cssi/SciSciGPT-SciSciNet huggingface dataset:
EMG-Controlled Non-Anthropomorphic Hand Teleoperation Using a Continuous Teleoperation Subspace

We present a method for EMG-driven teleoperation of non-anthropomorphic robot hands. EMG sensors are appealing as a wearable, inexpensive, and unobtrusive way to gather information about the teleoperator’s hand pose. However, mapping from EMG signals to the pose space of a non-anthropomorphic hand presents multiple challenges. We present a method that first projects from forearm EMG into a subspace relevant to teleoperation. To increase robustness, we use a model which combines continuous and discrete predictors along different dimensions of this subspace. We then project from the teleoperation subspace into the pose space of the robot hand. Our method is effective and intuitive, as it enables novice users to teleoperate pick and place tasks faster and more robustly than state-of-the-art EMG teleoperation methods when applied to a non-anthropomorphic, multi-DOF robot hand.
Risk-averse classification

We develop a new approach to solving classification problems, which is based on the theory of coherent measures of risk and risk sharing ideas. We introduce the notion of a risk-averse classifier and a family of risk-averse classification problems. We show that risk-averse classifiers are associated with minimal points of the possible classification errors, where the minimality is understood with respect to a suitable stochastic order. The new approach allows for measuring risk by distinct risk functional for each class. We analyze the structure of the new classification problem and establish its theoretical relation to known risk-neutral design problems. In particular, we show that the risk-sharing classification problem is equivalent to an implicitly defined optimization problem with unequal weights for each data point. Additionally, we derive a confidence interval for the total risk of a risk-averse classifier. We implement our methodology in a binary classification scenario on several different data sets. We formulate specific risk-averse support vector machines in order to demonstrate the proposed approach and carry out numerical comparison with classifiers which are obtained using the Huber loss function and other loss functions known in the literature.
Gaussian Processes Model-Based Control of Underactuated Balance Robots

Control of underactuated balance robot requires external subsystem trajectory tracking and internal unstable subsystem balancing with limited control authority. We present a learning-based control approach for underactuated balance robots. The tracking and balancing control is designed the controller in fast- and slow-time scales. In the slow-time scale, model predictive control is adopted to plan desired internal state profile to achieve external trajectory tracking task. The internal state is then stabilized around the planned profile in the fast-time scale. The control design is based on a learned Gaussian process (GP) regression model without need of a priori knowledge about the robot dynamics. The controller also incorporates the GP model predicted variance to enhance robustness to modeling errors. Experiments are presented using a Furuta pendulum system.
Rigid Body Motion Prediction with Planar Non-convex Contact Patch

We present a principled method for motion prediction via dynamic simulation for rigid bodies in intermittent contact with each other where the contact is assumed to be a planar non-convex contact patch. The planar non-convex contact patch can either be a topologically connected set or disconnected set. Such algorithms are useful in planning and control for robotic manipulation. Most work in rigid body dynamic simulation assume that the contact between objects is a point contact, which may not be valid in many applications. In this paper, by using the convex hull of the contact patch, we build on our recent work on simulating rigid bodies with convex contact patches, for simulating motion of objects with planar non-convex contact patches. We formulate a discrete-time mixed complementarity problem where we solve the contact detection and integration of the equations of motion simultaneously. Thus, our method is a geometrically-implicit method and we prove that in our formulation, there is no artificial penetration between the contacting rigid bodies. We solve for the equivalent contact point (ECP) and contact impulse of each contact patch simultaneously along with the state, i.e., configuration and velocity of the objects. We provide empirical evidence to show that our method can seamlessly capture transition between different contact modes like patch contact to multiple or single point contact during simulation.
Asymmetric or Incomplete Information about Asset Values

We provide a new framework for using text as data in empirical models. The framework identifies salient information in unstructured text that can control for multidimensional heterogeneity among assets. We demonstrate the efficacy of the framework by reexamining principal-agent problems in residential real estate markets. We show that the agent-owned premiums reported in the extant literature dissipate when the salient textual information is included. The results suggest the previously reported agent-owned premiums suffer from an omitted variable bias, which prior studies incorrectly ascribed to market distortions associated with asymmetric information. (JEL D82, G14, R00)Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
Learning from Standards Deviations: Three Dimensions for Building Education Policies That Last.

