeted immunity to various device manufacturers and emergency volunteers However, the one group with huge targets on their backs are individual physicians, who are often making impossible choices that are reasonable at the time, but might not appear reasonable to a jury after the fact, and with the bias of hindsight Recognizing that the potential for liability might cause undue psychological stress on health care providers, this essay argues for statutory immunity that protects them from rationing and other health care decisions that are made in good faith, and that are in compliance with documented state, institutional, or professional pandemic-response guidelines
Towards a Public Key Management Framework for Virtual Assets and Virtual Asset Service Providers.

The recent Financial Action Task Force (FATF) Recommendations define virtual assets and virtual asset service providers (VASPs), and require under the Travel Rule that originating VASPs obtain and ...
Authoritarian energy transitions undermined? Environmental governance cycles in China’s power sector

Abstract We develop a theory to explain the persistence of tensions between decentralized delegation and centralized control of environmental governance in authoritarian regimes. Economic benefits from decentralization – information, competition, and efficiency – conflict with environmental goals of centralized policy harmonization and management of inter-jurisdictional externalities. Decentralization to local government actors can facilitate economic growth but also empower them in ways that undermine environmental governance. Persistent tensions between decentralized and centralized imperatives generate cycles in environmental and energy systems governance. We test our theory of authoritarian environmental governance cycles using the case of China’s power sector, drawing on evidence from primary source documents, field interviews, and multiple data sources on the development and distribution of energy generating capacity. We focus on two policy areas – coal-fired power and wind energy – that are integral to central government efforts to improve the quality of environmental governance. This research explains the puzzling alternations in the locus of governance, and contributes to understanding inter-governmental relations and environmental politics in authoritarian regimes.
Topological theory of physical fields

We study the topology associated with physical vector and scalar fields. A mathematical object, e.g., a ball, can be continuously deformed, without tearing or gluing, to make other topologically equivalent objects, e.g., a cube or a solid disk. If tearing or gluing get involved, i.e., the deformation is not continuous anymore, the initial topology will consequently change giving rise to a topologically distinct object, e.g., a torus. This simple concept in general topology may be employed in the study of physical systems described by fields. Instead of continuously deforming objects, we can take a continuously evolving field, with an appropriately defined topology, such that the topology remains unchanged in time unless the system undergoes an important physical change, e.g., a transition to a different energy state. For instance, a sudden change in the magnetic topology in an energetically relaxing plasma, a process called reconnection, strongly affects the dynamics, e.g., it is involved in launching solar flares and generating large scale magnetic fields in astrophysical objects. In this topological formalism, the magnetic topology in a plasma can spontaneously change due to the presence of dissipative terms in the induction equation which break its time symmetry. We define a topology for the vector field $\bf F$ in the phase space $(\bf x, F)$. As for scalar fields represented by a perfect fluid, e.g., the inhomogeneous inflaton or Higgs fields, the fluid velocity $\bf u$ defines the corresponding topology. The vector field topology in its corresponding phase space $(\bf x, F)$ will be preserved in time if certain conditions including time reversal invariance are satisfied by the field and its governing differential equation.
Correlated Binary Data

Correlated binary data may arise in longitudinal or spatial studies or through some other form of clustering of sampling units. The simplest setting, with clusters of size two and no covariates, leads to familiar methods of analysis. Models with covariates include response feature models, conditionally specified models, transitional models, marginal models, and cluster-specific models. These may lead to different interpretations of the regression parameters. Keywords: longitudinal; matched pairs; response feature; conditional; transitional; marginal; cluster-specific; missing data; generalized estimating equations; generalized linear model
Environmental Performance Index

The Pilot 2006 Environmental Performance Index (EPI) is an aggregate measure of a country's policy results to achieve 16 performance targets in protecting human environmental health and maintaining ecological vitality. The objective underlying the EPI is to move the environmental debate from emotional and rhetoric arguments to more data- and evidence-based action that facilitates performance tracking and accountability of decision makers. The EPI thereby helps to fill a significant gap in environmental policy making and supports the international community's ongoing effort to curb extreme poverty and promote human development through an ambitious measurement initiative called the Millennium Development Goals. Keywords: index; indicator; environmental performance; environmental policy making; Millennium Development Goals
The Highest Price Ever: The Great NYSE Seat Sale of 1928-1929 and Capacity Constraints

