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Coordination is one of the keys for the success of open source software (OSS) communities because geographically distributed members need to collaborate on their work using communication tools (e.g., mailing lists, bulletin board systems, bug tracking systems, and so on). In this paper, we investigated the informal social structure among developers and users by analyzing two mailing lists of developers and users in the Apache community based on betweenness centrality, one centrality measure proposed by Freeman. From the analysis results, we found that (1) participants with high betweenness coordinated activities between developers and users and (2) some participants have been functioning as coordinators in the community for a long time., OSS 2008 : Open Source Development, Communities and Quality, September 7-10, 2008, Milano, Italy

Self-assembly of modular robotic systems enables the construction of complex robotic configurations to adapt to different tasks. This paper presents a framework for SMORES types of modular robots to efficiently self-assemble into tree topologies. These modular robots form kinematic chains that have been shown to be capable of a large variety of manipulation and locomotion tasks, yet they can reconfigure using a mobile reconfiguration. A desired kinematic topology can be mapped onto a planar pattern with the optimal module assignment based on the modules’ locations, then the mobile reconfiguration assembly process can be executed in parallel. A docking controller is developed to guarantee the success of docking processes. A hybrid control architecture is designed to handle a large number of modules and complex behaviors of each individual, and achieve efficient and robust self-assembly actions. The framework is demonstrated in both hardware and simulation on the SMORES-EP platform.

Continuous-time Markov chains are frequently used as stochastic models for chemical reaction networks, especially in the growing field of systems biology. A fundamental problem for these Stochastic Chemical Reaction Networks (SCRNs) is to understand the dependence of the stochastic behavior of these systems on the chemical reaction rate parameters. Towards solving this problem, in this paper we develop theoretical tools called comparison theorems that provide stochastic ordering results for SCRNs. These theorems give sufficient conditions for monotonic dependence on parameters in these network models, which allow us to obtain, under suitable conditions, information about transient and steady-state behavior. These theorems exploit structural properties of SCRNs, beyond those of general continuous-time Markov chains. Furthermore, we derive two theorems to compare stationary distributions and mean first passage times for SCRNs with different parameter values, or with the same parameters and different initial conditions. These tools are developed for SCRNs taking values in a generic (finite or countably infinite) state space and can also be applied for non-mass-action kinetics models. When propensity functions are bounded, our method of proof gives an explicit method for coupling two comparable SCRNs, which can be used to simultaneously simulate their sample paths in a comparable manner. We illustrate our results with applications to models of enzymatic kinetics and epigenetic regulation by chromatin modifications.

Recent elections have highlighted how electoral cycles are often accompanied by increases in negative rhetoric surrounding immigration. Exploiting as-if random assignment in individual interview dates for the European Social Survey, this paper examines how proximity to elections affects individual preferences on immigration. We find that closer to elections, attitudes toward immigration become more negative. This effect is primarily driven by country-elections where party platforms are more likely to include anti-immigrant rhetoric. When elections are more distant, these effects largely disappear, highlighting the possibility that anti-immigration electoral mandates are based on artificially inflated concerns of the electorate about immigration. Overall, these results provide important insights into how elections influence issue stances and social cohesion in Europe.

In-situ visual observations of marine organisms is crucial to developing behavioural understandings and their relations to their surrounding ecosystem. Typically, these observations are collected via divers, tags, and remotely-operated or human-piloted vehicles. Recently, however, autonomous underwater vehicles equipped with cameras and embedded computers with GPU capabilities are being developed for a variety of applications, and in particular, can be used to supplement these existing data collection mechanisms where human operation or tags are more difficult. Existing approaches have focused on using fully-supervised tracking methods, but labelled data for many underwater species are severely lacking. Semi-supervised trackers may offer alternative tracking solutions because they require less data than fully-supervised counterparts. However, because there are not existing realistic underwater tracking datasets, the performance of semi-supervised tracking algorithms in the marine domain is not well understood. To better evaluate their performance and utility, in this paper we provide (1) a novel dataset specific to marine animals located at http://warp.whoi.edu/vmat/ , (2) an evaluation of state-of-the-art semi-supervised algorithms in the context of underwater animal tracking, and (3) an evaluation of real-world performance through demonstrations using a semi-supervised algorithm on-board an autonomous underwater vehicle to track marine animals in the wild.

Powerful actors have engaged in information control for centuries, restricting, promoting, or influencing the information environment as it suits their evolving agendas. In the Digital Age, information control has moved online, and information operations now target the online platforms that play a critical role in news engagement and civic debate. In this paper, we use a discrete-time stochastic model to analyze coordinated activity in an online social network, representing the behaviors of accounts as interacting Markov chains. From a dataset of 31,521 tweets posted by 206 accounts, half of which were identified by Twitter as participating in a state-linked information operation, we evaluate the coordination, measured by the apparent influence, between pairs of state-linked accounts compared to unaffiliated accounts. Our analysis reveals that state-linked actors demonstrate significantly higher levels of coordination among themselves compared to their coordination with unaffiliated accounts. Furthermore, the degree of coordination observed between state-linked accounts is more than seven times greater than the coordination observed between unaffiliated accounts. Moreover, we find that the account that represented the most coordinated activity in the network had no followers, demonstrating the power of our modeling approach to unearth hidden connections even in the absence of explicit network structure.