Cynthia Coburn, in her 2016 article in the American Journal of Education—“What’s Policy Got to Do With It?”—states that the field of policy implementation suffers from the propensity to learn the s...
Inventory Routing Problem with Facility Location

We study problems that integrate depot location decisions along with the inventory routing problem of serving clients from these depots over time balancing the costs of routing vehicles from the depots with the holding costs of demand delivered before they are due. Since the inventory routing problem is already complex, we study the version that assumes that the daily vehicle routes are direct connections from the depot thus forming stars as solutions, and call this problem the Star Inventory Routing Problem with Facility Location (SIRPFL). As a stepping stone to solving SIRPFL, we first study the Inventory Access Problem (IAP), which is the single depot, single client special case of IRP. The Uncapacitated IAP is known to have a polynomial time dynamic program. We provide an NP-hardness reduction for Capacitated IAP where each demand cannot be split among different trips. We give a 3-approximation for the case when demands can be split and a 6-approximation for the unsplittable case. For Uncapacitated SIRPFL, we provide a 12-approximation by rounding an LP relaxation. Combining the ideas from Capacitated IAP and Uncapacitated SIRPFL, we obtain a 24-approximation for Capacitated Splittable SIRPFL and a 48-approximation for the most general version, the Capacitated Unsplittable SIRPFL.
Physics of high-efficiency iii-nitride quantum wells light-emitting diodes

The review of the approaches to achieve high efficiency III-Nitride based visible and UV light-emitting diodes will be presented. The engineering from nanoscale, microscale, and macroscale in devices will be elaborated for high efficiency devices.
Detection and Prevalence of Monoclonal Gammopathy of Undetermined Significance: A study utilizing mass spectrometry-based monoclonal immunoglobulin rapid accurate mass measurement

High-sensitivity mass spectrometry assays are available to detect monoclonal immunoglobulins. To better assess the prevalence of monoclonal gammopathy of undetermined significance (MGUS), we identified 300 patients diagnosed with MGUS or related gammopathy who had a prior negative work-up for monoclonal proteins as part of the Olmsted County MGUS screening study. Two mass spectrometry-based detection methods (matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) and monoclonal immunoglobulin rapid accurate mass measurements (miRAMM) along with traditional immunofixation were performed on the Olmsted baseline and MGUS diagnostics serum samples. Among the 226 patients considered negative for MGUS based on protein electrophoresis and serum-free light-chain assay, a monoclonal protein could be detected at baseline in 24 patients (10.6%) by immunofixation, 113 patients (50%) by MADLI-TOF mass spectrometry, and 149 patients (65.9%) by miRAMM mass spectrometry. In addition, using miRAMM, some patients demonstrated an oligoclonal to monoclonal transition giving insight into the origin of MGUS. Using the sensitive miRAMM, MGUS is present in 887 of 17,367 persons from the Olmsted County cohort, translating into a prevalence of 5.1% among persons 50 years of age and older. This represents the most accurate prevalence estimate of MGUS thus far.
Improving solve time of aggregation‐based adaptive AMG

This paper proposes improving the solve time of a bootstrap AMG designed previously by the authors. This is achieved by incorporating the information, set of algebraically smooth vectors, generated by the bootstrap algorithm, in a single hierarchy by using sufficiently large aggregates, and these aggregates are compositions of aggregates already built throughout the bootstrap algorithm. The modified AMG method has good convergence properties and shows significant reduction in both, memory and solve time. These savings with respect to the original bootstrap AMG are illustrated on some difficult (for standard AMG) linear systems arising from discretization of scalar and vector function elliptic partial differential equations (PDEs) in both 2d and 3d.
Estimation of phase in EEG rhythms for real-time applications

Objective Estimating the ongoing phase of oscillations in electroencephalography (EEG) recordings is an important aspect of understanding brain function, as well as for the development of phase-dependent closed-loop real-time systems that deliver stimuli. Such stimuli may take the form of direct brain stimulation (for example transcranial magnetic stimulation), or sensory stimuli (for example presentation of an auditory stimulus). We identify two linked problems related to estimating the phase of EEG rhythms with a specific focus on the alpha-band: 1) when the signal after a specific stimulus is unknown (real-time case), or 2) when it is corrupted by the presence of the stimulus itself (offline analysis). We propose methods to estimate the phase at the presentation time of these stimuli. Approach Machine learning methods are used to learn the causal mapping from an unprocessed EEG recording to a phase estimate generated with a non-causal signal processing chain. This mapping is then used to predict the phase causally where non-causal methods are inappropriate. Main results We demonstrate the ability of these machine learning methods to estimate instantaneous phase from an EEG signal subjected to very minor pre-processing with higher accuracy than commonly used signal-processing methods. Significance Neural oscillations have been implicated in a wide variety of sensory, cognitive and motor functions. The instantaneous phase of these rhythms may reflect specific processes of computation which can be acted upon if they can be estimated with sufficient accuracy. Such brain-state dependent paradigms are of increasing medical and scientific interest.
Tractable Approach to MmWaves Cellular Analysis With FSO Backhauling Under Feedback Delay and Hardware Limitations