During the 1920s the New York Stock Exchange's position as the dominant American exchange was eroding. Costs to customers, measured as bid-ask spreads, spiked when surging inflows of orders collided with the constraint created by a fixed number of brokers. The NYSE's management proposed and the membership approved a 25 percent increase in the number of seats by issuing a quarter-seat dividend to all members. An event study reveals that the aggregate value of the NYSE rose in anticipation of improved competitiveness. These expectations were justified as bid-ask spreads became less sensitive to peak volume days.
The Indigenous Decade in Review

This Article considers the decade, 2010 to 2019, in respect to indigenous peoples in the United States. The degree of invisibility of indigenous peoples, in spite of the existence of 574 federally recognized tribes with political status, is a central issue in major cases and events of the decade. Land and environment, social concerns, and collective identity are the three areas through which this Article considers the decade. The Declaration on the Rights of Indigenous Peoples, endorsed in 2010, sets a measure for the nation-state’s engagement with indigenous peoples possessed of self-determination. The criticality of a new place in the American consciousness for the political status of indigenous peoples in the United States going forward is a feature of the decade.
Synthesis of an Oxathiolane Drug Substance Intermediate Guided by Constraint-Driven Innovation.

A new route was developed for construction of the oxathiolane intermediate used in the synthesis of lamivudine (3TC) and emtricitabine (FTC). We developed the presented route by constraining oursel...
AlignNet: A Unifying Approach to Audio-Visual Alignment

We present AlignNet, a model that synchronizes videos with reference audios undernon-uniform and irregularmis- alignments. AlignNet learns the end-to-end dense correspondence between each frame of a video and an audio. Our method is designed according to simple and well- established principles: attention, pyramidal processing, warping, and affinity function. Together with the model, we release a dancing dataset Dance50 for training and evaluation. Qualitative, quantitative and subjective evaluation results on dance-music alignment and speech-lip alignment demonstrate that our method far outperforms the state-of- the-art methods. Code, dataset and sample videos are available at our project page1.
A non-linear adiabatic theorem for the one-dimensional Landau-Pekar equations

Abstract We discuss a one-dimensional version of the Landau–Pekar equations, which are a system of coupled differential equations with two different time scales. We derive an approximation on the slow time scale in the spirit of a non-linear adiabatic theorem. Dispersive estimates for solutions of the Schrodinger equation with time-dependent potential are a key technical ingredient in our proof.
An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines

The extraction of anglicisms (lexical borrowings from English) is relevant both for lexicographic purposes and for NLP downstream tasks. We introduce a corpus of European Spanish newspaper headlines annotated with anglicisms and a baseline model for anglicism extraction. In this paper we present: (1) a corpus of 21,570 newspaper headlines written in European Spanish annotated with emergent anglicisms and (2) a conditional random field baseline model with handcrafted features for anglicism extraction. We present the newspaper headlines corpus, describe the annotation tagset and guidelines and introduce a CRF model that can serve as baseline for the task of detecting anglicisms. The presented work is a first step towards the creation of an anglicism extractor for Spanish newswire.
Continuous-time Lower Bounds for Gradient-based Algorithms

This article derives lower bounds on the convergence rate of continuous-time gradient-based optimization algorithms. The algorithms are subjected to a time-normalization constraint that avoids a reparametrization of time in order to make the discussion of continuous-time convergence rates meaningful. We reduce the multi-dimensional problem to a single dimension, recover well-known lower bounds from the discrete-time setting, and provide insight into why these lower bounds occur. We present algorithms that achieve the proposed lower bounds, even when the function class under consideration includes certain nonconvex functions.
Real-Time Dispatching of Large-Scale Ride-Sharing Systems: Integrating Optimization, Machine Learning, and Model Predictive Control