According to a conjecture of Lusztig, the asymptotic affine Hecke algebra should admit a description in terms of the Grothedieck group of sheaves on the square of a finite set equivariant under the action of the centralizer of a nilpotent element in the reductive group. A weaker form of this statement, allowing for possible central extensions of stabilizers of that action, has been proved by the first named author with Ostrik. In the present paper, we describe an example showing that nontrivial central extensions do arise, thus the above weaker statement is optimal. We also show that Lusztig's homomorphism from the affine Hecke algebra to the asymptotic affine Hecke algebra induces an isomorphism on cocenters and discuss the relation of the above central extensions to the structure of the cocenter.

It is often argued that the indispensability of continuum models comes from their empirical adequacy despite their decoupling from the microscopic details of the modelled physical system. There is thus a commonly held misconception that temperature varying across a region of space or time can always be accurately represented as a continuous function. We discuss three inter-related cases of temperature modelling — in phase transitions, thermal boundary resistance and slip flows — and show that the continuum view is fallacious on the ground that the microscopic details of a physical system are not necessarily decoupled from continuum models. We show how temperature discontinuities are present in both data (experiments and simulations) and phenomena (theory and models) and how discontinuum models of temperature variation may have greater empirical adequacy and explanatory power. The conclusions of our paper are: a) continuum idealisations are not indispensable to modelling physical phenomena and both continuous and discontinuous representations of phenomena work depending on the context; b) temperature is not necessarily a continuously defined function in our best scientific representations of the world; and c) that its continuity, where applicable, is a contingent matter. We also raise a question as to whether discontinuous representations should be considered truly de-idealised descriptions of physical phenomena.

This paper proposes a generalized self-cueing real-time attention scheduling framework for DNN-based visual machine perception pipelines on resource-limited embedded platforms. Self-cueing means we identify subframe-level regions of interest in a scene internally by exploiting temporal correlations among successive video frames as opposed to externally via a cueing sensor. One limitation of our original self-cueing-and-inspection strategy (Liu et al. in Proceedings of the 28th IEEE real-time and embedded technology and applications symposium (RTAS), 2022b) lies in its lack of computational efficiency under high workloads, like busy traffic scenarios where a large number of objects are identified and separately inspected. We extend the conference publication by integrating image resizing with intermittent inspection and task batching in attention scheduling. The extension enhances the original algorithm by accelerating the processing of large objects by reducing their resolution at the cost of only a negligible degradation in accuracy, thereby achieving a higher overall object inspection throughput. After extracting partial regions around objects of interest, using an optical flow-based tracking algorithm, we allocate computation resources (i.e. DNN inspection) to them in a criticality-aware manner using a generalized batched proportional balancing algorithm (GBPB), to minimize a concept of generalized system uncertainty. It saves computational resources by inspecting low-priority regions intermittently at low frequencies and inspecting large objects at low resolutions. We implement the system on an NVIDIA Jetson Xavier platform and extensively evaluate its performance using a real-world driving dataset from Waymo. The proposed GBPB algorithm consistently outperforms the previous BPB algorithm that only uses intermittent inspection and a set of baselines. The performance gain of GBPB is larger in facing more significant resource constraints (i.e., lower samp

Portrait viewpoint and illumination editing is an important problem with several applications in VR/AR, movies, and photography. Comprehensive knowledge of geometry and illumination is critical for obtaining photorealistic results. Current methods are unable to explicitly model in 3D while handling both viewpoint and illumination editing from a single image. In this paper, we propose VoRF, a novel approach that can take even a single portrait image as input and relight human heads under novel illuminations that can be viewed from arbitrary viewpoints. VoRF represents a human head as a continuous volumetric field and learns a prior model of human heads using a coordinate-based MLP with individual latent spaces for identity and illumination. The prior model is learned in an auto-decoder manner over a diverse class of head shapes and appearances, allowing VoRF to generalize to novel test identities from a single input image. Additionally, VoRF has a reflectance MLP that uses the intermediate features of the prior model for rendering One-Light-at-A-Time (OLAT) images under novel views. We synthesize novel illuminations by combining these OLAT images with target environment maps. Qualitative and quantitative evaluations demonstrate the effectiveness of VoRF for relighting and novel view synthesis, even when applied to unseen subjects under uncontrolled illumination. This work is an extension of Rao et al. (VoRF: Volumetric Relightable Faces 2022). We provide extensive evaluation and ablative studies of our model and also provide an application, where any face can be relighted using textual input.

Consider an agent traversing a graph of “gadgets”, where each gadget has local state that changes with each traversal by the agent according to specified rules. Prior work has studied the computational complexity of deciding whether the agent can reach a specified location, a problem we call reachability. This paper introduces new goals for the agent, aiming to characterize when the computational complexity of these problems is the same or differs from that of reachability. First we characterize the complexity of universal traversal—where the goal is to traverse every gadget at least once—for DAG gadgets (partially), one-state gadgets, and reversible deterministic gadgets. Then we study the complexity of reconfiguration—where the goal is to bring the system of gadgets to a specified state. We prove many cases PSPACE-complete, and show in some cases that reconfiguration is strictly harder than reachability, while in other cases, reachability is strictly harder than reconfiguration.