In this work, we investigate the performance of a millimeter waves (mmWaves) cellular system with free space optical (FSO) backhauling. MmWave channels are subject to Nakagami-m fading while the optical links experience the Double Generalized Gamma including atmospheric turbulence, path loss and the misalignment between the transmitter and the receiver aperture (also known as the pointing errors). The FSO model also takes into account the receiver detection technique which could be either heterodyne or intensity modulation and direct detection (IM/DD). Each user equipment (UE) has to be associated to one serving base station (BS) based on the received signal strength (RSS) or Channel State Information (CSI). We assume partial relay selection (PRS) with CSI based on mmWaves channels to select the BS associated with the highest received CSI. Each serving BS decodes the received signal for denoising, converts it into modulated FSO signal, and then forwards it to the data center. Thereby, each BS can be viewed as a decode-and-forward (DF) relay. In practice, the relay hardware suffers from nonlinear high power amplification (HPA) impairments which, substantially degrade the system performance. In this work, we will discuss the impacts of three common HPA impairments named respectively, soft envelope limiter (SEL), traveling wave tube amplifier (TWTA), and solid state power amplifier (SSPA). Novel closed-forms and tight upper bounds of the outage probability, the probability of error, and the achievable rate are derived. Capitalizing on these performance, we derive the high SNR asymptotes to get engineering insights into the system gain such as the diversity order.
A Minimalistic Approach to Segregation in Robot Swarms

We present a decentralized algorithm to achieve segregation into an arbitrary number of groups with swarms of autonomous robots. The distinguishing feature of our approach is in the minimalistic assumptions on which it is based. Specifically, we assume that (i) Each robot is equipped with a ternary sensor capable of detecting the presence of a single nearby robot, and, if that robot is present, whether or not it belongs to the same group as the sensing robot; (ii) The robots move according to a differential drive model; and (iii) The structure of the control system is purely reactive, and it maps directly the sensor readings to the wheel speeds with a simple ‘if’ statement. We present a thorough analysis of the parameter space that enables this behavior to emerge, along with an analysis that explains the emergent behavior. Finally, we study the effect on segregation performance of some nonideal aspects in the robot morphology.
For what it's worth: My advice to young investigators on preparation of an NSF-CAREER proposal

This paper is part of a special panel session on NSF—CAREER proposals and awards. The author is a CAREER recipient and reviewer. After a brief discussion of the author's own proposal, the paper offers some advice to young investigators on how to write a strong NSF-CAREER proposal.
Corrsin lecture on hairy hydrodynamics

Simple models for hairy surfaces interacting with flows are derived, and used to extract optimization and design principles in idealized contexts. Reconfiguration, in which flow couples to the hairs geometric configuration, is investigated along with drainage of thin films through beds of hairs.
Software availability and entry

Research Summary What happens to market structure as an industry's operations lean ever more on software? We find that software availability is associated with an increase in entry and an increase in exit by the oldest and most established firms. We suggest three potential mechanisms and, through post hoc analysis, determine which is most consistent with observed patterns. We find the effect of software availability on entry is stronger in settings with more available IT talent, more permissive labor policies, and greater demand uncertainty. Observed patterns are most consistent with software enhancing labor productivity and thus reducing exposure to uncertainty. Managerial Summary Managers and policymakers are concerned with whether technologies like software and robotics spreading to new industries can make powerful firms even harder to unseat. For software, we find evidence consistent with the opposite: as software becomes more prevalent, entry by new firms increases and the likelihood that older firms exit increases. The patterns of entry we observe are consistent with the following mechanism. Software allows the same number of employees to serve a wider range of production levels, meaning that potential startups are less likely to be deterred by uncertain demand.
HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline

Prior research in resource scheduling for machine learning training workloads has largely focused on minimizing job completion times. Commonly, these model training workloads collectively search over a large number of parameter values that control the learning process in a hyperparameter search. It is preferable to identify and maximally provision the best-performing hyperparameter configuration (trial) to achieve the highest accuracy result as soon as possible. To optimally trade-off evaluating multiple configurations and training the most promising ones by a fixed deadline, we design and build HyperSched---a dynamic application-level resource scheduler to track, identify, and preferentially allocate resources to the best performing trials to maximize accuracy by the deadline. HyperSched leverages three properties of a hyperparameter search workload overlooked in prior work -- trial disposability, progressively identifiable rankings among different configurations, and space-time constraints -- to outperform standard hyperparameter search algorithms across a variety of benchmarks.
The State-of-the-Art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.
Analytical solution for a hybrid Logistic-Monod cell growth model in batch and continuous stirred tank reactor culture.