This paper considers the dispatching of large-scale real-time ride-sharing systems to address congestion issues faced by many cities. The goal is to serve all customers (service guarantees) with a small number of vehicles while minimizing waiting times under constraints on ride duration. This paper proposes an end-to-end approach that tightly integrates a state-of-the-art dispatching algorithm, a machine-learning model to predict zone-to-zone demand over time, and a model predictive control optimization to relocate idle vehicles. Experiments using historic taxi trips in New York City indicate that this integration decreases average waiting times by about 30% over all test cases and reaches close to 55% on the largest instances for high-demand zones.
Mining Archive.org’s Twitter Stream Grab for Pharmacovigilance Research Gold

In the last few years, Twitter has become an important resource for the identification of Adverse Drug Reactions (ADRs), monitoring flu trends, and other pharmacovigilance and general research applications. Most researchers spend their time crawling Twitter, buying expensive pre-mined datasets, or tediously and slowly building datasets using the limited Twitter API. However, there are a large number of datasets that are publicly available to researchers that are underutilized or unused. In this work, we demonstrate how we mined over 9.4 billion Tweets from archive.org’s Twitter stream grab using a drug-term dictionary and plenty of computing power. Knowing that not everything that shines is gold, we used pre-existing drug-related datasets to build machine learning models to filter our findings for relevance. In this work, we present our methodology and the 3,346,758 identified tweets for public use in future research.
The Weighted Euler Curve Transform for Shape and Image Analysis

The Euler Curve Transform (ECT) of Turner et al.\ is a complete invariant of an embedded simplicial complex, which is amenable to statistical analysis. We generalize the ECT to provide a similarly convenient representation for weighted simplicial complexes, objects which arise naturally, for example, in certain medical imaging applications. We leverage work of Ghrist et al.\ on Euler integral calculus to prove that this invariant---dubbed the Weighted Euler Curve Transform (WECT)---is also complete. We explain how to transform a segmented region of interest in a grayscale image into a weighted simplicial complex and then into a WECT representation. This WECT representation is applied to study Glioblastoma Multiforme brain tumor shape and texture data. We show that the WECT representation is effective at clustering tumors based on qualitative shape and texture features and that this clustering correlates with patient survival time.
The Complexity of Interactively Learning a Stable Matching by Trial and Error

In a stable matching setting, we consider a query model that allows for an interactive learning algorithm to make precisely one type of query: proposing a matching, the response to which is either that the proposed matching is stable, or a blocking pair (chosen adversarially) indicating that this matching is unstable. For one-to-one matching markets, our main result is an essentially tight upper bound of O(n2 log n) on the deterministic query complexity of interactively learning a stable matching in this coarse query model, along with an efficient randomized algorithm that achieves this query complexity with high probability. For many-to-many matching markets in which participants have responsive preferences, we first give an interactive learning algorithm whose query complexity and running time are polynomial in the size of the market if the maximum quota of each agent is bounded; our main result for many-to-many markets is that the deterministic query complexity can be made polynomial (more specifically, O(n3 log n)) in the size of the market even for arbitrary (e.g., linear in the market size) quotas. The high-level idea for our results is as follows. In each iteration, the algorithm chooses a "speculative" preference order for each agent, consistent with all observed blocking pairs so far. It then computes a stable matching with respect to these speculative preferences. When a blocking pair is revealed, this pins down at least one "mistake" (a transposition) in one agent's speculative order. The crux of the analysis is to show that a judicious choice of speculative orders ensures that any such mistake rules out a constant fraction of all remaining candidate orders for this agent. Such a choice can be made for the one-to-one setting by Yu's Proportional Transitivity Theorem. For the many-to-many setting, we obtain a slightly weaker bound via a direct proof. In order to achieve computational efficiency, the algorithm must be able to infer the following for each triple of agents x, y, z: what fraction of preference orders for x consistent with all past observations rank y ahead of z? We show that this quantity can be computed sufficiently accurately by sampling linear extensions of a partial order (nearly) uniformly at random. Whether computational efficiency can be achieved for the more complex many-to-many setting is an appealing open question.
Data and Incentives