Why, when, and how do stereotypes change? This paper develops a computational account based on the principles of structure learning: stereotypes are governed by probabilistic beliefs about the assignment of individuals to groups. Two aspects of this account are particularly important. First, groups are flexibly constructed based on the distribution of traits across individuals; groups are not fixed, nor are they assumed to map on to categories we have to provide to the model. This allows the model to explain the phenomena of group discovery and subtyping, whereby deviant individuals are segregated from a group, thus protecting the group’s stereotype. Second, groups are hierarchically structured, such that groups can be nested. This allows the model to explain the phenomenon of subgrouping, whereby a collection of deviant individuals is organized into a refinement of the superordinate group. The structure learning account also sheds light on several factors that determine stereotype change, including perceived group variability, individual typicality, cognitive load, and sample size.

Let M be a cancellative and commutative (additive) monoid. The monoid M is atomic if every non-invertible element can be written as a sum of irreducible elements, which are also called atoms. Also, M satisfies the ascending chain condition on principal ideals (ACCP) if every increasing sequence of principal ideals (under inclusion) becomes constant from one point on. In the first part of this paper, we characterize torsion-free monoids that satisfy the ACCP as those torsion-free monoids whose submonoids are all atomic. A submonoid of the nonnegative cone of a totally ordered abelian group is often called a positive monoid. Every positive monoid is clearly torsion-free. In the second part of this paper, we study the atomic structure of certain classes of positive monoids.

Incentive-based public transport demand management (PTDM) can effectively mitigate overcrowding issues in crowded urban rail systems. Analyzing passengers’ behavioral responses to the incentive can guide the design, implementation, and update of PTDM strategies. Though several studies reported passengers’ responses to fare incentives, they focused on passengers’ short-term behavioral responses. Limited studies explore passengers’ longitudinal behavioral responses for different types of adopters, which is important for policy assessment and adjustment. This paper explores and models passengers’ longitudinal behavior response to a pre-peak fare discount incentive using 18 months of smartcard data in public transport in Hong Kong. We classified adopters into six types based on their temporal travel pattern changes before and after the promotion. The longitudinal analysis reveals that among all adopters, 19% of users change their departure times to take advantage of fare discounts but do not contribute to the goal of reducing peak-hour travel. However, these adopters are more likely to sustain their changed behavior in a long term which is not desired by the incentive program. The spatial analysis shows that the origin station distribution of late adopters is relatively more diverse than the early adopters with more trips starting from distant areas. The diffusion modeling shows that the majority adopters are innovators and the word-of-mouth diffusion effect (imitators) is marginal. The discrete choice model results highlight the heterogeneous impact of factors on different types of adopters and their values of time changes. The significant factors common to adopters are: departure time flexibility, the expected money savings, the required departure time changes, and work locations. The findings are useful for public transport planners and policymakers for informed incentive design and management.

In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals. To address the issues reinforcement learning algorithms face when task rewards are sparse, this paper proposes an intrinsic motivation approach that can be easily integrated into any standard reinforcement learning algorithm and can allow robotic manipulators to learn useful manipulation skills with only sparse extrinsic rewards. Through integrating and balancing empowerment and curiosity, this approach shows superior performance compared to other state-of-the-art intrinsic exploration approaches during extensive empirical testing. When combined with other strategies for tackling the exploration challenge, e.g. curriculum learning, our approach is able to further improve the exploration efficiency and task success rate. Qualitative analysis also shows that when combined with diversity-driven intrinsic motivations, this approach can help manipulators learn a set of diverse skills which could potentially be applied to other more complicated manipulation tasks and accelerate their learning process.

This paper addresses the noise-minimal trajectory optimization problem for a specific type of aircraft: rotorcraft. It relies on a realistic noise footprint computation software provided by industry that is black-box. Locally optimal trajectories are computed through a tailored solution approach based on the Mesh-Adaptive Direct Search algorithm. We propose multiple surrogates defined according to our knowledge of the problem, including a surrogate relying on the physics of the problem (approximating the rotorcraft noise model), and another based on a machine learning (neural network) method. The proposed solution approach is further enhanced by the computation of an appropriate starting guess through a path planning algorithm tailored to the problem, and by the reduction of the variable space domain. The performance of the proposed methodology both in terms of quality of the solutions (trajectories exhibiting significant noise reduction compared to those currently flown in practice) and computing time is illustrated through numerical experiments on real-world case studies.

Often marketers face the challenge of how to communicate best with the customers who have the right responsibilities, influence or purchasing power, especially in business-to-business (B2B) settings. For example, B2B marketers selling software and IT need to identify IT decision-makers (ITDMs) within organizations. The modern digital environment in theory allows marketers to target individuals in organizations through specifically designed third-party audience segments based on deterministic prospect lists or probabilistic inference. However, in this paper we show that in our context, such ‘off-the-shelf’ segments perform no better at reaching the right person than random prospecting. We present evidence that even deterministic attribute information is flawed for ITDM identification, and that the poor campaign results can be partly linked to incorrect assignment of established prospect profiles to online identifiers. We then use access to our publisher network data to investigate what would happen if the advertiser had used first-party data that are less susceptible to the identified issues. We demonstrate that first-party demographics or contextual information allows advertisers and publishers to outperform both third-party ITDM audience segments and random prospecting. Our findings have implications for understanding the shift in digital advertising away from third-party cookie tracking, and how to execute digital marketing in the context of broad privacy regulation.