Monod and Logistic growth models have been widely used as basic equations to describe cell growth in bioprocess engineering. In the case of the Monod equation, the specific growth rate is governed by a limiting nutrient, with the mathematical form similar to the Michaelis-Menten equation. In the case of the Logistic equation, the specific growth rate is determined by the carrying capacity of the system, which could be growth-inhibiting factors (i.e., toxic chemical accumulation) other than the nutrient level. Both equations have been found valuable to guide us build unstructured kinetic models to analyze the fermentation process and understand cell physiology. In this work, we present a hybrid Logistic-Monod growth model, which accounts for multiple growth-dependent factors including both the limiting nutrient and the carrying capacity of the system. Coupled with substrate consumption and yield coefficient, we present the analytical solutions for this hybrid Logistic-Monod model in both batch and continuous stirred tank reactor (CSTR) culture. Under high biomass yield (Yx/s ) conditions, the analytical solution for this hybrid model is approaching to the Logistic equation; under low biomass yield condition, the analytical solution for this hybrid model converges to the Monod equation. This hybrid Logistic-Monod equation represents the cell growth transition from substrate-limiting condition to growth-inhibiting condition, which could be adopted to accurately describe the multi-phases of cell growth and may facilitate kinetic model construction, bioprocess optimization, and scale-up in industrial biotechnology.
Flexible stability and nonsoficity

A sofic group $G$ is said to be flexibly stable if every sofic approximation to $G$ can converted to a sequence of disjoint unions of Schreier graphs by modifying an asymptotically vanishing proportion of edges. We establish that if $\mathrm{PSL}_d(\mathbb{Z})$ is flexibly stable for some $d \geq 5$ then there exists a group which is not sofic.
Growth Hormone Deficiency and Excessive Sleepiness: A Case Report and Review of the Literature

The somatotropic axis is intricately involved in normal sleep, as evidenced by the fact that hypothalamic growth hormone-releasing hormone (GHRH) has sleep promoting effects and pituitary growth hormone (GH) release is strongly associated with slow-wave sleep (SWS). Abnormalities in the somatotropic axis, such as GH deficiency of hypothalamic or pituitary origin, result in an alteration of normal sleep patterns which may explain the fatigue reported in these individuals. Sleep disorders such as narcolepsy, in which individuals abnormally enter rapid eye movement (REM) sleep at sleep onset are also associated with an altered GHRH circadian rhythm and abnormal GH secretion. While few studies are available, this review explores what is known about sleep abnormalities in GH deficiency, the effect of treatment on sleep in patients with GH deficiency, and GH secretion in narcolepsy. Emerging evidence suggests a hypothalamic link between narcolepsy and GH secretion. We also describe the unique constellation of isolated idiopathic GH deficiency and severe excessive sleepiness in adopted Nicaraguan siblings, one of which has narcolepsy and the other idiopathic hypersomnia.
Improving Privacy in Graphs Through Node Addition

The rapid growth of computer systems which generate graph data necessitates employing privacy-preserving mechanisms to protect users’ identity. Since structure-based de-anonymization attacks can reveal users’ identity’s even when the graph is simply anonymized by employing naive ID removal, recently, k- anonymity is proposed to secure users’ privacy against the structure-based attack. Most of the work ensured graph privacy using fake edges, however, in some applications, edge addition or deletion might cause a significant change to the key property of the graph. Motivated by this fact, in this paper, we introduce a novel method which ensures privacy by adding fake nodes to the graph. First, we present a novel model which provides k- anonymity against one of the strongest attacks: seed-based attack. In this attack, the adversary knows the partial mapping between the main graph and the graph which is generated using the privacy-preserving mechanisms. We show that even if the adversary knows the mapping of all of the nodes except one, the last node can still have k- anonymity privacy. Then, we turn our attention to the privacy of the graphs generated by inter-domain routing against degree attacks in which the degree sequence of the graph is known to the adversary. To ensure the privacy of networks against this attack, we propose a novel method which tries to add fake nodes in a way that the degree of all nodes have the same expected value.
Ready for prime time: role for geriatric assessment to improve quality of care in hematology practice

Older adults represent the growing majority of patients diagnosed with hematologic disorders, yet they remain underrepresented on clinical trials. Older patients of the same chronologic age differ from one another with varying comorbidity and functional reserve. The concepts of frailty and resilience are important to patient-centered care and are patient and setting specific. The use of geriatric assessment to inform tailored decision making and management can personalize care for older adults with hematologic malignancies. This article will highlight available evidence to support the role of geriatric assessment measures to enhance quality of care for older adults diagnosed with hematologic malignancies.
New North American Isolates of Venturia inaequalis Can Overcome Apple Scab Resistance of Malus floribunda 821.