Many firms, such as banks and insurers, condition their level of service on a consumer's perceived "quality," for instance their creditworthiness. Increasingly, firms have access to consumer segmentations derived from auxiliary data on behavior, and can link outcomes across individuals in a segment for prediction. How does this practice affect consumer incentives to exert (socially-valuable) effort, e.g. to repay loans? We show that the impact of an identified linkage on behavior and welfare depends crucially on the structure of the linkage---namely, whether the linkage reflects quality (via correlations in types) or a shared circumstance (via common shocks to observed outcomes).
FRaZ: A Generic High-Fidelity Fixed-Ratio Lossy Compression Framework for Scientific Floating-point Data

With ever-increasing volumes of scientific floating-point data being produced by high-performance computing applications, significantly reducing scientific floating-point data size is critical, and error-controlled lossy compressors have been developed for years. None of the existing scientific floating-point lossy data compressors, however, support effective fixed-ratio lossy compression. Yet fixed-ratio lossy compression for scientific floating-point data not only compresses to the requested ratio but also respects a user-specified error bound with higher fidelity. In this paper, we present FRaZ: a generic fixed-ratio lossy compression framework respecting user-specified error constraints. The contribution is twofold. (1) We develop an efficient iterative approach to accurately determine the appropriate error settings for different lossy compressors based on target compression ratios. (2) We perform a thorough performance and accuracy evaluation for our proposed fixed-ratio compression framework with multiple state-of-the-art error-controlled lossy compressors, using several real-world scientific floating-point datasets from different domains. Experiments show that FRaZ effectively identifies the optimum error setting in the entire error setting space of any given lossy compressor. While fixed-ratio lossy compression is slower than fixed-error compression, it provides an important new lossy compression technique for users of very large scientific floating-point datasets.
A tertiary center experience of multiple myeloma patients with COVID-19: lessons learned and the path forward.

The COVID-19 pandemic, caused by SARS-CoV-2 virus, has resulted in over 100,000 deaths in the USA. Our institution has treated over 2000 COVID-19 patients during the pandemic in New York City. The pandemic directly impacted cancer patients and the organization of cancer care. Mount Sinai Hospital has a large and diverse multiple myeloma (MM) population. Herein, we report the characteristics of COVID-19 infection and serological response in MM patients in a large tertiary care institution in New York. We performed a retrospective study on a cohort of 58 patients with a plasma-cell disorder (54 MM, 4 smoldering MM) who developed COVID-19 between March 1, 2020, and April 30, 2020. We report epidemiological, clinical, and laboratory characteristics including the persistence of viral detection by polymerase chain reaction (PCR) and anti-SARS-CoV-2 antibody testing, treatments initiated, and outcomes. Of the 58 patients diagnosed with COVID-19, 36 were hospitalized and 22 were managed at home. The median age was 67 years; 52% of patients were male and 63% were non-White. Hypertension (64%), hyperlipidemia (62%), obesity (37%), diabetes mellitus (28%), chronic kidney disease (24%), and lung disease (21%) were the most common comorbidities. In the total cohort, 14 patients (24%) died. Older age (> 70 years), male sex, cardiovascular risk, and patients not in complete remission (CR) or stringent CR were significantly (p < 0.05) associated with hospitalization. Among hospitalized patients, laboratory findings demonstrated elevation of traditional inflammatory markers (CRP, ferritin, D-dimer) and a significant (p < 0.05) association between elevated inflammatory markers, severe hypogammaglobulinemia, non-White race, and mortality. Ninety-six percent (22/23) of patients developed antibodies to SARS-CoV-2 at a median of 32 days after initial diagnosis. The median time to PCR negativity was 43 (range 19–68) days from initial positive PCR. Drug exposure and MM disease status at the time of contracting COVID-19 had no bearing on mortality. Mounting a severe inflammatory response to SARS-CoV-2 and severe hypogammaglobulinemia was associated with higher mortality. The majority of patients mounted an antibody response to SARS-CoV-2. These findings pave a path to the identification of vulnerable MM patients who need early intervention to improve outcomes in future outbreaks of COVID-19.
Finite-Time Stability of Discrete Autonomous Systems