This paper introduces the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic architecture [Harb et al. in When waiting is not an option: learning options with a deliberation cost. arXiv preprint arXiv:1709.04571, 2017] to enable hierarchical reinforcement learning across multiple sensory inputs. Agents trained using our approach learn to attend to their various sensory modalities (e.g., audio, video) at the appropriate moments, thereby executing actions based on multiple sensory streams without reliance on supervisory data. We demonstrate empirically that the sensory attention mechanism anticipates and identifies useful latent features, while filtering irrelevant sensor modalities during execution. Further, we provide concrete examples in which the approach not only improves performance in a single task, but accelerates transfer to new tasks. We modify the Arcade Learning Environment [Bellemare et al. in J Artif Intell Res 47:253–279, 2013] to support audio queries (ALE-audio code available at https://github.com/shayegano/Arcade-Learning-Environment), and conduct evaluations of crossmodal learning in the Atari 2600 games H.E.R.O. and Amidar. Finally, building on the recent work of Babaeizadeh et al. [in: International conference on learning representations (ICLR), 2017], we open-source a fast hybrid CPU–GPU implementation of CASL (CASL code available at https://github.com/shayegano/CASL).

In a 1983 paper, the author has established a (decategorified) Satake equivalence for affine Hecke algebras. In this paper, we give new proofs for some results of that paper, one based on the theory of J-rings and one based on the known character formula for rational representations of a reductive group in positive, large characteristic. We also give an extension of that formula to disconnected groups.

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.

In-space manufacturing is a candidate approach for constructing next-generation space structures with larger characteristic dimensions than modern deployable structures. While many construction methods have been proposed, analysis of their performance for building precision structures, such as large-diameter reflectors, is scarce. In this paper, we present a quantitative, system-level comparison of materials and processes for in-space manufacturing. By using performance metrics for thermal stability, resistance to disturbance loads, and minimal-mass buckling strength, we identify candidate feedstock materials. Then, using the metrics of energy consumption and accuracy, we compare candidate processing methods and find that deformation processing is a promising on-orbit manufacturing method. We synthesize the analysis with a case study on the construction of a tetrahedral truss supporting a reflector surface and provide guidelines for assessing materials and processes for in-space manufacturing.

The science objectives of the LISA mission have been defined under the implicit assumption of a 4-years continuous data stream. Based on the performance of LISA Pathfinder, it is now expected that LISA will have a duty cycle of $$\approx 0.75$$ ≈ 0.75 , which would reduce the effective span of usable data to 3 years. This paper reports the results of a study by the LISA Science Group, which was charged with assessing the additional science return of increasing the mission lifetime. We explore various observational scenarios to assess the impact of mission duration on the main science objectives of the mission. We find that the science investigations most affected by mission duration concern the search for seed black holes at cosmic dawn, as well as the study of stellar-origin black holes and of their formation channels via multi-band and multi-messenger observations. We conclude that an extension to 6 years of mission operations is recommended.

This paper presents fast non-sampling based methods to assess the risk for trajectories of autonomous vehicles when probabilistic predictions of other agents’ futures are generated by deep neural networks (DNNs). The presented methods address a wide range of representations for uncertain predictions including both Gaussian and non-Gaussian mixture models to predict both agent positions and control inputs conditioned on the scene contexts. We show that the problem of risk assessment when Gaussian mixture models of agent positions are learned can be solved rapidly to arbitrary levels of accuracy with existing numerical methods. To address the problem of risk assessment for non-Gaussian mixture models of agent position, we propose finding upper bounds on risk using nonlinear Chebyshev’s Inequality and sums-of-squares programming; they are both of interest as the former is much faster while the latter can be arbitrarily tight. These approaches only require higher order statistical moments of agent positions to determine upper bounds on risk. To perform risk assessment when models are learned for agent control inputs as opposed to positions, we propagate the moments of uncertain control inputs through the nonlinear motion dynamics to obtain the exact moments of uncertain position over the planning horizon. To this end, we construct deterministic linear dynamical systems that govern the exact time evolution of the moments of uncertain position in the presence of uncertain control inputs. The presented methods are demonstrated on realistic predictions from DNNs trained on the Argoverse and CARLA datasets and are shown to be effective for rapidly assessing the probability of low probability events.

In this paper, we explore neural network models that learn to associate segments of spoken audio captions with the semantically relevant portions of natural images that they refer to. We demonstrate that these audio-visual associative localizations emerge from network-internal representations learned as a by-product of training to perform an image-audio retrieval task. Our models operate directly on the image pixels and speech waveform, and do not rely on any conventional supervision in the form of labels, segmentations, or alignments between the modalities during training. We perform analysis using the Places 205 and ADE20k datasets demonstrating that our models implicitly learn semantically coupled object and word detectors.

In this paper, we propose an alternative construction of a certain class of Deformed Double Current Algebras. We construct them as spherical subalgebras of symplectic reection algebras in the Deligne category. They can also be thought of as ultraproducts of the corresponding spherical subalgebras in finite rank. We also provide new presentations of DDCA of types A and B by generators and relations.

The interaction of the cerebellum with cerebral cortical dynamics is still poorly understood. In this paper, dynamical causal modeling is used to examine the interaction between cerebellum and cerebral cortex as indexed by MRI resting-state functional connectivity in three large-scale networks on healthy young adults (N = 200; Human Connectome Project dataset). These networks correspond roughly to default mode, task positive, and motor as determined by prior cerebellar functional gradient analyses. We find uniform interactions within all considered networks from cerebellum to cerebral cortex, providing support for the notion of a universal cerebellar transform. Our results provide a foundation for future analyses to quantify and further investigate whether this is a property that is unique to the interactions from cerebellum to cerebral cortex.