Apple scab, caused by Venturia inaequalis, is a destructive fungal disease of major apple cultivars worldwide, most of which are moderately to highly susceptible. Thus, development of scab resistan...
Defined extracellular ionic solutions to study and manipulate the cellular resting membrane potential.

ABSTRACT All cells possess an electric potential across their plasma membranes and can generate and receive bioelectric signals. The cellular resting membrane potential (RMP) can regulate cell proliferation, differentiation and apoptosis. Current approaches to measure the RMP rely on patch clamping, which is technically challenging, low-throughput and not widely available. It is therefore critical to develop simple strategies to measure, manipulate and characterize the RMP. Here, we present a simple methodology to study the RMP of non-excitable cells and characterize the contribution of individual ions to the RMP using a voltage-sensitive dye. We define protocols using extracellular solutions in which permeable ions (Na+, Cl− and K+) are substituted with non-permeable ions [N-Methyl-D-glucamine (NMDG), gluconate, choline, SO42−]. The resulting RMP modifications were assessed with both patch clamp and a voltage sensitive dye. Using an epithelial and cancer cell line, we demonstrate that the proposed ionic solutions can selectively modify the RMP and help determine the relative contribution of ionic species in setting the RMP. The proposed method is simple and reproducible and will make the study of bioelectricity more readily available to the cell biology community. This article has an associated First Person interview with the first author of the paper.
Asymmetric Molecular Architecture of the Human γ-Tubulin Ring Complex

Summary The γ-tubulin ring complex (γ-TuRC) is an essential regulator of centrosomal and acentrosomal microtubule formation, yet its structure is not known. Here, we present a cryo-EM reconstruction of the native human γ-TuRC at ∼3.8 A resolution, revealing an asymmetric, cone-shaped structure. Pseudo-atomic models indicate that GCP4, GCP5, and GCP6 form distinct Y-shaped assemblies that structurally mimic GCP2/GCP3 subcomplexes distal to the γ-TuRC “seam.” We also identify an unanticipated structural bridge that includes an actin-like protein and spans the γ-TuRC lumen. Despite its asymmetric architecture, the γ-TuRC arranges γ-tubulins into a helical geometry poised to nucleate microtubules. Diversity in the γ-TuRC subunits introduces large (>100,000 A2) surfaces in the complex that allow for interactions with different regulatory factors. The observed compositional complexity of the γ-TuRC could self-regulate its assembly into a cone-shaped structure to control microtubule formation across diverse contexts, e.g., within biological condensates or alongside existing filaments.
Atomic resolution convergent beam electron diffraction analysis using convolutional neural networks.

Abstract Two types of convolutional neural network (CNN) models, a discrete classification network and a continuous regression network, were trained to determine local sample thickness from convergent beam diffraction (CBED) patterns of SrTiO3 collected in a scanning transmission electron microscope (STEM) at atomic column resolution. Acquisition of atomic resolution CBED patterns for this purpose requires careful balancing of CBED feature size in pixels, acquisition speed, and detector dynamic range. The training datasets were derived from multislice simulations, which must be convolved with incoherent source broadening. Sample thicknesses were also determined using quantitative high-angle annular dark-field (HAADF) STEM images acquired simultaneously. The regression CNN performed well on sample thinner than 35 nm, with 70% of the CNN results within 1 nm of HAADF thickness, and 1.0 nm overall root mean square error between the two measurements. The classification CNN was trained for a thicknesses up to 100 nm and yielded 66% of CNN results within one classification increment of 2 nm of HAADF thickness. Our approach depends on methods from computer vision including transfer learning and image augmentation.
Mask & Focus: Conversation Modelling by Learning Concepts

Sequence to sequence models attempt to capture the correlation between all the words in the input and output sequences. While this is quite useful for machine translation where the correlation among the words is indeed quite strong, it becomes problematic for conversation modelling where the correlation is often at a much abstract level. In contrast, humans tend to focus on the essential concepts discussed in the conversation context and generate responses accordingly. In this paper, we attempt to mimic this response generating mechanism by learning the essential concepts in the context and response in an unsupervised manner. The proposed model, referred to as Mask & Focus maps the input context to a sequence of concepts which are then used to generate the response concepts. Together, the context and the response concepts generate the final response. In order to learn context concepts from the training data automatically, we mask words in the input and observe the effect of masking on response generation. We train our model to learn those response concepts that have high mutual information with respect to the context concepts, thereby guiding the model to focus on the context concepts. Mask & Focus achieves significant improvement over the existing baselines in several established metrics for dialogues.
Spatial Analysis of Functional Enrichment (SAFE) in Large Biological Networks