Finite-time stability involves dynamical systems whose trajectories converge to a Lyapunov stable equilibrium state in finite time. In this paper, we address finite time stability of discrete-time dynamical systems. Specifically, we show that finite time stability leads to uniqueness of solutions in forward time. Furthermore, we provide Lyapunov and converse Lyapunov theorems for finite-time stability of discrete autonomous systems involving scalar difference fractional inequalities and minimum operators. In addition, lower semicontinuity of the settling-time function capturing the finite settling time behavior of the dynamical system is studied and illustrated through several examples. In particular, it is shown that the regularity properties of the Lyapunov function and those of the settlingtime function are related. Consequently, converse Lyapunov theorems for finite time stability of discrete-time systems can only assure the existence of lower semicontinuous Lyapunov functions.
Prescribed-time Convergence with Input Constraints: A Control Lyapunov Function Based Approach

In this paper, we present a control framework for a general class of control-affine nonlinear systems under spatiotemporal and input constraints. Specifically, the proposed control architecture addresses the problem of reaching a given final set S in a prescribed (user-defined) time with bounded control inputs. To this end, a time transformation technique is utilized to transform the system subject to temporal constraints into an equivalent form without temporal constraints. The transformation is defined so that asymptotic convergence in the transformed time scale results into prescribed-time convergence in the original time scale. To incorporate input constraints, we characterize a set of initial conditions D M such that starting from this set, the closed-loop trajectories reach the set S within the prescribed time. We further show that starting from outside the set D M , the system trajectories reach the set D M in a finite time that depends upon the initial conditions and the control input bounds. We use a novel parameter μ in the controller, that controls the convergence-rate of the closed-loop trajectories and dictates the size of the set D M . Finally, we present a numerical example to showcase the efficacy of our proposed method.
On CDCL-based proof systems with the ordered decision strategy

We prove that CDCL SAT-solvers with the ordered decision strategy and the DECISION learning scheme are equivalent to ordered resolution. We also prove that, by replacing this learning scheme with its opposite, which learns the first possible non-conflict clause, they become equivalent to general resolution. In both results, we allow nondeterminism in the solver’s ability to perform unit propagation, conflict analysis, and restarts in a way that is similar to previous works in the literature. To aid the presentation of our results, and possibly future research, we define a model and language for CDCL-based proof systems – particularly those with nonstandard features – that allow for succinct and precise theorem statements.
A comprehensive review of deep learning applications in hydrology and water resources

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research that incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources.
Linear Models are Most Favorable among Generalized Linear Models

We establish a nonasymptotic lower bound on the L 2 minimax risk for a class of generalized linear models. It is further shown that the minimax risk for the canonical linear model matches this lower bound up to a universal constant. Therefore, the canonical linear model may be regarded as most favorable among the considered class of generalized linear models (in terms of minimax risk). The proof makes use of an information-theoretic Bayesian Cramer-Rao bound for log-concave priors, established by Aras et al. (2019).
Polylidar3D-Fast Polygon Extraction from 3D Data.

Flat surfaces captured by 3D point clouds are often used for localization, mapping, and modeling. Dense point cloud processing has high computation and memory costs making low-dimensional representations of flat surfaces such as polygons desirable. We present Polylidar3D, a non-convex polygon extraction algorithm which takes as input unorganized 3D point clouds (e.g., LiDAR data), organized point clouds (e.g., range images), or user-provided meshes. Non-convex polygons represent flat surfaces in an environment with interior cutouts representing obstacles or holes. The Polylidar3D front-end transforms input data into a half-edge triangular mesh. This representation provides a common level of abstraction for subsequent back-end processing. The Polylidar3D back-end is composed of four core algorithms: mesh smoothing, dominant plane normal estimation, planar segment extraction, and finally polygon extraction. Polylidar3D is shown to be quite fast, making use of CPU multi-threading and GPU acceleration when available. We demonstrate Polylidar3D’s versatility and speed with real-world datasets including aerial LiDAR point clouds for rooftop mapping, autonomous driving LiDAR point clouds for road surface detection, and RGBD cameras for indoor floor/wall detection. We also evaluate Polylidar3D on a challenging planar segmentation benchmark dataset. Results consistently show excellent speed and accuracy.
Cohomological localization for transverse Lie algebra actions on Riemannian foliations