The Simons Observatory is a ground-based cosmic microwave background instrument to be sited in the Atacama Desert in Chile. SO will deploy 60,000 transition-edge sensors (TES) bolometers in 49 separate focal-plane modules across a suite of four telescopes covering three dichroic bands termed low frequency (LF), mid-frequency and ultra-high frequency. Each MF and UHF focal-plane module packages 1720 feedhorn-coupled detectors with cryogenic components for highly multiplexed readout using microwave SQUID multiplexing. In this paper, we describe the testing program we have developed for high-throughput validation of modules after they are assembled. The validation requires measurements of the yield, saturation powers, time constants, noise properties and optical efficiencies. Additional measurements will be performed for further characterization as needed. We describe the methods developed and demonstrate preliminary results from the initial testing of a prototype module.

The Simons Observatory (SO) includes four telescopes that will measure the temperature and polarization of the cosmic microwave background using over 60,000 highly sensitive transition-edge bolometers (TES). These multichroic TES bolometers are read out by a microwave RF SQUID multiplexing system with a multiplexing factor of 910. Given that both TESes and SQUIDs are susceptible to magnetic field pickup and that it is hard to predict how they will respond to such fields, it is important to characterize the magnetic response of these systems empirically. This information can then be used to limit spurious signals by informing magnetic shielding designs for the detectors and readout. This paper focuses on measurements of magnetic pickup with different magnetic shielding configurations for the SO universal multiplexing module (UMM), which contains the SQUIDs, associated resonators, and TES bias circuit. The magnetic pickup of a prototype UMM was tested under three shielding configurations: no shielding (copper packaging), aluminum packaging for the UMM, and a tin/lead-plated shield surrounding the entire dilution refrigerator 100 mK cold stage. We also present measurements of the pickup in the UMM when aluminum feedhorns are installed. The measurements show that the aluminum packaging outperforms the copper packaging by a shielding factor of 8-10, and adding the tin/lead-plated 1K shield further increases the relative shielding factor in the aluminum configuration by 1-2 orders of magnitude. The addition of feedhorns provides a factor of 30 improvement when the tin/lead shield is not installed and a factor of 5 improvement when it is.

This paper aims to develop distributed algorithms for nonconvex optimization problems with complicated constraints associated with a network. The network can be a physical one, such as an electric power network, where the constraints are nonlinear power flow equations, or an abstract one that represents constraint couplings between decision variables of different agents. Despite the recent development of distributed algorithms for nonconvex programs, highly complicated constraints still pose a significant challenge in theory and practice. We first identify some difficulties with the existing algorithms based on the alternating direction method of multipliers (ADMM) for dealing with such problems. We then propose a reformulation that enables us to design a two-level algorithm, which embeds a specially structured three-block ADMM at the inner level in an augmented Lagrangian method framework. Furthermore, we prove the global and local convergence as well as iteration complexity of this new scheme for general nonconvex constrained programs, and show that our analysis can be extended to handle more complicated multi-block inner-level problems. Finally, we demonstrate with computation that the new scheme provides convergent and parallelizable algorithms for various nonconvex applications, and is able to complement the performance of the state-of-the-art distributed algorithms in practice by achieving either faster convergence in optimality gap or in feasibility or both.

This paper, originally printed 50 years ago, was the first to provide a detailed discussion of the possible design of an interferometric gravitational-wave detector. In particular, unlike any previous paper based on a similar idea, it provides a detailed analysis of the noise sources and their mitigation. Since the work eventually led to opening up the whole field of gravitational-wave astronomy, as described in the accompanying editorial note, with many exciting results already, and brought the author a share in a Nobel Prize, it is one of the most significant papers in the Golden Oldies series.

A Correction to this paper has been published: https://doi.org/10.1007/s12021-021-09522-x

To maintain a robust catalog of resident space objects (RSOs), space situational awareness (SSA) mission operators depend on ground- and space-based sensors to repeatedly detect, characterize, and track objects in orbit. Although some space sensors are capable of monitoring large swaths of the sky with wide fields of view (FOVs), others—such as maneuverable optical telescopes, narrow-band imaging radars, or satellite laser-ranging systems—are restricted to relatively narrow FOVs and must slew at a finite rate from object to object during observation. Since there are many objects that a narrow FOV sensor could choose to observe within its field of regard (FOR), it must schedule its pointing direction and duration using some algorithm. This combinatorial optimization problem is known as the sensor-tasking problem. In this paper, we developed a deep reinforcement learning agent to task a space-based narrow-FOV sensor in low Earth orbit (LEO) using the proximal policy optimization algorithm. The sensor’s performance—both as a singular sensor acting alone, but also as a complement to a network of taskable, narrow-FOV ground-based sensors—is compared to the greedy scheduler across several figures of merit, including the cumulative number of RSOs observed and the mean trace of the covariance matrix of all of the observable objects in the scenario. The results of several simulations are presented and discussed. Additionally, the results from an LEO SSA sensor in different orbits are evaluated and discussed, as well as various combinations of space-based sensors.