Spatial analysis of functional enrichment (SAFE) is a systematic quantitative approach for annotating large biological networks. SAFE detects network regions that are statistically overrepresented for functional groups or quantitative phenotypes of interest, and provides an intuitive visual representation of their relative positioning within the network. In doing so, SAFE determines which functions cocluster in a network, which parts of the network they are associated with and how they are potentially related to one another.Here, I provide a detailed stepwise description of how to perform a SAFE analysis. As an example, I use SAFE to annotate the genome-scale genetic interaction similarity network from Saccharomyces cerevisiae with Gene Ontology (GO) biological process terms. In addition, I show how integrating GO with chemical genomic data in SAFE can recapitulate known modes of action of chemical compounds and potentially identify novel drug mechanisms.
Microparticle traction force microscopy reveals subcellular force exertion patterns in immune cell–target interactions

Force exertion is an integral part of cellular behavior. Traction force microscopy (TFM) has been instrumental for studying such forces, providing spatial force measurements at subcellular resolution. However, the applications of classical TFM are restricted by the typical planar geometry. Here, we develop a particle-based force sensing strategy for studying cellular interactions. We establish a straightforward batch approach for synthesizing uniform, deformable and tuneable hydrogel particles, which can also be easily derivatized. The 3D shape of such particles can be resolved with superresolution (<50 nm) accuracy using conventional confocal microscopy. We introduce a reference-free computational method allowing inference of traction forces with high sensitivity directly from the particle shape. We illustrate the potential of this approach by revealing subcellular force patterns throughout phagocytic engulfment and force dynamics in the cytotoxic T-cell immunological synapse. This strategy can readily be adapted for studying cellular forces in a wide range of applications. Traction force microscopy is an effective method for measuring cellular forces but it is limited by planar geometry. Here the authors develop a facile method to produce deformable hydrogel particles and a reference-free computational method to resolve surface traction forces from particle shape deformation.
Randomized Linear Algebra Approaches to Estimate the von Neumann Entropy of Density Matrices

The von Neumann entropy , named after John von Neumann, is an extension of the classical concept of entropy to the field of quantum mechanics. From a numerical perspective, von Neumann entropy can be computed simply by computing all eigenvalues of a density matrix, an operation that could be prohibitively expensive for large-scale density matrices. We present and analyze three randomized algorithms to approximate von Neumann entropy of real density matrices: our algorithms leverage recent developments in the Randomized Numerical Linear Algebra (RandNLA) literature, such as randomized trace estimators, provable bounds for the power method, and the use of random projections to approximate the eigenvalues of a matrix. All three algorithms come with provable accuracy guarantees and our experimental evaluations support our theoretical findings showing considerable speedup with small loss in accuracy.
Government as Network Catalyst: Accelerating Self-Organization in a Strategic Industry

Governments have long standing interests in preventing market failures and enhancing innovation in strategic industries. Public policy regarding domestic technology is critical to both national security and economic prosperity. Governments often seek to enhance their global competitiveness by promoting private sector cooperative activity at the inter-organizational level. Research on network governance has illuminated the structure of boundary-spanning collaboration mainly for programs with immediate public or non-profit objectives. Far less research has examined how governments might accelerate private sector cooperation to prevent market failures or to enhance innovation. The theoretical contribution of this research is to suggest that government programs might catalyze cooperative activity by accelerating the preferential attachment mechanism inherent in social networks. We analyze the long-term effects of a government program on the strategic alliance network of 451 organizations in the high-tech semiconductor industry between 1987 and 1999, using stochastic network analysis methods for longitudinal social networks.
Metrics for Graph Comparison: A Practitioner's Guide

Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees yield insight into the generative mechanisms and functional properties of the graph. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. Common choices include spectral distances and distances based on node affinities. However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies at different structural scales. In this work, we compare commonly used graph metrics and distance measures, and demonstrate their ability to discern between common topological features found in both random graph models and real world networks. We put forward a multi-scale picture of graph structure wherein we study the effect of global and local structures on changes in distance measures. We make recommendations on the applicability of different distance measures to the analysis of empirical graph data based on this multi-scale view. Finally, we introduce the Python library NetComp that implements the graph distances used in this work.
Adaptive Coding for Information Freshness in a Two-User Broadcast Erasure Channel