Abstract We prove localization and integration formulas for the equivariant basic cohomology of Riemannian foliations. As a corollary we obtain a Duistermaat–Heckman theorem for transversely symplectic foliations.
Exploiting Self-Capacitances for Wireless Power Transfer

Conventional approaches for wireless power transfer rely on the mutual coupling (near-field or far-field) between the transmitter and receiver transducers. As a result, the power-transfer efficiency of these approaches scales non-linearly with the cross-sectional area of the transducers and with the relative distance and respective alignment between the transducers. In this paper we show that when the operational power-budget requirements are in the order of microwatts, a self-capacitance (SC) based power delivery has significant advantages in terms of power transfer-efficiency (PTE), receiver form-factor and system scalability when compared to other modes of wireless power transfer (WPT) methods. We present a simple and a tractable equivalent circuit model that can be used to study the effect of different parameters on the SC-based WPT. In this paper we have experimentally verified the validity of the circuit using a cadaver mouse model. We also demonstrate the feasibility of a hybrid telemetry system where the microwatts of power that can be harvested from SC-based WPT approach is used for back-scattering a radio-frequency signal and is used for remote sensing of in-vivo physiological parameters like temperature. The functionality of the hybrid system has also been verified using a cadaver mouse model housed in a cage that was retrofitted with 915 MHz RF back-scattering antennas. We believe that the proposed remote power-delivery and hybrid telemetry approach would be useful in remote activation of wearable devices and in the design of energy-efficient animal cages used for long-term monitoring applications.
SAHRTA: A Supervisory-Based Adaptive Human-Robot Teaming Architecture

Supervisory-based human-robot teams are deployed in various dynamic and extreme environments (e.g., space exploration). Achieving high task performance in such environments is critical, as a mistake may lead to significant monetary loss or human injury. Task performance may be augmented by adapting the supervisory interface's interactions or autonomy levels based on the human supervisor's workload level, as workload is related to task performance. Typical adaptive systems rely solely on the human's overall or cognitive workload state to select what adaptation strategy to implement; however, overall workload encompasses many dimensions (i.e., cognitive, physical, visual, auditory, and speech) called workload components. Selecting an appropriate adaptation strategy based on a complete human workload state (rather than a single workload dimension) may allow for more impactful adaptations that ensure high task performance. A Supervisory-Based Adaptive Human-Robot Teaming Architecture (SAHRTA) that selects an appropriate level of autonomy or system interaction based on a complete realtime multi-dimensional workload estimate and predicted future task performance is introduced. SAHRTA was shown to improve overall task performance in a physically expanded version of the NASA Multi-Attribute Task Battery.
PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines

In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to generate end-to-end ML pipelines. While these techniques facilitate the creation of models, given their black-box nature, the complexity of the underlying algorithms, and the large number of pipelines they derive, they are difficult for developers to debug. It is also challenging for machine learning experts to select an AutoML system that is well suited for a given problem. In this paper, we present the Pipeline Profiler, an interactive visualization tool that allows the exploration and comparison of the solution space of machine learning (ML) pipelines produced by AutoML systems. PipelineProfiler is integrated with Jupyter Notebook and can be combined with common data science tools to enable a rich set of analyses of the ML pipelines, providing users a better understanding of the algorithms that generated them as well as insights into how they can be improved. We demonstrate the utility of our tool through use cases where PipelineProfiler is used to better understand and improve a real-world AutoML system. Furthermore, we validate our approach by presenting a detailed analysis of a think-aloud experiment with six data scientists who develop and evaluate AutoML tools.
Semi-Supervised Graph-to-Graph Translation