Recovery is now widely acknowledged as the dominant approach to the management of mental distress and illness in government, third-sector and some peer-support contexts across the United Kingdom and elsewhere in the Anglophone Global North. Although narrative has long been recognised in practice and in policy as a key “technology of recovery,” there has been little critical investigation of how recovery narratives are constituted and mobilised, and with what consequences. This paper offers an interdisciplinary. critical medical humanities analysis of the politics and possibilities of Recovery Narrative, drawing literary theoretical concepts of genre and philosophical approaches to the narrative self into conversation with the critiques of recovery advanced by survivor-researchers, sociologists and mad studies scholars.. Our focus is not on the specific stories of individuals, but on the form, function and effects of Recovery Narrative as a highly circumscribed kind of storytelling. We identify the assumptions, lacunae and areas of tension which compel a more critical approach to the way this genre is operationalised in and beyond mental health services and conclude by reflecting on the possibilities offered by other communicative formats, spaces and practices.

Recovery is now widely acknowledged as the dominant approach to the management of mental distress and illness in government, third-sector and some peer-support contexts across the United Kingdom and elsewhere in the Anglophone Global North. Although narrative has long been recognised in practice and in policy as a key “technology of recovery,” there has been little critical investigation of how recovery narratives are constituted and mobilised, and with what consequences. This paper offers an interdisciplinary. critical medical humanities analysis of the politics and possibilities of Recovery Narrative, drawing literary theoretical concepts of genre and philosophical approaches to the narrative self into conversation with the critiques of recovery advanced by survivor-researchers, sociologists and mad studies scholars.. Our focus is not on the specific stories of individuals, but on the form, function and effects of Recovery Narrative as a highly circumscribed kind of storytelling. We identify the assumptions, lacunae and areas of tension which compel a more critical approach to the way this genre is operationalised in and beyond mental health services and conclude by reflecting on the possibilities offered by other communicative formats, spaces and practices.

Recovery is now widely acknowledged as the dominant approach to the management of mental distress and illness in government, third-sector and some peer-support contexts across the United Kingdom and elsewhere in the Anglophone Global North. Although narrative has long been recognised in practice and in policy as a key “technology of recovery,” there has been little critical investigation of how recovery narratives are constituted and mobilised, and with what consequences. This paper offers an interdisciplinary. critical medical humanities analysis of the politics and possibilities of Recovery Narrative, drawing literary theoretical concepts of genre and philosophical approaches to the narrative self into conversation with the critiques of recovery advanced by survivor-researchers, sociologists and mad studies scholars.. Our focus is not on the specific stories of individuals, but on the form, function and effects of Recovery Narrative as a highly circumscribed kind of storytelling. We identify the assumptions, lacunae and areas of tension which compel a more critical approach to the way this genre is operationalised in and beyond mental health services and conclude by reflecting on the possibilities offered by other communicative formats, spaces and practices.

Recovery is now widely acknowledged as the dominant approach to the management of mental distress and illness in government, third-sector and some peer-support contexts across the United Kingdom and elsewhere in the Anglophone Global North. Although narrative has long been recognised in practice and in policy as a key “technology of recovery,” there has been little critical investigation of how recovery narratives are constituted and mobilised, and with what consequences. This paper offers an interdisciplinary. critical medical humanities analysis of the politics and possibilities of Recovery Narrative, drawing literary theoretical concepts of genre and philosophical approaches to the narrative self into conversation with the critiques of recovery advanced by survivor-researchers, sociologists and mad studies scholars.. Our focus is not on the specific stories of individuals, but on the form, function and effects of Recovery Narrative as a highly circumscribed kind of storytelling. We identify the assumptions, lacunae and areas of tension which compel a more critical approach to the way this genre is operationalised in and beyond mental health services and conclude by reflecting on the possibilities offered by other communicative formats, spaces and practices.

The majority of geographical profiling research focuses on the relationship between offender and location, which works particularly well when a burial site is known. In real-world investigations, however, burial or dump sites are often not known. The aim of the current paper is to outline a relatively under-used method of geographic profiling: Winthropping. While the method has been around for several decades, few studies have provided any research findings using it. There are two likely reasons for Winthropping being under-used: first, it has not been clearly, theoretically explained; second, given its relative novelty, it may not be immediately clear how to use it in research and real-world scenarios. The current paper outlines several key psychological (e.g., satisficing and affordances) and criminological (e.g., rational choice theory and crime geometry) theories that may explain why Winthropping works. Case studies are provided, and a methodological approach (matrix forecasting) is then provided to show how it could work in research practice and real-world applications. Overall, Winthropping is deemed to be highly useful, and it is hoped that experts in the field will begin developing this tool for wider, applied use.

The majority of geographical profiling research focuses on the relationship between offender and location, which works particularly well when a burial site is known. In real-world investigations, however, burial or dump sites are often not known. The aim of the current paper is to outline a relatively under-used method of geographic profiling: Winthropping. While the method has been around for several decades, few studies have provided any research findings using it. There are two likely reasons for Winthropping being under-used: first, it has not been clearly, theoretically explained; second, given its relative novelty, it may not be immediately clear how to use it in research and real-world scenarios. The current paper outlines several key psychological (e.g., satisficing and affordances) and criminological (e.g., rational choice theory and crime geometry) theories that may explain why Winthropping works. Case studies are provided, and a methodological approach (matrix forecasting) is then provided to show how it could work in research practice and real-world applications. Overall, Winthropping is deemed to be highly useful, and it is hoped that experts in the field will begin developing this tool for wider, applied use.