In this paper, we investigate the impact of coding on the Age of Information (AoI) in a two- user broadcast symbol erasure channel with feedback. We assume each update consists of K symbols and the source is able to broadcast one symbol in each time slot. Due to random channel noise, the intended symbol at each user will be erased according to an independent and identically distributed (i.i.d.) Bernoulli process. A user is able to successfully decode an update if it accumulates sufficient information and successfully decodes the K symbols of the update. Assuming perfect feedback information at the source right after the transmission of each symbol, our objective is to design an adaptive coding scheme to achieve small AoI at both users. We propose a novel coding scheme to judiciously combine symbols from different updates together, and analyze the AoI at both users. Compared with a baseline greedy scheme, the proposed adaptive coding scheme improves the AoI at the weak user by orders of magnitude without compromising the AoI at the strong user.
Piecewise Stationary Modeling of Random Processes Over Graphs With an Application to Traffic Prediction

Stationarity is a key assumption in many statistical models for random processes. With recent developments in the field of graph signal processing, the conventional notion of wide-sense stationarity has been extended to random processes defined on the vertices of graphs. It has been shown that well-known spectral graph kernel methods assume that the underlying random process over a graph is stationary. While many approaches have been proposed, both in machine learning and signal processing literature, to model stationary random processes over graphs, they are too restrictive to characterize real-world datasets as most of them are non-stationary processes. In this paper, to well-characterize a non-stationary process over graph, we propose a novel model and a computationally efficient algorithm that partitions a large graph into disjoint clusters such that the process is stationary on each of the clusters but independent across clusters. We evaluate our model for traffic prediction on a large-scale dataset of fine-grained highway travel times in the Dallas-Fort Worth area. The accuracy of our method is very close to the state-of-the-art graph based deep learning methods while the computational complexity of our model is substantially smaller.
Trion formation and unconventional superconductivity in a three-dimensional model with short-range attraction

A three-fermion problem in a three-dimensional lattice with anisotropic hopping is solved by discretizing the Schrodinger equation in momentum space. Interparticle interaction comprises on-site Hub...
Screening for Hepatitis C Virus Infection in Adolescents and Adults: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force.

Importance A 2013 review for the US Preventive Services Task Force (USPSTF) of hepatitis C virus (HCV) screening found interferon-based antiviral therapy associated with increased likelihood of sustained virologic response (SVR) and an association between achieving an SVR and improved clinical outcomes. New direct-acting antiviral (DAA) regimens are available. Objective To update the 2013 review on HCV screening to inform the USPSTF. Data Sources Ovid MEDLINE, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews through February 2019, with surveillance through September 2019. Study Selection Randomized clinical trials (RCTs) and nonrandomized treatment studies of HCV screening and DAA therapy; cohort studies on screening, antiviral therapy, and the association between an SVR after antiviral therapy and clinical outcomes. Data Extraction and Synthesis One investigator abstracted data; a second checked accuracy. Two investigators independently rated study quality. Main Outcomes and Measures Mortality, morbidity, quality of life, screening and treatment harms, and screening diagnostic yield. Results Eight RCTs of DAA therapy vs placebo or an outdated antiviral regimen, 48 other treatment studies, and 33 cohort studies, with a total of 179 230 participants, were included. No study evaluated effects of HCV screening vs no screening. One new study since the 2013 review (n = 5917) found similar diagnostic yield of risk-based screening (sensitivity, 82%; number needed to screen to identify 1 HCV case, 15) and birth cohort screening (sensitivity, 76%; number needed to screen, 29), assuming perfect implementation. Ten open-label studies (n = 3292) reported small improvements in some quality-of-life and functional outcomes (eg, less than 3 points on the 0 to 100 36-Item Short Form Health Survey physical and mental component summary scales) after DAA treatment compared with before treatment. Two cohort studies (n = 24 686) found inconsistent associations of antiviral therapy vs no therapy with risk of hepatocellular carcinoma. Forty-nine treatment studies (n = 10 181) found DAA regimens associated with pooled SVR rates greater than 95% across genotypes, and low short-term rates of serious adverse events (1.9%) and withdrawal due to adverse events (0.4%). An SVR after antiviral therapy was associated with decreased adjusted risk of all-cause mortality (13 studies, n = 36 986; pooled hazard ratio [HR], 0.40 [95% CI, 0.28-0.56) and hepatocellular carcinoma (20 studies, n = 84 491; pooled HR, 0.29 [95% CI, 0.23 to 0.38]) vs no SVR. Conclusions and Relevance Direct evidence on the effects of HCV screening on clinical outcomes remains unavailable, but DAA regimens were associated with SVR rates greater than 95% and few short-term harms relative to older antiviral therapies. An SVR after antiviral therapy was associated with improved clinical outcomes compared with no SVR.
The ACM Electronic Publishing Plan