Graph translation is very promising research direction and has awide range of potential real-world applications. Graph is a natural structure for representing relationship and interactions, and its translation can encode the intrinsic semantic changes of relation-ships in different scenarios. However, despite its seemingly wide possibilities, usage of graph translation so far is still quite limited.One important reason is the lack of high-quality paired dataset. For example, we can easily build graphs representing peoples? shared music tastes and those representing co-purchase behavior, but a well paired dataset is much more expensive to obtain. Therefore,in this work, we seek to provide a graph translation model in the semi-supervised scenario. This task is non-trivial, because graph translation involves changing the semantics in the form of link topology and node attributes, which is difficult to capture due to the combinatory nature and inter-dependencies. Furthermore, due to the high order of freedom in graph's composition, it is difficult to assure the generalization ability of trained models. These difficulties impose a tighter requirement for the exploitation of unpaired samples. Addressing them, we propose to construct a dual representation space, where transformation is performed explicitly to model the semantic transitions. Special encoder/decoder structures are designed, and auxiliary mutual information loss is also adopted to enforce the alignment of unpaired/paired examples. We evaluate the proposed method in three different datasets.
Covariant phase space with boundaries

The covariant phase space method of Iyer, Lee, Wald, and Zoupas gives an elegant way to understand the Hamiltonian dynamics of Lagrangian field theories without breaking covariance. The original literature however does not systematically treat total derivatives and boundary terms, which has led to some confusion about how exactly to apply the formalism in the presence of boundaries. In particular the original construction of the canonical Hamiltonian relies on the assumed existence of a certain boundary quantity “B”, whose physical interpretation has not been clear. We here give an algorithmic procedure for applying the covariant phase space formalism to field theories with spatial boundaries, from which the term in the Hamiltonian involving B emerges naturally. Our procedure also produces an additional boundary term, which was not present in the original literature and which so far has only appeared implicitly in specific examples, and which is already nonvanishing even in general relativity with sufficiently permissive boundary conditions. The only requirement we impose is that at solutions of the equations of motion the action is stationary modulo future/past boundary terms under arbitrary variations obeying the spatial boundary conditions; from this the symplectic structure and the Hamiltonian for any diffeomorphism that preserves the theory are unambiguously constructed. We show in examples that the Hamiltonian so constructed agrees with previous results. We also show that the Poisson bracket on covariant phase space directly coincides with the Peierls bracket, without any need for non-covariant intermediate steps, and we discuss possible implications for the entropy of dynamical black hole horizons.
Quantitative Projections in the Sturm Oscillation Theorem

Abstract There is c > 0 such that for all f ∈ C [ 0 , π ] with at most d − 1 roots inside ( 0 , π ) ∑ 1 ≤ n ≤ d | 〈 f , sin ⁡ ( n x ) 〉 | ≥ κ − κ 2 log ⁡ κ ‖ f ‖ L 2 where κ = c ‖ ∇ f ‖ L 2 ‖ f ‖ L 2 . This quantifies the Sturm-Hurwitz Theorem and connects a purely topological condition (number of roots) to the Fourier spectrum. It is also one of few estimates on Fourier coefficients from below. The result holds more generally for eigenfunctions of regular Sturm-Liouville problems − ( p ( x ) y ′ ( x ) ) ′ + q ( x ) y ( x ) = λ w ( x ) y ( x ) on ( a , b ) . Sturm-Liouville theory shows the existence of a sequence of solutions ( ϕ n ) n = 1 ∞ that form an orthogonal basis of L 2 ( a , b ) with respect to w ( x ) d x . Sturm himself proved that if f : ( a , b ) → R is a finite linear combinations of ϕ n having d − 1 roots inside ( a , b ) , then f cannot be orthogonal to A = span { ϕ 1 , … , ϕ d } . We prove a lower bound on the size of the projection ‖ π A f ‖ .