This paper explains how social media drives organization-public collaborative outcomes such as social media-enabled service co-creation in non-profit organizations (nonprofits). We assume a technology affordances perspective to identify social media structures enacted through discovering functional affordances, managing constraints through privacy preferences, and constructing meaning and values, We explain how these structures relate to service co-creation. We surveyed 73 nonprofits on social media and collected 289 usable responses. We apply structural equation modeling to analyze the data. Our findings suggest that symbolic constructed meaning and values together with the organization’s privacy preferences on social media are positively related to socialization, visibility, and information sharing affordances. Unlike information sharing, socialization and visibility affordances are, in turn, positively related to service co-creation. This study advances our theoretical understanding of how social technology structures produce collaborative outcomes and offers practical insights into the cumulative value of social media.

Despite decades of research, there is no consensus as to the core correlates of national-level voter turnout. We argue that this is, in part, due to the lack of comprehensive, systematic empirical analysis. This paper conducts such an analysis. We identify 44 articles on turnout from 1986 to 2017. These articles include over 127 potential predictors of voter turnout, and we collect data on seventy of these variables. Using extreme bounds analysis, we run over 15 million regressions to determine which of these 70 variables are robustly associated with voter turnout in 579 elections in 80 democracies from 1945 to 2014. Overall, 22 variables are robustly associated with voter turnout, including compulsory voting, concurrent elections, competitive elections, inflation, previous turnout, and economic globalization.

This paper proposes a simple low memory architecture for computing discrete wavelet transform (DWT) of high-resolution (HR) images on low-cost memory-constrained sensor nodes used in visual sensor networks (VSN) or Internet of Multimedia Things (IoMT). The main feature of the proposed architecture is the novel data scanning technique that makes memory requirement independent of the image size. The proposed architecture needs only (30S) words of memory, where S is the number of parallel processing units and a critical path delay (CPD) equal to the delay of a multiplier (Tm). Furthermore, a multiplierless version of this architecture is also proposed which reduces the CPD to Ta

Games can be rich environments for learning and can elicit evidence of students’ conceptual understanding and inquiry processes. Illuminating students’ content-specific gameplay decisions, or methods of completing game tasks related to a certain domain, requires a context that is open-ended enough for students to make choices that demonstrate their thinking. Doing this also requires rich log data and methods of Game Learning Analytics (GLA) that are granular enough to look at the specific choices most relevant to that context and domain. This paper presents research done on student exploration of high school level Mendelian genetics in a multiplayer online game called The Radix Endeavor. The study uses three approaches to identify content-specific gameplay decisions and distinguish players utilizing different methods, looking at actions and tool use, play patterns and player types, and tool input patterns. In the context of the selected game quest, the three approaches were found to yield insights into different ways that students complete tasks in genetics, suggesting the potential for a set of more generalized guiding questions in the GLA field that could be adopted by learning games designers and data scientists to convey information about content-specific gameplay decisions in learning games.

This paper asks whether the large amounts of digital data that are typically observed on large technology platforms—such as Google, Facebook, Uber and Amazon—typically give rise to structural conditions that would lead to antitrust concerns. In particular, I evaluate whether digital data augments or decreases concerns with regard to network effects and switching costs. I also evaluate whether data should be thought of as an ‘essential facility’.

Autonomous vehicle (AV) technologies are under constant improvement with pilot programs now underway in several urban areas worldwide. Modeling and field-testing efforts are demonstrating that shared mobility coupled with AV technology for automated mobility on-demand (AMoD) service may significantly impact levels of service and environmental outcomes in future cities. Given these rapidly emerging developments, there is an urgent need for methods to adequately quantify the economic impacts of new vehicle technologies and future urban mobility policy. In this paper, we show how broader user-centric impacts can be captured by the activity-based accessibility (ABA) measure, which takes advantage of the rich data and outcomes of utility-maximization activity-based models and its interaction with mesoscale agent-based traffic simulation frameworks. Using the SimMobility simulator, we evaluate shared AMoD strategies applied to a Singapore micromodel city testbed. A near-future strategy of exclusive availability of AMoD service in the central business district (CBD), and a further-horizon strategy of the full operation of AMoD city-wide in the absence of other on-demand services, were tested and evaluated. Our results provide insights into the income and accessibility effects on the population under the implementation of shared and automated mobility policies. The outcomes indicate that the city-wide deployment of AMoD results in greater accessibility and network performance. Moreover, the accessibility of low-income individuals is improved relative to that of mid- and high-income individuals. The restriction of AMoD to the CBD along with the operation of other on-demand services, however, provides a certain level of disbenefit to segments of the population in two exceptional cases. The first is to high-income individuals who live in a suburban zone and rely heavily on on-demand services; the second is to mid-income residents that have excellent public transportat

Speech is one of the most natural communication channels for expressing human emotions. Therefore, speech emotion recognition (SER) has been an active area of research with an extensive range of applications that can be found in several domains, such as biomedical diagnostics in healthcare and human–machine interactions. Recent works in SER have been focused on end-to-end deep neural networks (DNNs). However, the scarcity of emotion-labeled speech datasets inhibits the full potential of training a deep network from scratch. In this paper, we propose new approaches for classifying emotions from speech by combining conventional mel-frequency cepstral coefficients (MFCCs) with image features extracted from spectrograms by a pretrained convolutional neural network (CNN). Unlike prior studies that employ end-to-end DNNs, our methods eliminate the resource-intensive network training process. By using the best prediction model obtained, we also build an SER application that predicts emotions in real time. Among the proposed methods, the hybrid feature set fed into a support vector machine (SVM) achieves an accuracy of 0.713 in a 6-class prediction problem evaluated on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, which is higher than the previously published results. Interestingly, MFCCs taken as unique input into a long short-term memory (LSTM) network achieve a slightly higher accuracy of 0.735. Our results reveal that the proposed approaches lead to an improvement in prediction accuracy. The empirical findings also demonstrate the effectiveness of using a pretrained CNN as an automatic feature extractor for the task of emotion prediction. Moreover, the success of the MFCC-LSTM model is evidence that, despite being conventional features, MFCCs can still outperform more sophisticated deep-learning feature sets.