For the past three years, the ACM Publications Board has been developing its vision for the future of publication in the electronic age and a program to achieve it. I am pleased to present that plan here. We envisage a diminishing role for print journals and exciting new programs around an ACM digital library. The plan raises important policy questions regarding the treatment of copyright. Our interim answers to these policy questions are discussed in two additional documents—the ACM Interim Copyright Policies and the ACM Author's Guide to the Interim Copyright Policies. Our overall intention is to move aggressively into electronic publishing and to preserve the traditional openness of ACM publications in the new media. Authors and readers should find these policies at least as hospitable as the traditional ones.
Generic Quantum Metric Rigidity

We introduce the coherent algebra of a compact metric measure space by analogy with the corresponding concept for a finite graph. As an application we show that upon topologizing the collection of isomorphism classes of compact metric measure spaces appropriately, the subset consisting of those with trivial compact quantum automorphism group is of second Baire category. The latter result can be paraphrased as saying that "most" compact metric measure spaces have no (quantum) symmetries; in particular, they also have trivial ordinary (i.e. classical) automorphism group.
Convex inner approximation of the feeder hosting capacity limits on dispatchable demand

This paper presents a method to obtain a convex inner approximation that aims to improve the feasibility of optimal power flow (OPF) models in distribution feeders. For a resistive distribution network, both real and reactive power effect the node voltages and this makes it necessary to consider both when formulating the OPF problem. Inaccuracy in linearized OPF models may lead to under and over voltages when dispatching flexible demand, at scale, in response to whole-sale market or grid conditions. In order to guarantee feasibility, this paper obtains an inner convex set in which the dispatchable resources can operate, based on their real and reactive power capabilities, that guarantees network voltages to be feasible. Test simulations are conducted on a standard IEEE distribution test network to validate the approach.
Fixation of Chondral-only Shear Fractures of the Knee in Pediatric and Adolescent Athletes*

Purpose: Attempted fixation of acute osteochondral shear injuries of the knee, though rare, is generally pursued in young, active patients, provided the adequately-sized fragment contains substantial bony tissue. Traditional biological principles suggest cartilage alone cannot be reaffixed to bone. However, due to several case reports of successful fixation of chondral-only fragments, this concept remains controversial and incompletely explored. The purpose of the current study was therefore to evaluate the presenting features, techniques, rates of healing, clinical, and radiological results in a cohort of youth athletes who underwent fixation of …
Underlying Neurobiological and Neurocognitive Mechanisms of Impulsivity in Risk-Taking Behaviors.

Impulsivity has been widely implicated in many maladaptive risk-taking and clinical disorders associated with such behaviors [...]
On a conjecture of Mahowald on the cohomology of finite sub-Hopf algebras of the Steenrod algebra

Mahowald's conjecture arose as part of a program attempting to view chromatic phenomena in stable homotopy theory through the lens of the classical Adams spectral sequence. The conjecture predicts the existence of nonzero classes in the cohomology of the finite sub-Hopf algebras $A(n)$ of the mod 2 Steenrod algebra that correspond to generators in the homotopy rings of certain periodic spectra. The purpose of this note is to present a proof of the conjecture.
TAPBPR promotes antigen loading on MHC-I molecules using a peptide trap

Chaperones Tapasin and TAP-binding protein related (TAPBPR) perform the important functions of stabilizing nascent MHC-I molecules (chaperoning) and selecting high-affinity peptides in the MHC-I groove (editing). While X-ray and cryo-EM snapshots of MHC-I in complex with TAPBPR and Tapasin, respectively, have provided important insights into the peptide-deficient MHC-I groove structure, the molecular mechanism through which these chaperones influence the selection of specific amino acid sequences remains incompletely characterized. Based on structural and functional data, a loop sequence of variable lengths has been proposed to stabilize empty MHC-I molecules through direct interactions with the floor of the groove. Using deep mutagenesis on two complementary expression systems, we find that important residues for the Tapasin/TAPBPR chaperoning activity are located on a large scaffolding surface, excluding the loop. Conversely, loop mutations influence TAPBPR interactions with properly conformed MHC-I molecules, relevant for peptide editing. Detailed biophysical characterization by solution NMR, ITC and FP-based assays shows that the loop hovers above the MHC-I groove to promote the capture of incoming peptides. Our results suggest that the longer loop of TAPBPR lowers the affinity requirements for peptide selection to facilitate peptide loading under conditions and subcellular compartments of reduced ligand concentration, and to prevent disassembly of high-affinity peptide-MHC-I complexes that are transiently interrogated by TAPBPR during editing. The molecular chaperones tapasin and TAPBPR play important roles in defining the repertoire of peptides displayed by MHC class I. Here, the authors combine NMR, ITC, fluorescence polarization measurements and deep mutational scanning analyses to reveal a peptide editing mechanism, where the G24-R36 loop in TAPBPR acts as a molecular trap to promote the selection of high-affinity peptide cargo.