This paper shows the performance of various traction control strategies that aim to minimize slippage and wheel fighting by properly adjusting the velocity of each traction wheel in a planetary rover. These strategies are validated through simulations performed in ANVEL (Quantum Signal LLC) and using two rovers currently employed by NASA. These experiments use similar features to those that a planetary rover would face on the Moon such as terrain geomorphology and lunar gravity. After running those experiments, the following conclusions were drawn: (1) when no traction control is considered, results show the rover gets entrapped or makes a shorter progress than when traction control is applied; (2) the proposed traction controllers demonstrate a proper balance between slip-compensation (lowest mean slip) and reduction of wheel fighting effects (less aggressive control actions); (3) after considering two different planetary rovers, it is observed that the mechanical configuration effects slip reduction. These contributions can also be observed in the accompanying videos.

Cerebellar reserve refers to the capacity of the cerebellum to compensate for tissue damage or loss of function resulting from many different etiologies. When the inciting event produces acute focal damage (e.g., stroke, trauma), impaired cerebellar function may be compensated for by other cerebellar areas or by extracerebellar structures (i.e., structural cerebellar reserve). In contrast, when pathological changes compromise cerebellar neuronal integrity gradually leading to cell death (e.g., metabolic and immune-mediated cerebellar ataxias, neurodegenerative ataxias), it is possible that the affected area itself can compensate for the slowly evolving cerebellar lesion (i.e., functional cerebellar reserve). Here, we examine cerebellar reserve from the perspective of the three cornerstones of clinical ataxiology: control of ocular movements, coordination of voluntary axial and appendicular movements, and cognitive functions. Current evidence indicates that cerebellar reserve is potentiated by environmental enrichment through the mechanisms of autophagy and synaptogenesis, suggesting that cerebellar reserve is not rigid or fixed, but exhibits plasticity potentiated by experience. These conclusions have therapeutic implications. During the period when cerebellar reserve is preserved, treatments should be directed at stopping disease progression and/or limiting the pathological process. Simultaneously, cerebellar reserve may be potentiated using multiple approaches. Potentiation of cerebellar reserve may lead to compensation and restoration of function in the setting of cerebellar diseases, and also in disorders primarily of the cerebral hemispheres by enhancing cerebellar mechanisms of action. It therefore appears that cerebellar reserve, and the underlying plasticity of cerebellar microcircuitry that enables it, may be of critical neurobiological importance to a wide range of neurological/neuropsychiatric conditions.

In this paper, we report on the preliminary results of a nanowire kinetic inductance detector, a device which operates as both a standard kinetic inductance detector (KID) and a superconducting nanowire single-photon detector (SNSPD). The device consists of an array of detectors, each with a characteristic resonant frequency which can be readout and distinguished on a single transmission line. We demonstrate, due to the nanowire’s small volume, a higher responsivity when operating as a KID under optical loading. Operating the device as an SNSPD, we show the sinusoidal pulse generated from an absorbed photon. Multiple detectors can be struck simultaneously while maintaining the capability to distinguish each pixel. Preliminary results show a variation in count rates among the array, and sources are discussed in the text.

The household travel survey (HTS) finds itself in the midst of rapid technological change. Traditional methods are increasingly being sidelined by digital devices and computational power—for tracking movements, automatically detecting modes and activities, facilitating data collection, etc.. Smartphones have recently emerged as the latest technological enhancement. FMS is a smartphone-based prompted-recall HTS platform, consisting of an app for sensor data collection, a backend for data processing and inference, and a user interface for verification of inferences (e.g., modes, activities, times, etc.). FMS, has been deployed in several cities of the global north, including Singapore. This paper assesses the first use of FMS in a city of the global south, Dar es Salaam. FMS in Dar was implemented over a 1-month period, among 581 adults chosen from 300 randomly selected households. Individuals were provided phones with data plans and the FMS app preloaded. Verification of the collected data occurred every 3 days, via a phone interview. The experiment reveals various social and technical challenges. Models of individual likelihood to participate suggest little bias. Several socioeconomic and demographic characteristics apparently do influence, however, the number of days fully verified per individual. Similar apparent biases emerge when predicting the likelihood of a given day being verified. Some risk of non-random, non-response is, thus, evident.

Varchenko (Adv Math 97(1):110–144, 1993) defined the Varchenko matrix associated with any real hyperplane arrangement and computed its determinant. In this paper, we show that the Varchenko matrix of a hyperplane arrangement has a diagonal form if and only if it is semigeneral, i.e., without degeneracy. In the case of semigeneral arrangement, we present an explicit computation of the diagonal form via combinatorial arguments and matrix operations, thus giving a combinatorial interpretation of the diagonal entries.